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Financial and economic uncertainties and their effects on the economy

• Original Paper
• Open access
• Published: 20 March 2023
• Volume 50 , pages 481–521, ( 2023 )

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• Ines Fortin   ORCID: orcid.org/0000-0003-4517-455X 1 ,
• Jaroslava Hlouskova 1 , 2 &
• Leopold Sögner 1 , 3  

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We estimate new indices measuring financial and economic uncertainty in the euro area, Germany, France, the United Kingdom and Austria, following the approach of Jurado et al. (Am Econ Rev 105:1177–1216, 2015), which measures uncertainty by the degree of predictability. We perform an impulse response analysis in a vector error correction framework, where we focus on the impact of both local and global uncertainty shocks on industrial production, employment and the stock market. We find that global financial and economic uncertainties have significant negative effects on local industrial production, employment, and the stock market, while we find hardly any influence of local uncertainty on these variables. In addition we perform a forecasting analysis, where we assess the merits of uncertainty indicators for forecasting industrial production, employment and the stock market, using different performance measures. The results suggest that financial uncertainty significantly improves the forecasts of the stock market in terms of profit-based measures, while economic uncertainty gives, in general, more insight when forecasting macroeconomic variables.

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1 Introduction

In the aftermath of the 2008 financial crisis and the Great Recession, the interest of economists and policymakers has been markedly focused on the analysis of tools and techniques to assess the strengths and vulnerabilities of financial systems and, in particular, on measuring financial uncertainty and its effect on the economy. Also before the crisis, however, episodes of financial instability had highlighted the importance of continuous monitoring of financial systems in order to prevent crises. The International Monetary Fund, for example, had identified a broad set of prudential and macroeconomic variables that are relevant for assessing financial soundness (see International Monetary Fund 2002 ), which was later reduced to a subset including both aggregate bank balance sheet and income statement information and aggregate indicators of financial fragility of nonfinancial firms and nonbank financial markets. These indicators are referred to as financial soundness indicators and have, more recently, been examined with respect to their ability to predict financial sector distress (see Pietrzak 2021 ). The European Central Bank (ECB) has introduced a family of composite indicators of systemic stress (CISS) which are based on five categories—the financial intermediaries sector, money markets, equity markets, bond markets and foreign exchange markets—and which are supposed to measure a country’s financial stability. Footnote 1

Other indicators which are (closely) related to the indicators of financial stability are so-called uncertainty indices. Because uncertainty is unobserved, a number of proxies have been proposed in the literature. Traditional methods include, for example, the disagreement among professional forecasters, see Zarnowitz and Lambros ( 1987 ) and Bomberger ( 1996 ). Another measure of financial uncertainty, which has become very popular, is the realized and implied stock market volatility, see Bloom ( 2009 ). A big advantage of this measure is that realized volatility, based on observed stock market returns, is readily available for almost all countries.

More recently, alternative measures using a more formal econometric framework have been introduced. Jurado et al. ( 2015 ) suggest that uncertainty relates to whether the economy is more or less predictable, i.e., less or more uncertain. The authors propose to use as uncertainty measure the common variation in forecast errors for a broad range of macroeconomic and financial variables. Rossi and Sekhposyan ( 2015 ) agree with Jurado et al. ( 2015 ) that uncertainty relates to whether the economy is more or less forecastable. The uncertainty index they propose is the percentile in the historical distribution of forecast errors associated with the realized forecast error. They distinguish between upside and downside uncertainty, because these uncertainties may affect the economy in different ways. Carriero et al. ( 2018 ) deal with common variation in the residual volatilities in a large vector autoregression model and estimate measures of uncertainty jointly with assessing its impact on the macroeconomy. Chuliá et al. ( 2017 ) propose an index of time-varying stock market uncertainty. The index is constructed by first removing the common variations in the series, based on identifying expected variation (risk) and unexpected variation (uncertainty). Baker et al. ( 2016 ) develop an index of economic policy uncertainty based on the frequency of key uncertainty-related terms that occur in newspaper articles. Böck et al. ( 2021 ) examine the merits of sovereign CDS volatility as an indicator of economic policy uncertainty, which, however, is not available for all countries. Scotti ( 2016 ) uses “surprises” from Bloomberg forecasts to construct measures of economic uncertainty. In contrast to most measures of uncertainty, which deal with common shocks, Bijapur ( 2021 ) proposes an indicator of firm-level uncertainty, which is composed of idiosyncratic shocks. Bloom ( 2014 ) surveys related literature.

Interest in financial and economic uncertainty has been spurred by a growing body of evidence that uncertainty rises sharply in recessions. In most of the literature, measures of uncertainty are estimated in a first step and then used as if they were observable data series in the following econometric analysis of its impact on macroeconomic variables. Most of the above cited studies include at least a small analysis on the effects of uncertainty on the economy. The authors include their preferred uncertainty measure, together with a small set of macroeconomic variables like industrial production, inflation and employment, in a vector autoregression model and examine the responses of the macroeconomic variables to the uncertainty shock. Uncertainty usually rises in economic downturns; but is uncertainty a source of business cycles or is it rather an endogenous response to them, and does the type of uncertainty matter? Ludvigson et al. ( 2021 ) find that higher macroeconomic uncertainty in recessions is often an endogenous response to output shocks, while financial uncertainty is a likely source of output fluctuations.

We propose to use financial and economic uncertainty indicators in the spirit of Jurado et al. ( 2015 ) in order to measure financial and economic (in)stability in the euro area, Germany, France, the United Kingdom and Austria. We thus follow the approach to remove the forecastable component of the variation of the variables under consideration and focus on the conditional expectation of the squared forecast errors. The data we use to compute our financial uncertainty index cover the main financial market segments: money market, equity market, (sovereign) bond market, and foreign exchange market. These data are available at a daily frequency and we transform them to monthly data (using monthly averages), because we propose to estimate financial uncertainty at a monthly frequency. The data we use to estimate our economic uncertainty index include sentiment indicators, data on employment, retail sales, manufacturing, orders, price indices, and survey data related order books, production expectations, employment expectations, etc. We construct both financial and economic uncertainty indices for the same countries, and examine the resulting differences.

First we assess the impact of both local (country specific) and global (US) financial and economic uncertainty on the economy by estimating a vector error correction (VEC) model and analysing the responses of main macroeconomic variables (industrial production, employment) and the stock market to a shock in uncertainty. Furthermore we examine the role of both local and global financial and economic uncertainty indices in forecasting. We also consider the ECB’s new composite indicator of systemic stress (CISS) as an alternative measure of financial instability. We use different VEC models including or excluding uncertainty indices and assess the respective forecasts. In doing so we employ both traditional loss-based performance measures (root mean squared error and mean absolute error) and profit-based measures (directional accuracy/hit rate and directional value).

The remainder of this paper is organized as follows. Section  2 revises the methodology used to estimate uncertainty. Section  3 describes the data and presents the resulting indices of financial and economic uncertainty. Section  4 describes the impulse response analysis and the forecasting analysis, and presents the corresponding results. All analyses are performed for the euro area, Germany, France, the United Kingdom and Austria. Section  5 summarizes and concludes.

2 Methodology

Econometric studies on measuring uncertainty and its effects on the economy started with the seminal paper by Bloom ( 2009 ). Other relevant contributions include, among others, Bachmann et al. ( 2013 ), Baker et al. ( 2016 ), Basu and Bundick ( 2017 ), Berger et al. ( 2016 ), Caggiano et al. ( 2014 ), Chuliá et al. ( 2017 ), Carriero et al. ( 2018 ), Gilchrist et al. ( 2014 ), Jurado et al. ( 2015 ), and Scotti ( 2016 ),Bloom ( 2014 ) surveys related work.

In order to formally assess uncertainty we follow the approach focusing on unforecastable components of the variation of variables under consideration (see, e.g., Carriero et al. 2018 ; Chuliá et al. 2017 ; Jurado et al. 2015 , later referred to as JLN). Below we briefly sketch the approach used in JLN, where the notion of uncertainty is formalized as follows: Let \(y_{jt} \in Y_t \equiv \{ y_{1t}, \ldots , y_{Nt} \}\) be a variable and let \(Y_t\) be the set of variables describing a certain sector, e.g., the financial sector, where we intend to measure uncertainty. Its \(h-\) period ahead uncertainty, \({\mathcal {U}}_{jt}(h)\) , is the conditional volatility of the purely unforecastable component of the future value of a given variable. Namely,

where \(I_t\) is information available at t . Footnote 2 If the expectation at t of the squared error in forecasting \(y_{j,t+h}\) rises then uncertainty in the variable rises. Uncertainty in the whole sector, approximated by the elements of \(Y_t\) , is an aggregate of individual uncertainties

with the aggregation weights \(w_j\) and the implicit assumption that the law of large numbers holds. The econometric framework of JLN, which we adopt, is based on the following main steps:

The conditional expectation of the forecast error in ( 1 ), and thus \({{\mathbb {E}}}[y_{j,t+h} | I_t]\) , Footnote 3 is approximated by forecasts of diffusion indices (common factors). Common factors are estimated from a large set of predictors, \(x_{it}\) , \(i=1, \ldots , N^x\) . The information (in more technical terms the \(\sigma\) -field) generated by these predictors is assumed to approximate \(I_t\) as closely as possible. In addition we assume that the conditional expectation is linear in \(x_{it}\) , \(i=1, \ldots , N^x\) . The common factors will be treated as known later on. Forecasts of both real activity and financial returns can be substantially improved by augmenting best-fitting conventional forecasting equations with common factors estimated from large datasets (see Ludvigson and Ng 2007 , 2009 ; Stock and Watson 2006 , among others)

where \(\Phi _j^y(L)\) , \(\gamma _j^F(L)\) and \(\gamma _j^W(L)\) are finite-order polynomials in the lag operator L , Footnote 4 and \({\hat{\textbf{F}}}_t\) is the \(k_F-\) dimensional vector of estimates of latent common factors of the predictors \({\textbf{X}}_t=(x_{1t}, \ldots , x_{N^xt})'\) available for the analysis, which thus have the following factor structure

\({\textbf{F}}_t\) is the \(k_F\) -dimensional vector of latent common factors, \(\Lambda _i^F\) is the \(k_F\) -dimensional vector of factor loadings and \(e_{it}\) is the idiosyncratic error. The number of factors, \(k_F\) , is much smaller than the number of series \(N^x\) . Finally, the \(k_W\) -dimensional vector \({\textbf{W}}_t\) contains additional predictors such as squares of \({\hat{F}}_{1t}\) and factors in \(x_{it}^2\) to capture possible nonlinearities and potential effects that conditional volatilities might have on \(y_{jt}\) . Footnote 5 Time varying volatilities of \(y_{j,t+1}\) , the factors and additional predictors are allowed. The estimation of the factors uses the method of static principal components. Factors are selected on the basis of potential predictive power, see (Bai and Ng 2002 , 2006 , 2008 ).

The conditional expectation of the squared forecast errors in ( 1 ) is computed from a parametric stochastic volatility model for the one-step-ahead predictive errors for both \(y_{jt}\) and the factors. Footnote 6 The conditional volatility for \(h>1\) steps ahead is computed recursively, and through this procedure additional unforecastable variation is created via time varying volatility in the errors of the predictor variables (factors). In more detail, when allowing for the autoregressive dynamics in the factors and introducing notation \(Y_{jt} \equiv \left( y_{jt},y_{jt-1},\dots ,y_{jt-q+1} \right) ' \in {\mathbb {R}}^q\) , \({\textbf{Z}}_t \equiv \left( \hat{{\textbf{F}}}_t', {\textbf{W}}_t' \right) ' \in {\mathbb {R}}^{k}\) , where \(k= k_F+k_W\) , and \({\mathcal {Z}}_t \equiv \left( {\textbf{Z}}_t', \ldots , {\textbf{Z}}_{t-q+1}' \right) ' \in {\mathbb {R}}^{kq}\) , then we can obtain forecasts using the companion form

where \(\Lambda _j\) and \(\Phi _j^Y\) are functions of the coefficients in the lag polynomials in ( 3 ) and \(\Phi ^{{\mathcal {Z}}}\) records coefficients of the components in \({\mathcal {Z}}_t\) . In addition, stationarity of the corresponding time series is assumed. Footnote 7 Let \(\Omega _{jt}(h) \equiv {{\mathbb {E}}}_t \left( {\mathcal {Y}}_{j,t+h}-{{\mathbb {E}}}_t \left( {\mathcal {Y}}_{j,t+h} \right) \right) \left( {\mathcal {Y}}_{j,t+h}-{{\mathbb {E}}}_t \left( {\mathcal {Y}}_{j,t+h}\right) \right) '\) , be the forecast error variance of \({\mathcal {Y}}_{jt}\) modelled in ( 5 ) which evolves as

see Eq. (9) in JLN, where \({\mathcal {Y}}_{j,t}^{\nu }=\left( ({\nu }_t^{{\mathcal {Z}}})', ({\nu }_{jt}^{Y})' \right) '\) and

Thus, the expected forecast uncertainty of \(y_{j,t+h}\) is the square root of the corresponding scalar on the diagonal of \(\Omega _{jt}(h)\) , i.e.,

where \(e_j\) is the corresponding selection vector. In addition, stochastic volatility of \(y_{jt}\) and the factors is assumed, i.e., \(\nu _{j,t+1}=\sigma _{j,t+1} \varepsilon _{j,t+1}\) with \(\varepsilon _{j,t+1} \sim N(0,1)\) and

which affects the time variation in uncertainty ( 7 ) (see JLN, page 1187). Equation ( 10 ) can be estimated using Markov Chain Monte Carlo (MCMC) methods, following Kastner and Frühwirth-Schnatter ( 2014 ) and Kastner ( 2016 ).

The aggregate uncertainty, \({\mathcal {U}}_{t}^Y(h)\) , is estimated from individual uncertainty measures \({\mathcal {U}}_{jt}(h)\) . We consider two kinds of weights: equal weights and weights based on the common factors in the individual measures of uncertainty. As the implied uncertainty indices are very similar, we use the simpler version based on equal weights in this paper.

We use slightly modified versions of the codes provided by Jurado et al. ( 2015 ) to compute our financial and economic uncertainty indices.

3 Data and uncertainty indices

The following subsections describe the data used for estimating the uncertainty indices and present graphs of the estimated financial and economic uncertainty indices, for the euro area (EA), Germany (DE), France (FR), the United Kingdom (UK), and Austria (AT).

The financial data we use in order to estimate the financial uncertainty index include monthly observations on interest rates, yields on government bonds, yields on corporate bonds, interest rate swaps, overnight interest rates, spreads between different yields and/or rates, stock indices, bond indices, foreign exchange rates, dividend-price ratios, earnings-price ratios, and volatilities of stock/bond indices and foreign exchange returns. We consider different maturities for the rates/yields and use averages of the daily observations to compute monthly values. In total we have 74 financial variables for the euro area and Germany, 72 for France, 76 for the UK, and 77 for Austria, when we compute the financial uncertainty indices. The data set which is used to extract the factors used for forecasting the conditional volatilities for the financial variables, consists of both the financial variables just described and additional macroeconomic variables. The macroeconomic variables include sentiment indicators, data on employment, retail sales, manufacturing, orders, price indices, and survey data for twelve industries related to important economic questions concerning order books, production trend observed in recent months, production expectations, employment expectations, etc. Footnote 8 Note that the macroeconomic data are not real-time but ex-post (possibly revised) time series. Footnote 9 The macroeconomic data set includes 122 time series for the euro area and Austria, 120 for Germany, and 114 times series for France and the UK, respectively. Footnote 10 All data range from January 2000 until December 2020, i.e., we have 252 observations per variable. Details on the data used and a list of all variables considered for the euro area can be found in Appendix A . When we compute the macroeconomic uncertainty indicator we use again the macroeconomic and the financial data to extract the factors, but we forecast conditional volatilities for the macroeconomic variables (not for the financial variables). In doing so we follow Jurado et al. ( 2015 ) to group some variables which are originally included in the financial variables with the macroeconomic variables. In this case \(N=135\) for the euro area, \(N=134\) for Germany, \(N=128\) for France and the UK, respectively, and \(N=138\) for Austria.

In a prior version of this article we also use aggregate banking data to construct alternative versions of the financial uncertainty indices (for the euro area and Austria), in order to detect potential differences and, in particular, to analyze whether banking data improve the predictive properties of financial uncertainty. We find that, overall, banking data do not seem to improve the forecast performance. For more details, see Fortin et al. ( 2021 ).

3.2 Uncertainty indices

Figure  1 presents the financial and economic uncertainty indices for the euro area, showing three indices in each case, relating to forecast horizons of one, three and twelve months. While the level of uncertainty clearly increases with the forecast horizon (on average), the variability of uncertainty decreases, at least with the larger forecast horizon of twelve months. Footnote 11 This is also true for the country specific uncertainty indices, see Fig.  2 , which shows the country indices for financial and economic uncertainty, for forecast horizons of one and twelve months. Financial uncertainty indices in the euro area, Germany, France and Austria show very similar developments and reveal spikes around the bursting of the dot-com bubble 2000–2001, the global financial crisis 2007–2008, the European sovereign debt crisis 2010–2011, as well as around the outbreak of the Covid-19 crisis in early 2020. The UK is a bit different. The European sovereign debt crisis 2010–2011 is obviously not reflected in the UK financial uncertainty index, the most pronounced spike here corresponds to the global financial crisis (2007–2008), and the peak around the Covid-19 crisis is larger than in other countries. In all countries and the euro area economic uncertainty exhibits both a smaller level (on average) and a significantly smaller variability than financial uncertainty. Economic uncertainty in the euro area exhibits two peaks, one around the global financial crisis (great depression) and one around the outbreak of the Covid-19 crisis in 2020. Albeit rather similar, the development of economic uncertainty is more diverse among the countries than that of financial uncertainty. Footnote 12 In particular the spikes around the global financial crisis are not so clearly pronounced in all countries. Further, the Covid-19 crisis suggests an exceptionally large increase in economic uncertainty in the UK, like for financial uncertainty, for a forecast horizon of one month, and there is another peak in the UK after the Brexit referendum in June 2016.

The financial uncertainty indices for different forecast horizons, for the euro area and the countries, are highly correlated (above 0.96). This is true across different forecast horizons, and also across the regions, if the UK is not considered. Clearly, UK financial uncertainty is not so highly correlated with financial uncertainty in the other countries or the euro area (around 0.6). Also the economic uncertainty indices are positively correlated within the countries (above 0.65 and mostly larger), however, at a lower degree across the countries (0.2 \(-\) 0.7). With economic uncertainty the correlation across countries increases with the forecast horizon, in particular for euro area countries, see Fig.  2 . The descriptive statistics suggest that all uncertainty indices exhibit a (strongly) positive skewness. Footnote 13 This implies that the distribution is not symmetric and, in particular, that the right tail of the distribution is longer and the mass of the distribution is concentrated on the left. The kurtosis is mostly around three, which is the value for the Gaussian distribution, only for economic uncertainty in the euro area and the UK the numbers are around/above ten. This suggests that the underlying distribution produces more extreme realizations than the normal distribution. When looking at Figs.  1 and  2 we observe particularly sharp increases in economic uncertainty during the Covid-19 crisis for the euro area and the UK. This might be one of the drivers of excess kurtosis for economic uncertainty. Indeed, when estimating the kurtosis of economic uncertainty for the subsample excluding the Covid-19 crisis (May 2000 to December 2019), we obtain values which are much lower than for the total sample.

Financial and economic uncertainty indices for the euro area, for forecast horizons of one, three and twelve months

Financial and economic uncertainty indices for the euro area, Germany, France, the United Kingdom and Austria, for forecast horizons of one (top row) and twelve (bottow row) months

4 Empirical analysis

The data sample covers monthly observations for the period ranging from May 2000 through December 2020. We do not start earlier because our uncertainty indices can only be created from May 2000 onwards, due to data availability of the predictors and the autoregressive structure of ( 3 ), where the number of lags is four. For all countries under consideration we perform an impulse response analysis to quantify the dynamic responses of macroeconomic variables (industrial production, employment) and stock market indices to uncertainty shocks (of both financial and economic nature). We use the Cholesky decomposition to identify the structural shocks, in the vector error correction (VEC) model

where \(\left( {\textbf{y}}_{t} \right)\) is an \(n-\) dimensional stochastic process, t denotes the time dimension, and \({\textbf{c}}\) is an \(n-\) dimensional vector of intercept terms. The parameter matrix \({\varvec{\alpha }}\) is of dimension \(n \times r\) , while the matrix of cointegrating vectors \({\varvec{\beta }}\) is an \(n \times r\) matrix, where n is the number of variables and r is the number of cointegrating relationships. For matrix \({\varvec{\beta }}\) we apply the usual normalization such that \({\varvec{\beta }}_{1:r,1:r}\) is the r -dimensional identity matrix. The short-run dynamics are described by the \(n \times n\) matrices \({\varvec{\Gamma }}_{j}\) , \(j=1,\dots ,p\) . Finally, \({\textbf{u}}_{t}\) is a white noise process with mean zero and covariance matrix \(\varvec{\Sigma }\) . Footnote 14

Our VEC model contains the following (endogenous) variables: the global (US) financial or economic uncertainty index, \(x_t^{US}\) , the corresponding local (country specific) uncertainty index, \(x_t^{j}\) , and the country specific variables: industrial production, \(ip_t^{j}\) , employment, \(empl_t^{j}\) , the consumer price index, \(cpi_t^{j}\) , the short-term interest rate, \(ir_t\) , and the stock market index, \(stm_t^j\) , where \(j \in \left\{ \text {EA}, \text {DE}, \text {FR}, \text {UK}, \text {AT} \right\}\) , \(ir=\) 3 m-Euribor for euro area countries, \(ir=\) 3 m-Libor for the UK, and the stock market indices are the Euro Stoxx 50, the DAX 30, Footnote 15 the CAC 40, the FTSE 100 and the ATX. Hence, \(n=7\) and \({\textbf{y}}_t^j= \left( x_t^{US}, x_t^{j}, ip_t^{j}, empl_t^{j}, cpi_t^{j}, ir_t^j, stm_t^j \right) '\) . All variables except the uncertainty indices and the interest rate enter in log levels. To describe global (financial and economic) uncertainty we use the US financial and economic uncertainty indicators as calculated by Jurado et al. ( 2015 ). Footnote 16 For all financial and economic uncertainty indices we use the one-month ahead uncertainties.

The number of lags p is chosen based on the Schwarz information criterion. Footnote 17 The application of the error correction model ( 11 ) is supported as follows: For the time series considered, except for the uncertainty indices, the null hypothesis of a unit root cannot be rejected at the 5% significance level, using augmented Dickey-Fuller tests. To deal with stationary variables in the VEC model we follow Lütkepohl ( 2005 )[page 250] on the restrictions of cointegrating vectors in \({\varvec{\beta }}\) . That is, the first two coordinates of \({\textbf{y}}_t\) are global and local (stationary) uncertainty indices, \(x_t^{US}\) and \(x_t^j\) , and thus the first two cointegrating vectors are the corresponding canonical basis vectors. Footnote 18 We perform Johansen cointegration tests among all integrated endogenous variables and obtain evidence of one additional cointegrating vector, for each country j and the euro area. Thus, we have three cointegrating vectors, i.e., \({\hat{r}}=3\) .

In the current specification of the VEC model ( 11 ) all the variables considered are assumed to be endogenous. Assuming that the model is correctly specified, the parameters can be estimated consistently (see, e.g., Lütkepohl 2005 ). Note that the current specification allows to estimate the impact and the reverse impact of the global uncertainty index, approximated by \(x_t^{US}\) , and of the local uncertainty index, \(x_t^j\) . At least for larger countries and the euro area, effects in both directions cannot be ruled out a priori. Therefore, also the global uncertainty index is modelled endogenously within our VEC model. To investigate the stability of our modelling approach, as a robustness check, we include the S &P 500 index or US industrial production as exogenous variables. Footnote 19 When comparing the impulse response functions of ( 11 ) with those obtained when including US industrial production or the S&P 500 index, we see that the differences are neglectable, which supports to proceed with the VEC model as defined in ( 11 ). Footnote 20

4.1 Impulse response analysis

To identify the impact of an uncertainty shock on macroeconomic variables and the stock market we employ an impulse response analysis based on the Cholesky decomposition. We present results of estimated impulse responses of logged values of industrial production, employment and the stock market to one standard deviation increases (“shocks”) of either the financial or the economic uncertainty index, over the next 60 months, where we consider both global and local indices, respectively. Footnote 21 Fig.  3 shows the graphs for the euro area, Figs.  6 , 7 , 8 and  9 (in Appendix  B.1 ) present results for the other countries.

The first row of graphs in Fig.  3 shows the estimated impact of an increase in the euro area (local) financial uncertainty. Given the 95% confidence bounds shown by the shaded areas, we do not observe any significant impact of local financial uncertainty on industrial production and employment, nor on the Euro Stoxx 50. The second row shows the effects of an increase in global financial uncertainty. In this case, contrary to the situation before, we do see statistically significant decreases of industrial production, employment and the stock market. Global financial uncertainty thus seems to be a more influential factor for economic activity and the stock market than local financial uncertainty. The third row considers the impact of an increase in local economic uncertainty, and we observe a significant albeit short-run decrease for industrial production, while for employment we see a small and significant permanent decline; however, there is no significant impact of local economic uncertainty on the stock market. Finally, the fourth row considers the impact of an increase in global economic uncertainty, and here we see statistically significant negative effects upon all three variables considered.

To summarize, the impact of global uncertainty, both financial and economic, on euro area industrial production, employment and on the Euro Stoxx 50 is always significant, while this is never the case for local financial uncertainty. Note that the impact of global financial uncertainty exceeds the impact of global economic uncertainty. However, the impact of local economic uncertainty on the macroeconomic variables is also significant, albeit much smaller in size and persistence than that of global economic uncertainty. Somewhat surprisingly, although local uncertainty indices are constructed on the basis of local data, our impulse response analysis mainly identifies global uncertainty as a key driving factor of economic and financial activity.

As local and global uncertainty indicators enter the corresponding VEC models we can also investigate how local and global uncertainty indices influence each other. Interestingly we observe a strong significant impact of global on local economic uncertainty for approximately 2.5 years, while the impact of global on local financial uncertainty is barely significant, and only observable for about a year, see Fig.  4 . However, we hardly see any significant impact of euro area uncertainty upon global uncertainty, neither for financial nor for economic uncertainty, although this could possibly be the case for a large area like the euro area.

Looking at individual countries (see Figs.  6 , 7 , 8 and  9 in Appendix  B.1 ), the impulse response results are roughly similar as for the euro area. Local stock markets seem to be influenced mainly by global uncertainty. Both global financial and global economic uncertainty show significant effects on all local stock markets, where the impact of financial uncertainty is found to be stronger (in magnitude and/or persistence). Note that the long-term impact of global financial uncertainty upon local stock markets is largest for Austria and smallest for the UK. On the other hand, local uncertainty (neither financial nor economic) does not seem to be an important factor for stock markets. Among all countries considered (including the euro area), only in the UK the local stock market responds significantly, albeit only very shortly (four quarters), to a shock in local financial uncertainty.

Also country specific industrial production seems to be influenced more by global than by local uncertainty, where the magnitude of the effect is usually larger for global economic than global financial uncertainty (except for France and Austria). Local financial uncertainty does never significantly impact industrial production; local economic uncertainty, however, shows a significant but short-term effect for Germany (six quarters) and the UK (two quarters). The impulse response results for employment are very similar.

Impulse responses of industrial production, employment and the Euro Stoxx 50 to a one standard deviation shock of financial uncertainty (first block) and of economic uncertainty (second block) for the euro area and \(h=1\) , with 95% confidence intervals. In each block the first row shows the effect of euro area (financial/economic) uncertainty, the second row shows the effect of global (financial/economic) uncertainty

Effect of global uncertainty on euro area uncertainty and the other way round. The graphs show the effect of global uncertainty upon euro area uncertainty (left) and the effect of euro area uncertainty upon global uncertainty (right). The first row shows the case for financial uncertainties, the second row shows the case for economic uncertainties

4.2 Forecasting analysis

To analyze the value added of our uncertainty indices for forecasting industrial production, employment and the stock market, we compare the forecast performance of the VEC models forecasting these variables when the uncertainty indices are included and when they are omitted. In addition, we consider the forecast performance of a VEC model when the uncertainty index is replaced by the CISS, and we examine two benchmark models, the random walk (RW) and the univariate autoregressive model of order one, DAR(1). Footnote 22

We consider rolling-window estimation for our analysis, i.e., we keep the size of the estimation sample constant and equal to eighty months, and move forward the sample by one month, re-estimating the model parameters. The out-of-sample period, in which we evaluate the forecast performance, ranges from January 2007 to December 2020. In order to evaluate different forecasts we do not only employ traditional loss measures, like root mean squared error (RMSE) and mean absolute error (MAE), but also profit-based measures like directional accuracy (DA) and directional value (DV). The directional accuracy, or hit rate, is a binary variable measuring whether the direction of a variable change was correctly forecasted. The directional value additionally incorporates the economic value of directional forecasts by assigning to each correctly predicted change its magnitude. The loss-based and profit-based performance measures are formally defined as follows

where \(z_{t}\) is the variable we want to forecast, namely \(z_t \in \left\{ ip_t^j, empl_t^j, stm_t^j \right\}\) at time t , for country \(j \in \left\{ \text {EA}, \text {DE}, \text {FR}, \text {UK}, \text {AT} \right\}\) , and \({\hat{z}}_{t+h|t}\) is the forecast of the variable for time \(t+h\) conditional on the information available at time t , i.e., h is the forecast horizon, and \({\mathbb {I}} (\cdot )\) is the indicator function. The aggregate performance measures for each model are calculated over the out-of-sample period for a given forecast horizon as follows

where \(T_1=\) January 2007 and \(T_2=\) December 2020. We compare the forecast performance of the VEC models for the cases with: (i) both local (country driven) and global (US) financial uncertainty indices, (ii) both local and global economic uncertainty indices, (iii) the local financial uncertainty index, (iv) the local economic uncertainty index, (v) the country specific CISS, Footnote 23 (vi) no uncertainty and no CISS indices, and for two benchmark models, Footnote 24 (vii) autoregressive model of order one in differences, DAR(1), and (viii) random walk (RW). We consider forecast horizons of one and twelve months.

4.2.1 Euro area

Table  1 presents the forecast performance of the different models described above for the euro area. The first, second and third blocks present the forecast performance for industrial production, employment and the stock market, respectively. When forecasting industrial production, the best performance regarding loss measures (RMSE and MAE) is achieved by the random walk for both forecast horizons. With respect to profit-based measures and a forecast horizon of one month, the best hit rate is implied by the model including the CISS, while the best directional value (DV) is achieved by the model with both local and global economic uncertainty indices. For a forecast horizon of twelve months the best model with respect to the hit rate is the one with local economic uncertainty and with respect to the directional value it is the model with the CISS.

Regarding the forecast performance for employment we observe that uncertainty indices improve the forecast performance regarding loss measures. For a forecast horizon of one month, the model with CISS gives the smallest RMSE and the model with both global and local financial uncertainty indices implies the smallest MAE (which, however, is only marginally lower than for other models). For a forecast horizon of twelve months, the model with local economic uncertainty yields the smallest RMSE, while the model with local financial uncertainty yields the smallest MAE. Regarding profit-based measures, the model with the CISS always performs best.

Finally, we observe the following pattern in the forecast performance for the Euro Stoxx 50. While benchmark models provide the lowest RMSE and MAE, the model with both local and global financial uncertainty yields the largest hit rate and directional value, for forecast horizons of one and twelve months.

While Table  1 presents forecast performance criteria over the total out-of-sample period (January 2007–December 2020) it is also interesting to look at the forecast performance in sub-periods, to get an idea about which model performs best in which sub-period. Figure  5 shows the RMSE over rolling windows of six months for forecasting industrial production in the euro area, and the directional value over rolling windows of twelve months for forecasting the Euro Stoxx 50, for a forecast horizon of one month. For industrial production we show the time-changing RMSE implied by the models including both local and global economic uncertainties, including only local economic uncertainty and including no uncertainty. For the Euro Stoxx 50 we present the time-changing DV implied by the models including both local and global financial uncertainties, including only local financial uncertainty and including no uncertainty. Note that in the case of industrial production the model with both local and global economic uncertainties provides the lowest RMSE in the period of global financial crisis, Footnote 25 while the same model yields the worst performance in the period of the European sovereign debt crisis. Thus considering global economic uncertainty improves forecasts during the global financial crisis but does not seem to be helpful in the euro area crisis. However, the model including both local and global financial uncertainties yields the largest DV for the Euro Stoxx 50 most of the time, including the period of the global financial crisis.

Forecasting industrial production and the Euro Stoxx 50 in the euro area with both local and global (economic/financial) uncertainties, with only local (economic/financial) uncertainty and with no uncertainty, for \(h=1\) . The RMSE and the DV are shown for rolling windows over six and twelve months, respectively

4.2.2 Other countries

We present the results related to the forecast performance for Germany, France, the UK and Austria in Tables  5 , 6 , 7 and 8 in Appendix B.2 .

4.2.3 Industrial production

In all countries we observe that models including economic uncertainty provide the best forecast performance for industrial production in terms of the RMSE and DV, for a forecast horizon of one month. In addition, we observe that both local and global financial uncertainties are important in forecasting industrial production, especially in Germany and France. For Germany this model yields the largest hit rate for a forecast horizon of one month and the largest DV for a forecast horizon of twelve months, while for France this model implies the largest hit rate and DV for a forecast horizon of twelve months. To summarize, the models with both local and global economic or financial uncertainties yield the best profit-based performance when forecasting industrial production, over the short and the long forecast horizons for Germany and France, while for the UK and Austria models with only economic uncertainty improve the forecast performance for a forecast horizon of one month.

4.2.4 Employment

Unlike with industrial production, only models with economic (not financial) uncertainty provide the best forecast performance when forecasting employment. For Germany this is the case for the RMSE and for profit measures, for a forecast horizon of one month. For a forecast horizon of twelve months, however, the model with no uncertainty yields the best forecast performance with respect to all measures. For France the models with economic uncertainty yield the smallest RMSE and MAE over both horizons. For the UK models with economic uncertainty imply smallest loss measures only for the longer forecast horizon, but largest profit measures for both the short and long forecast horizons. Footnote 26 For Austria the model with only local economic uncertainty implies the largest DV for a forecast horizon of one month. In all other cases the benchmark models yield the best performance. All in all, the models with both local and global economic uncertainty dominate the best models when forecasting employment. This holds for all countries but Austria, where the model with only local economic uncertainty seems to perform better.

4.2.5 Stock market

We observe a similar pattern in the forecast performance of stock market indices for Germany, France, the UK and Austria as in the euro area. Models with both local and global financial uncertainty yield the largest hit rates and directional values, while benchmark models provide the lowest RMSE and MAE, over both forecast horizons. For Austria, the model with both local and global financial uncertainty also yields the lowest RMSE, for a forecast horizon of one month.

In order to find out whether certain models forecast significantly better than others (with respect to a given performance measure), we perform the Diebold-Mariano test of equal forecast accuracy (see Diebold and Mariano 1995 ). We are particularly interested in whether models including uncertainty indices achieve significantly better forecasts than models without uncertainty indices. More precisely, we test whether the model including both local and global (financial or economic) uncertainties provides a significantly better forecast performance (at the 10% level) than the model with only local uncertainty Footnote 27 or the model with no uncertainty. In addition we test whether the model including (only) local uncertainty provides better forecasts than the model with no uncertainty. Our main results are as follows. We find significant differences between models only when forecasting stock market indices, considering profit-based measures. First, the models including both local and global financial uncertainties significantly outperform, except for Germany, the models including no uncertainty for forecast horizons of one and twelve months. Footnote 28 Second, models with both local and global financial uncertainties significantly outperform models with only local financial uncertainty, for a forecast horizon of one month. Footnote 29 This implies that for short-term forecasting global uncertainty seems to be more important than local uncertainty. Third, for all countries but Germany, models with only local financial uncertainty significantly outperform models with no uncertainty, for a forecast horizon of twelve months. Local financial uncertainty thus seems to be more important when forecasting over longer than over short horizons.

5 Conclusions

In this paper we obtain new indices measuring financial and economic uncertainty in the euro area, Germany, France, the UK and Austria, following the approach of Jurado et al. ( 2015 ), which measures uncertainty by the degree of predictability. We use monthly data comprising roughly 200 time series for the euro area and each country to construct our financial and economic uncertainty indices. The data cover the time span from January 2000 to December 2020.

After estimating the financial and economic uncertainty indices, we perform impulse response analyses in a vector error correction framework, where we focus on the impact of both local (country specific) and global (US) uncertainty shocks on industrial production, employment and the stock market, for the euro area, Germany, France, the UK and Austria. First, we observe significant negative effects of global financial uncertainty on industrial production, employment and the stock market, for the euro area and all countries considered. Second, for global economic uncertainty, we mostly observe a negative and statistically significant impact on the economic variables considered. Third, local financial uncertainty hardly shows statically significant effects on local industrial production, employment and the stock market. Forth, also for local economic uncertainty the effects are hardly significant. Only for the euro area, Germany and the UK local economic uncertainty shows a significant negative impact on the macroeconomic variables, however, only in the short run (Germany, UK).

In addition, we perform a forecasting analysis with respect to both loss-based and profit-based performance measures, where we assess the value added of our uncertainty indices in forecasting industrial production, employment and the stock market, for forecast horizons of one and twelve months. I.e., we compare the forecast performance of models including both local and global uncertainties, models including only local uncertainty and models including no uncertainty. We find that financial and/or economic uncertainty can improve the forecasting performance. Models including economic uncertainty improve the forecast performance for industrial production in the short run, while for the euro area, Germany and France models with financial uncertainty provide a value added for longer forecast horizons (twelve months). Regarding the forecasting of employment, models with economic uncertainty are among the best ones. Finally, a clear pattern can be observed when forecasting stock markets, considering profit-based performance measures. Models including both local and global financial uncertainties significantly outperform models including no uncertainty. In addition, models with both local and global financial uncertainties significantly outperform models with only local financial uncertainty, for a forecast horizon of one month, i.e., in the short-run global financial uncertainty seems to be more important than local uncertainty, when forecasting the stock market. Finally, for all countries but Germany, models with only local financial uncertainty significantly outperform models with no uncertainty, for a forecast horizon of twelve months.

The ECB’s indicators use different weighting schemes to aggregate individual variables or subindices into one index: weights reflecting the time-varying cross-correlation structure (CISS) or equal weights (new CISS), see Holló et al. ( 2012 ). The CISS is computed for the euro area as a whole on a weekly basis, the new CISS is computed for the euro area as a whole and for all euro area countries on a daily basis.

The proper measurement of uncertainty requires removing the forecastable component \({{\mathbb {E}}}\left( y_{j,t+h} | I_t \right)\) before computing conditional volatility. Otherwise the forecastable variation would be (falsely) classified as uncertain.

To simplify notation we use \({{\mathbb {E}}}_t (\cdot)\) to denote \({{\mathbb {E}}}[\cdot | I_t]\) .

Following JLN, we choose polynomials of order four for \(\Phi _j^y(L)\) and polynomials of order two for \(\gamma _j^F(L)\) and \(\gamma _j^W(L)\) .

We choose the factor in \(x_{it}^2\) corresponding to the largest eigenvalue.

To estimate stochastic volatility in the forecast errors we use the ‘stochvol’ R package (see Kastner 2016 ).

Following JLN, we choose an order of four in the autoregressive dynamics of the factors to coincide with the order of polynomial \(\Phi _j^y(L)\) , i.e., \(q=4\) .

In total, the survey data cover seven questions relating to (i) production trend observed in recent months, (ii) order books, (iii) export order books, (iv) stocks of finished products, (v) production expectations, (vi) selling price expectations, (vii) employment expectations, and one overall variable, the industrial confidence indicator.

For more details on real-time macroeconomic data see, for example, Croushore and Stark ( 2000 ).

Thus, \(N^x=196\) for the euro area, \(N^x=194\) for Germany, \(N^x=186\) for France, \(N^x=190\) for the UK, and \(N^x=199\) for Austria.

Note that the forecast tends to the unconditional mean as the forecast horizon tends to infinity.

Less diversity among the financial uncertainty indices is mainly due to the fact that a lot of financial variables are identical over all countries except the UK, as they are related to the euro area, e.g., money market interest rates and exchange rates.

The descriptive statistics for the euro area and all countries under consideration can be obtained upon request.

To simplify notation we drop the country index from Eq. ( 11 ).

In 2021 the DAX 30 was redesigned to include 40 stocks and is now called DAX 40.

We use total financial uncertainty and total macro uncertainty, see https://www.sydneyludvigson.com/macro-and-financial-uncertainty-indexes .

In most cases the lag length was one, i.e., \({\hat{p}}=1\) . Hence we proceeded with a lag of one for all models, also due to the curse of dimensionality.

The first vector has one as the first component and zeros elsewhere and the second vector has one as the second component and zeros elsewhere.

However, with larger models some of the results seem to become unstable. This would also be the case if we included all financial and economic (global and local) uncertainty indices in one large model.

For the impulse response analysis and the forecasting analysis we use EViews 13.

We thank an anonymous referee for the idea to include a global uncertainty indicator to get a better picture of the influence of global and/or local uncertainty.

As all forecasted variables are integrated of order one, we apply the AR(1) model on log differences of the underlying variable.

Note that in case when only one uncertainty index is included, or the CISS (see cases (iii)–(v)) the number of stationary variables in ( 11 ) reduces to one and number of cointegrating vectors reduces to two, i.e., \({\hat{r}}=2\) , where the first cointegrating vector is the first canonical basis vector.

In this case all variables in ( 11 ) are integrated of order one and \({\hat{r}}=1\) .

The model with the lowest RMSE over the total period, the random walk, performs much worse than any of the three models during the global financial crisis.

The only exception for the UK is the largest DV for a forecast horizon of twelve months, which is achieved by the random walk model.

Thus, whether including global uncertainty improves the forecast performance.

For Germany this applies only for a forecast horizon of one month.

For Austria this is also true when considering the RMSE, for a one-month forecast horizon. This is the only case when we find a significant result for a loss measure.

Two variables of the survey data, employment expectations, are not available for the euro area because the data only start later than January 2000. These are the series related to the industries beverages and wood.

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Open access funding provided by Institute for Advanced Studies Vienna. This study was funded by Oesterreichische Nationalbank (OeNB) under Anniversary Grant No. 18115.

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Ines Fortin, Jaroslava Hlouskova & Leopold Sögner

Department of Economics, Faculty of National Economy, University of Economics, Bratislava, Slovakia

Jaroslava Hlouskova

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Appendix A: Financial and macroeconomic data

In the following, we provide details on the financial and macroeonomic data for the euro area and the corresponding transformations, which we use for computing the financial and economic uncertainty indices. Similar data and transformations are used for Germany, France, the United Kingdom and Austria. The data are available either at monthly frequencies or at daily frequencies, where daily are transformed to monthly frequencies by taking monthly averages. The employment data for the euro area and France are only available at a quarterly frequency. We construct monthly data from these quarterly series by estimating the missing values, following Shumway and Stoffer ( 1982 ) and Seong et al. ( 2013 ). A formal description is provided in the appendix of a prior version of this article, see Fortin et al. ( 2021 ). We consider 74 financial variables and 122 macroeconomic variables for the euro area. For Germany we use 74/120 financial/macroeconomic variables, for France 72/114, for the United Kingdom 76/114, and for Austria 77/122.

In order to ensure stationarity we perform various transformations. With respect to the financial data, we compute first differences (first diff) for interest rates, and spreads, i.e., differences (diff), for rates/yields. We calculate returns for stock/bond indices and foreign exchange rates in two ways: first we calculate returns of a month with respect to the previous month and annualize the results (monthly returns, m/m-1 (a)), second we calculate returns of a month with respect to the previous year (yearly returns, m/m-12). Finally we compute volatilities, namely stochastic volatilities (stoch vola), for the monthly returns of stock/bond indices and foreign exchange rates. We transform the macroeconomic data by taking yearly growth rates (m/m-12), the survey data are given in balances (difference between positive and negative answering options, measured as percentage points of total answers) and are not transformed.

The macroeconomic data include eight questions from the industry survey data collected by the DG ECFIN, for twelve different industries; hence, in total, 96 variables. Footnote 30 The industries are beverages, wood (wood and wood and cork products except furniture, straw and plaiting materials), paper (paper and paper products), printing (printing and reproduction of recorded media), chemicals (chemicals and chemical products), rubber (rubber and plastics products), other minerals (other non-metallic mineral products), basic materials, fabricated metals (fabricated metal products except machinery and equipment), machinery (machinery and equipment N.E.C.), motor vehicles (motor vehicles, trailers and semi-trailers), and other manufacturing. The questions relate to the industrial confidence indicator, the production trend observed in recent months, order books, export order books, stocks of finished products, production expectations, selling price expectations, and employment expectations. The data used for calculating financial uncertainty indices are monthly and range from January 2000 to December 2020, i.e., 252 observations per variable.

Tables  3 and  4 list the financial and macroeconomic variables used for constructing the financial and economic uncertainty indices for the euro area. Similar data are used for Germany, France, the United Kingdom and Austria. Table 2 lists the abbreviations used in Tables  3 and  4 .

When we compute the macroeconomic uncertainty indicator for the euro area the following financial variables are grouped with the macroeconomic variables, not with the financial variables: Euribor, 3 m; Euribor, 6 m; Euribor, 12 m; Government bond yield, EA, 5–7y; Government bond yield, EA, 7–10y; Government bond yield, EA, > 10y; Euro Stoxx index, m/m-1 (a); Euro Stoxx dividend yield; Euro Stoxx price earnings ratio; growth rates, m/m-1 (a), of USD/EUR, JPY/EUR, CHF/EUR, and GBP/EUR.

The variable bank loans to non-financial corporations is created by summing the three variables bank loans to non-financial corporations < 1 year (EMEBMC0.A), bank loans to non-financial corporations 1–4 years (EMEBMC1.A), and bank loans to non-financial corporations > 4 years (EMEBMC5.A), and then computing growth rates. Two variables of the survey data relating to employment expectations are not available for the euro area because the data only start later than January 2000. These are the series related to beverages (EK11.7.BQ) and wood (EK16.7.BQ).

Appendix B: Empirical results

1.1 impulse response analysis.

In the following, we present graphs showing the impulse response functions for Germany, France, the United Kingdom and Austria. We present the responses of local industrial production, employment and the stock market to one standard deviation shocks in (i) the local and global financial uncertainty indices, and (ii) the local and global economic uncertainty indices (Figs. 6 , 7 , 8 , 9 ).

Impulse responses of industrial production, employment and the DAX 30 to a one standard deviation shock of financial uncertainty (first block) and of economic uncertainty (second block) for Germany and \(h=1\) , with 95% confidence intervals. In each block the first row shows the effect of German (financial/economic) uncertainty, the second row shows the effect of global (financial/economic) uncertainty

Impulse responses of industrial production, employment and the CAC 40 to a one standard deviation shock of financial uncertainty (first block) and of economic uncertainty (second block) for France and \(h=1\) , with 95% confidence intervals. In each block the first row shows the effect of French (financial/economic) uncertainty, the second row shows the effect of global (financial/economic) uncertainty

Impulse responses of industrial production, employment and the FTSE 100 to a one standard deviation shock of financial uncertainty (first block) and of economic uncertainty (second block) for the United Kingdom and \(h=1\) , with 95% confidence intervals. In each block the first row shows the effect of UK (financial/economic) uncertainty, the second row shows the effect of global (financial/economic) uncertainty

Impulse responses of industrial production, employment and the ATX to a one standard deviation shock of financial uncertainty (first block) and of economic uncertainty (second block) for Austria and \(h=1\) , with 95% confidence intervals. In each block the first row shows the effect of Austrian (financial/economic) uncertainty, the second row shows the effect of global (financial/economic) uncertainty

1.2 Forecasting analysis

In the following, we present forecast performance measures for Germany, France, the UK and Austria, when forecasting industrial production, employment and the stock market (Tables 5 , 6 , 7 and 8 ).

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Fortin, I., Hlouskova, J. & Sögner, L. Financial and economic uncertainties and their effects on the economy. Empirica 50 , 481–521 (2023). https://doi.org/10.1007/s10663-023-09570-3

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The impact of economic uncertainty on the financial markets: evidence from interest rates, exchange rates and cryptocurrency

Nguyen, Hai Nam (2023) The impact of economic uncertainty on the financial markets: evidence from interest rates, exchange rates and cryptocurrency. University of Southampton , Doctoral Thesis , 240 pp.

There has been a growing interest in studying economic uncertainty and its propagation on the economy and financial markets since the last global financial crisis. Literature provides ample evidence of the interconnectedness between major economic, financial, political shocks, economic uncertainty, and economic stagnation. This thesis consists of three essays that extend the literature with a focus on economic uncertainty from various sources and its impact on the real economy alongside with the financial markets. In chapter 2, we theoretically investigate different measurement methods of constructing economic uncertainty and three major transmission channels of uncertainty shocks to the economy. We perform a structured examination of three major proxies for uncertainty in the literature, including the financial uncertainty, the survey-based uncertainty, and the newspaper-based uncertainty. Considering the pros and cons of each uncertainty measurement's approach, we use the newspaper-based uncertainty as our uncertainty estimator to implement empirical analysis of its impact on economic activities and financial markets. Also in this chapter, we also document three major transmission channels of uncertainty shocks to the economy, including real option, risk aversion, and growth options effects. Uncertainty, under the real option and risk aversion channels exerts a negative influence on the economic activities by diminishing financial wealth, curbing investment and consumption, and increasing perceived risks of market participants. While uncertainty under the growth options channel, on the contrary, promotes riskier investments and economic growth's outlook. In chapter 3, we empirically study the impact of economic uncertainty shocks in the bond markets on the dynamics of the entire term structure of interest rate. Conducting on the bond yields, volatility and holding excess returns for the US, UK and Japan, we find that the responses of the yield and volatility factors to uncertainty shocks are more pronounced for US and UK markets. Besides, the impact of uncertainty on bonds' yields is shown to be larger for shorter-term bonds in shorter investment horizons, while the impact of uncertainty on bond's volatility exhibits a hump-shape pattern. Moreover, the inclusion of uncertainty factor in the term structure model helps explain the term premia and improve the prediction power of the model without being spanned by the three main components of the yield curve (level, slope, and curvature). In chapter 4, we investigate the propagation of monetary policy uncertainty to the determinations of exchange rate's behaviors and the role of uncertainty in explaining the forward premia puzzle. Our empirical results using quantile threshold regression method show that the impact of monetary policy uncertainty on forward exchange rate premia are significantly different in the two uncertainty regimes and heterogeneous across exchange rate quantiles. We then employ a quantile-based approach to obtain the time-varying conditional distribution as well as the risk measures of future evolution of the exchange rate. Our findings indicate tight connectedness between risk measurements (represented by appreciation and depreciation risks) and important economic events associated with high monetary policy uncertainty. Moreover, the risk measure diagrams for different pairs of currencies point out that the US dollar, Japanese yen and Canadian dollar are qualified as safe-haven currencies due to their low volatility in depreciation risks and abnormal large upside movement during high uncertainty periods. Finally, in the last chapter of this thesis, we explore the dynamic of economic policy uncertainty on Bitcoin returns and volatility. Using the Quantile-on-Quantile regression model and the Quantile-Granger causality approach, we detect the heterogeneous impacts of economic uncertainty on Bitcoin returns and volatilities across distributions of all considered variables for all markets. The relations between Bitcoin returns and uncertainty are shown to be notably strong during high uncertainty periods, implying the hedging ability of Bitcoin against uncertainty in some markets. The effects of uncertainty on Bitcoin volatility are found significant at extreme quantiles of both variables, implying the speculative characteristics of Bitcoin reflected by the high volatility and sensitivity of Bitcoin’s price fluctuations to investor sentiment

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Economic policy uncertainty and macroeconomic activity: an asymmetric approach.

Majid Makinayeri , University of Wisconsin-Milwaukee

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Mohsen Bahmani-Oskooee

Committee Members

N. Kundan Kishor, Rebecca Neumann, Vivian Lei

In the new global economy, uncertainty has become a critical determinant of financial and economic stability. This thesis aims to study the impact of uncertainty on a set of macroeconomic variables such as demand for money, investment, and consumption. Different measures of uncertainty are used by scholars in the investigation of money demand, investment, and consumption like monetary and output uncertainty. This study employs a more general and inclusive measure of uncertainty, policy uncertainty, which measures uncertainty in fiscal, regulatory and monetary policies. By implementing a Nonlinear Autoregressive Distributed Lag (ARDL) model, I aim to identify possible non-linear effects of uncertainty on economic variables, which help us to have a better understanding of its role in each of the G7 economies. The advantage of choosing this methodology is that it allows researchers to explain both long-run relationships and short-run dynamics of money demand, investment, and consumption. The empirical results exhibit that policy uncertainty has asymmetric effects on the macroeconomic variables in all G7 economies. These asymmetric reactions of the macroeconomic variables to fluctuations in policy uncertainty imply positive and negative shocks in economic policy uncertainty could not offset the effects of each other, and they have persistent impacts on demand for money, investment and consumption in the long-run.

Recommended Citation

Makinayeri, Majid, "Economic Policy Uncertainty and Macroeconomic Activity: An Asymmetric Approach" (2019). Theses and Dissertations . 2320. https://dc.uwm.edu/etd/2320

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The impact of economic uncertainty and financial stress on consumer confidence: the case of Japan

Journal of Asian Business and Economic Studies

ISSN : 2515-964X

Article publication date: 7 October 2021

Issue publication date: 15 February 2022

This study explores the response of consumer confidence in policy uncertainty in the Japanese context. The study also considers the dynamism of stock market behavior and financial stress and its impact on consumer confidence, which has remained unaddressed in the literature. The role of these control variables has important implications for policy discussions, particularly when other countries can learn from Japanese experiences.

Design/methodology/approach

The nonlinear autoregressive distributed lag model postulated by Shin et al . (2014) was used for studying the asymmetric response of consumer confidence to policy uncertainty. This method has improved estimates compared to traditional linear cointegration methods.

The findings confirm the asymmetric impact of policy uncertainty on the consumer confidence index in Japan. The impact of the rise in policy uncertainty is greater than that of a fall in asymmetry on consumer confidence in Japan. Furthermore, the Wald test confirmed asymmetric behavior.

Originality/value

The contribution of this study is threefold. First, this study contributes to the extant literature by analyzing the asymmetric response of consumer confidence to policy uncertainty, controlling for both the financial stress and stock price indices. Second, to test the robustness of the exercise, the study utilized different frequencies of observations. Third, this study is the first to utilize the concept of Arbatli et al. (2017) to formulate a combined index of uncertainty based on economic policy uncertainty index, along with uncertainty indices such as fiscal, monetary, trade and exchange rate policies to study the overall impact of policy uncertainty.

• Consumer confidence index
• Uncertainty index
• Financial stress index

Ghosh, S. (2022), "The impact of economic uncertainty and financial stress on consumer confidence: the case of Japan", Journal of Asian Business and Economic Studies , Vol. 29 No. 1, pp. 50-65. https://doi.org/10.1108/JABES-04-2021-0044

Emerald Publishing Limited

Copyright © 2021, Sudeshna Ghosh

Published in Journal of Asian Business and Economic Studies . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

In Japan, an untenable fiscal path accompanied by monetary policy restraints magnified uncertainty in the country. The reform inventiveness of the Prime Minister of Japan, popularly known as “Abenomics” since December 2012, was a directional change towards sustained accelerated growth. However, the behavior of the economy proved that continuing with “Abenomics” was indeed challenging. Against this background, we attempt to explore the impact of policy uncertainty changes on consumer confidence. To what extent does financial stress affect consumer confidence? Considering the pioneering work of Katona (1975) , a long series of studies have explored the significance of consumer confidence and its implications on economic performance.

The international economy is increasingly considered to be influenced by policy uncertainty at the global level owing to the growing underlying associations across countries. Abrupt variations in policies at both local and global levels often create disturbances in consumer confidence levels. According to Gholipour et al. (2021) , deliberations in the literature on the nexus between policy uncertainty and consumer confidence increased after the global financial crisis and, recently, after the pandemic.

We contribute to extant literature by exploring, in an asymmetric empirical framework, the impact of policy uncertainty on consumer confidence along with share price behavior and financial stress for Japan. This study is expected to add novelty in the literature as (1) it establishes the interlinkages between policy uncertainty and consumer confidence in an integrated framework, (2) utilizes the novel methodology of Shin et al. (2014) to explore the asymmetry in the relationship to investigate the hypothesis of the study, and (3) discusses the specific case of Japan, unlike earlier studies wherein the country of focus is the US.

The remainder of this study is organized as follows. Section 2 reviews existing literature. Section 3 provides an overview of the dataset and the methodology used thereof. Section 4 presents our empirical results. Section 5 discusses the major findings of this study. Finally, Section 6 concludes the paper.

2. Review of literature

Discussion of the findings on policy uncertainty and nexus with consumer confidence and business confidence.

Discussion on the findings on asymmetric impact of policy uncertainty.

2.1 Policy uncertainty impact on consumer confidence and business confidence

Various studies discuss how policy uncertainty at both local and global levels impacts the consumer and investor confidence. Notable studies for illustration ( De Mendonça and Almeida, 2019 ) investigated how macroeconomic variables and uncertainty at policy levels reduce business confidence levels, which have adverse implications on consumer confidence levels. These studies show that high policy uncertainty and low levels of credibility reduce consumer confidence levels. Benhabib et al. (2015) found that consumers' confidence affects aggregate demand, real wages and productivity decisions of an economy. The study concluded that confidence shocks affect output and employment, even if expectations are fully rational and no externalities are found. Akerlof and Shiller (2010) and, further, Mumtaz and Surico (2018) and Lee et al. (2019) discuss that economic policy uncertainty impacts consumers' risk perception, which has adversarial consequences on expenditure decisions. Moreover, for the United States economy, Mumtaz and Surico (2018) found that those shocks originating from economic policy uncertainty adversely impact the business and consumer confidence levels. The study further highlights those uncertainties on the position of public debt have persistent negative effects on consumer confidence levels, which unfavorably impact overall output levels. According to Bloom et al. (2018) , the incidence of uncertainty shocks affects business cycles. Based on micro-data, this study demonstrated that uncertainty has significant impact on business downswings. The study by De Mendonça and Almeida (2019) confirms that by Mumtaz and Surico (2018) in the Brazilian context. According to Al-Thaqeb and Algharabali (2019) , “Policy uncertainty is the economic risk associated with undefined future government policies and regulatory frameworks.” The study further suggests that policy uncertainty leads to delayed economic recovery, particularly after the recession, as households defer their consumption decisions. Istiak and Alam (2020) examined the impact of US economic policy uncertainty on the stock market fluctuations for Gulf Cooperation Council (GCC) countries. Based on monthly observations from 1992 to 2018 using the linear and nonlinear structural vector autoregression models, the study found that an increase in US economic policy uncertainty leads to a significant decline in the stock market index of GCC countries. Furthermore, the study obtained a symmetric association between the GCC countries' stock market index and US economic policy uncertainty. Contrary to earlier studies, Tajaddini and Gholipour (2020) explored the relationships between economic policy uncertainty and investments, particularly in research and development, and found no negative association between economic policy uncertainty and investment decisions in R&D. In a unique study, Vanlaer et al. (2020) discussed that uncertainty impacts consumer confidence, which has direct negative implications on household savings and consumption behavior. For Denmark's economy, Bergman, and Worm (2020) examined how economic policy uncertainty impacts consumer confidence and the expectations of households on their financial position. The study concluded that economic policy uncertainty is key in impacting consumer confidence and household-level expectations. The study by Nowzohour and Stracca (2020) made interesting explorations on how consumer confidence is globally correlated with stock market volatility and economic policy uncertainty for a set of 27 countries from 1985 to 2016. The results are coherent with the underlying postulate specifying that consumer confidence is closely correlated with the global policy and uncertainty and stock market volatility. The study concludes that speculating on the contemporaneous impact of consumer confidence on the macroeconomy is important.

The work by Gholipour et al. (2021) recently made a significant contribution by investigating how the global financial crisis, the Brexit incident, and the global COVID-19 pandemic generated uncertainty inducing economic policy uncertainty. This study further discusses the impact of economic policy uncertainty on the consumer confidence of a major set of countries with inbound tourism to African countries. The results confirm the earlier discussion in the literature on how uncertainty from economic policy severely impacts consumer confidence. This has a feedback effect on inbound tourist inflows to Africa from other major nations such as France, Japan, Russia, Spain and China. The study by Ozdemir et al. (2021) made a noteworthy exploration on the long-drawn nexus between economic policy uncertainty and demand for hotel room booking. This study further examined the moderating impact of consumer sentiment in this context. Based on the novel econometric estimation techniques, we found a significant mediating impact of consumer sentiment on economic policy uncertainty and its relationship with demand for hotel booking. This study thus provides interesting insights on consumers' perceptions of policy-related uncertainty.

2.2 Economic policy uncertainty impact: asymmetric implications

A strand of the literature deliberates on the asymmetric impact of policy uncertainty on consumer and household decision-making and investment decisions of business houses. Notable studies include works by Aye (2019) , Bahmani-Oskooee et al. (2021) , and Murad and Salim et al. (2021) . However, explorations in the literature continue to be scarce. Using quarterly time-series data for 1990Q1 to 2018Q2 for South African countries ( Aye, 2019 ), we explored the asymmetric uncertainty impact of fiscal policy on the economic functioning of the nation. Results on the GARCH asymmetry model show that bad news (as opposed to good news) originating from fiscal policy uncertainty has more severe impact on real economic activity. This study concludes that the importance of asymmetric impact owing to uncertainty from fiscal policy should be provided careful consideration owing to its association with economic growth.

Bahmani-Oskooee et al. (2021a) discussed the asymmetric impact of policy uncertainty on investment and consumption decisions in the context of G7 nations. Their study concludes that increased uncertainty damages the domestic economy for G7 nations more severely compared to favorable impact owing to a decline in uncertainty. Moreover, they suggest the strategic need to control for the adverse implications of uncertainty, particularly on the consumption and investment decisions in the G7 nations. Chen et al. (2020) examined the effect of economic policy uncertainty on the volatility of the exchange rate for China using annual observations from December 2001 to November 2018. The findings of the study based on quantile regression demonstrate the asymmetric impact of economic policy uncertainty on exchange rate volatility. Bahmani-Oskooee et al. (2021) explored the asymmetric impacts of economic policy uncertainty on income volatility for 41 states in the United States. The study obtained asymmetric implications on income volatility for both the short and long run. Aydin et al. (2021) explored the asymmetric impact of economic policy uncertainty on the stock prices of BRICS nations from March 2003 to March 2021. The findings confirm asymmetries across economic policy uncertainty and stock markets for the BRICS nations. This study concludes that the findings provide interesting insights into the impact of positive and negative shocks on stock market performance in BRICS countries. The study by Ugurlu-Yildirim et al. (2021) , which uses the nonlinear autoregressive distributed lag model, explored the cointegrating association between stock market conditions, monetary policy uncertainty, and sentiments of the investors for the US economy. These findings demonstrate cointegrating relationships across the variables. Furthermore, the impact of monetary policy uncertainty on investors' sentiment is negative and significant. An interesting study by Rehman et al. (2021) using novel estimation techniques of nonparametric causality in quantiles examined the asymmetric impact of economic policy uncertainty on the US markets using observations from January 1995 to December 2015. The results demonstrate the nonlinear impact of economic policy uncertainty on investor sentiment across the US markets. Murad et al. (2021) explored the symmetric and asymmetric impact of economic policy uncertainty in the context of demand for money in India. The observation period runs from 2003M1 to 2018M4. This study demonstrates that the asymmetric impact on the demand for money in India is a short-run manifestation.

The preceding discussion reflected the importance of policy uncertainty as a major driver of consumer confidence and household expenditure plans. However, in the literature, only limited information can be found on the interconnectedness of the major drivers of consumer confidence, which is indispensable for policy implications on monitoring the role of uncertainty on consumer perceptions. Research on the potential drivers of consumer confidence alongside the resounding importance of macro-variables is increasingly needed. Building on these research gaps, this study attempts to examine the impact of policy uncertainty in a multivariate framework on consumer confidence. The current study, considering the limitations of the linear model, attempts to explore the behavior of policy uncertainty in a nonlinear framework.

2.3 Research question and the hypothesis

Policy uncertainty impacts consumer confidence.

Policy uncertainty impacts consumer confidence asymmetrically.

3. Dataset, description of variables and methodology

3.1 dataset [1] and description of variables.

The dependent variable is the consumer confidence index denoted by CCI. The data set are obtained in monthly observations, beginning from the first month of 1995 to the third month of 2018. The variables chosen were seasonally adjusted. The data set are available from OECD (2019) .

The major explanatory variable is the overall policy uncertainty index denoted by GU. GU is constructed as a composite index of economic uncertainty policy index, fiscal policy uncertainty index, monetary policy uncertainty index, trade policy uncertainty index and exchange policy uncertainty index. The Principal Component Analysis is applied for the construction of the index GU. The data on the economic uncertainty policy index, fiscal policy uncertainty index, monetary policy uncertainty index, trade policy uncertainty index and exchange policy uncertainty index, are compiled from Arbatli et al . (2017) . The financial stress index denoted by FS and the share price index denoted by S is the major control variables. The FS indicator is obtained from the Asia Regional Integration Center, Tracking Asian Integration. The data on the share price index is obtained from the OECD (2019) . The data sets for all the variables are obtained in monthly observations, beginning from the first month of 1995 to the third month of 2018.

3.2 Methodology

3.2.1 theoretical framework.

The earliest theoretical model focusing on the importance of consumer confidence can be found in the pioneering study of Hall (1978) . The study by Hall (1978) explained that assuming the consumers behave as per the postulate of the Permanent Income Hypothesis, the household expectations in the ( t + 1)th time is impacted by the behavior of the economy and consumer expenditure of the t th time. Based on the random walk theory following Hall (1978) , the theoretical framework of the current model is explained in Equation (1) : (1) U ( CC ) = Exp t ∑ t = 0 ∞ ( 1 + α ) − t U ( CC t )

CC denotes the levels of consumer expectation,   α is the discount factor, Exp denotes the expected function of information content. Following the study by Katona (1975) , this study proposes how policy uncertainty, share market upheaval and financial stress affect consumers' confidence see Equation (2) for empirical investigation: (2) CCI t + 1 =   β 0 + β 1   GU t + β 2   X t +   u t where CCI t + 1 represents the consumer confidence index, GU t is the overall uncertainty measure and X t represent the control variables and u t is the usual error term. β i  = parameters of the model; i −1, 2. The impact on CCI by the explanatory variables comes with a time lag. An increase in GU will lead to a glum future thereby reducing CCI, a high value of FS implies macroeconomic stress this will also negatively impact CCI and further higher S implies high volatility of the economy which will dampen the CCI.

3.2.2 Econometric specification

3.2.2.1 unit root tests.

Before application of any time series method, it is essential to find out whether the set of observations is stationary or not, it would be counterfeit to obtain the results, with a time series of observations that are non-stationary. To explore the stationary properties of the time series, the unit root test of augmented Dickey–Fuller unit root test (ADF test), (1979) and the Phillips–Perron unit root test (PP), Phillips, and Perron (1988) is used here.

However, the difficulty with the ADF and PP test methods is with the basic presumption of the linearity in the time series. Usually, due to economic emergencies and policy shifts, the structural change takes place in many time series. The importance of examination of structural breaks is essential for Japan because it has gone through peculiar economic circumstances. In this paper ( Clemente et al. , 1998 ), the unit root with structural breaks is used. The null hypothesis of Clemente et al. (1998) unit root test can be found in Equation (3) : (3) H 0 :   y t = y t − 1 + δ 1 DTB 1 t + δ 2 DTB 2 t + ε t

The alternative hypothesis are in equation (4) (4) H 1 :   y t = ε + d 1 DU 1 t + d 2 DTB 2 t + e t

Here DTB i t is a pulse variable it has the value of 1 when t  =  TB i +1 ( i  = 1, 2) and 0 or else. Again DU i t  = 1 when t > TB i ( i  = 1, 2) and it is 0 or else.

After determining the stationary properties of the time series, the next task is to see the long-run cointegrating relation of the variables. The study applies the NARDL (Non-Linear Autoregressive Distributed Lag Model) method to examine the long-run relationship among the variables.

3.2.3 NARDL method of cointegration

The Nonlinear Auto-Regressive Distributed Lag Model (NARDL) proposed by Shin et al. (2014) is applied in this study to examine the scope of the passthrough of overall policy uncertainty (GU) into consumer confidence index over the long-run and short-run. A model for the long-run asymmetric cointegration is shown in Equation (5) (5) y t = β + x t + + β − x t − + u t where y t , is the dependent variable and x t is the regular set of the independent, explanatory variables. The variable x t is additionally divided into the partial sums of the negative and positive alterations in the explanatory variables in Equation (6) : (6) x t = x 0 + x t + + x t − where x t + and x t − are partial sum series that can be assessed with the help of Equations (7) and (8) : (7) x t + = ∑ j = 1 t Δ x j + = ∑ j = 1 t     max   ( Δ x j + ,   0 ) (8) x t − = ∑ j = 1 t Δ x j − = ∑ j = 1 t     min   ( Δ x j − ,   0 )

Executing, the process of Shin et al. (2014) , Equation (5) can be converted to an asymmetric error correction model (AECM). The NARDL ( p , q ) equation can be explained as: (9) Δ y t =   ρ y t − 1 +   θ + x t − 1 + + θ − x t − 1 − + ∑ j = 1 p φ j Δ y t − j +   ∑ j = 0 q ( π j + Δ x t − j + + π j − Δ x j − t − ) +   ε t

Equation (8) can be calculated using OLS and the occurrence of the cointegrating relationship can be found by testing the joint null hypothesis test ( ρ = θ + = θ − = 0 ). The asymmetric relation can be verified by making comparisons of the coefficients θ + and θ − . If the value of the coefficients is dissimilar, then the positive and negative changes of the independent variable would impact the dependent variable in a diverse way. The dissimilarity between θ + and θ − can be assessed for the null hypothesis θ +  =    θ − in a Wald test. If the null hypothesis is overruled, it would sanction the existence of the nonlinear relationship in the long run.

The NARDL methodology has certain advantages which make the application suitable for evaluating the asymmetric repercussions of uncertainty. The study by Shin et al. (2014) explained that the problems with the earlier estimation procedures were associated with the selection of the threshold variables.

4.1 Preliminary observations

Table 1 reports the basic characteristics of the observations, the mean of CCI is 99.5 while the SD is 1.19. The variable S has a high SD of 19.14. Table 2 presents the results of the correlation matrix. The results of the correlation matrix show that the consumer confidence index is negatively correlated with the overall uncertainty index and financial stress index. Since no substantial inference can be made based on the results of the correlation matrix, the subsequent subsection discusses the results based on the econometric model.

4.2 Results based on the econometric model

4.2.1 unit root tests.

To obtain the stationarity of the series, the augmented Dickey-Fuller unit root test (ADF test) ( Dickey and Fuller, 1979 ) and the Phillips–Perron unit root test (PP) ( Phillips and Perron, 1988 ) are performed ( Table 3 ). Since none of the variables is of I(2), the NARDL bound test methodology is applied. Table 4 explains the Clemente et al. (1998) unit root test encompassing the structural breaks. Since none of the variables is of order I(2), we estimate the NARDL model for testing the long-run cointegrating relation of the variables.

4.2.2 Cointegration: bound tests

Table 5 provides the F -statistics tests for the linear and nonlinear cointegration relations, respectively, found in Panel A and B. The estimated F -statistics decline (do not decline) to discard the null hypothesis of no cointegration relationship if the statistics are smaller (larger) than the lower(upper) critical values. If the statistics fall within lower and upper critical values the results become indecisive. The F -statistics shown in Panel A of Table 5 explain that no cointegrating relationship exists among the variables. Further, on examining the asymmetric impact based on the nonlinear ARDL, the results indicate that nonlinear cointegrating (long-run) relation exist between consumer confidence index, overall uncertainty index, financial stress index and stock price index for Japan, evident from F -statistics Panel B of Table 5 .

4.2.3 NARDL: Results

We explore the short-run dynamics and the long-run changes in the relation of the dependent variable with the explanatory variables alongside the positive and negative transformations. Based on the results of Table 6 we find that in the short-run GU has a significant impact on CCI, further FS also impacts CCI significantly. A 10% rise in GU leads to the dampening of CCI by 123% again a 10% fall in GU leads to the boosting of CCI by 80%.

Table 7 presents the long-run results based on the NARDL model. The most notable feature of the outcome of the empirical estimation is the differentness in response of consumer confidence to a positive change in global policy uncertainty (GU) vis-à-vis the response to a negative change in GU. A 10% rise in GU in the long run dampens the confidence of the consumers by 39%, as against a 10% fall in GU boosts the confidence of the consumers by 4%.

It is clear from the results of Table 7 that policy uncertainty shocks are an important source of deviation in consumer confidence. The relationship between GU and CCI demonstrates that changes in confidence levels are almost exclusively pushed by shocks emanating from policy uncertainty. Overall, this finding confirms Hypothesis 1 . This mechanism is also confirmed in the research by Nowzohour and Stracca (2020) , Vanlaer et al. (2020) and Bahmani-Oskooee and Mohammadian (2021a) .

According to our model, the shocks stemming from share prices and financial stress also impacts consumer confidence asymmetrically. The results reflect that the news from shocks provide better knowledge to consumers irrespective of their beliefs and accordingly the consumers respond in heterogeneity to positive and negative changes. A rise by 10% in FS dampens the confidence of the consumers by 8%, similarly, a fall in the FS by 10% enhances consumers' confidence by about 5% ( Table 7 ). Further, a 10% rise in S dampens consumer confidence by 6% whereas a fall in S raises the confidence levels by 4.8%. Such findings are consistent with the earlier studies in the literature ( De Mendonça and Almeida, 2019 ).

Broadly speaking there are three ways to interpret our NARDL results. First, an upward movement in GU corresponds to an unanticipated policy uncertainty shock which worsens consumer confidence by impacting indirectly consumers expectations. Second, an upward movement in GU captures the bad news which is not fully captured in other variables, this triggers a harmful effect on the confidence of consumers. The impact of the harm of bad news is more severe than the good news emanating from downward movement in GU. Third, we see from the evidence of our empirical exercise that GU is an important propagating mechanism of uncertainty shocks which asymmetrically impacts consumer confidence. The asymmetry Wald Test ( Table 7 , Panel B) confirms the long-run asymmetric impact. The interesting behavior is the asymmetric response to the shocks. This finding is comparable to the recent discovery by Ozdemir et al. (2021) and Gholipour et al. (2021) . The hypothesis H2 is confirmed.

5. Discussion

This study's findings suggest that only sound policy frameworks can positively influence consumer confidence, which, in turn, has favourable impact on the broad macroeconomic performance of the country. Consumer expectancies matter immensely as consumption is a significant component of major global economies. In the world's main economies, private consumption comprises at least half the country's GDP. In Japan, private consumption was about 55% of its nominal gross domestic product in 2019. Thus, a major downfall in consumer confidence because of the feeling of uncertainty of the economy will lead to economic decline. Our findings confirm the earlier studies by Ozdemir et al. (2021) and Gholipour et al. (2021) .

Overall, we found that consumer confidence in households is substantially impacted by policy uncertainty. Lee et al. (2019) assessed consumer confidence and its related impact on expenditures and had similar findings. Heightened levels of uncertainty originating from policy decisions affect consumer confidence; however, asymmetries in impact matters. Similarly, Aye (2019) explored asymmetries in uncertainty and their overall impact on consumer confidence levels. These studies obtain a statistically significant impact of asymmetry on consumer confidence, which indirectly assesses consumers' spending patterns.

5.1 Robustness tests: main findings

To test model robustness, the dataset was split into two subperiods, one covering monthly observations from 1995 to 2000, and a set of 72 observations was chosen. This period is related to the time of the Asian financial crisis. The second subperiod covers the monthly observations from 2005 to 2011, and a set of 84 observations was chosen. The second period covered the global financial crisis. The results confirm long-run asymmetric relationships across the variables. These tables are reported in Appendix .

6. Conclusion and policy implications

This study, based on monthly observations covering the period from the first month of 1995 to the third month of 2018, explored the asymmetric influence of the overall policy uncertainty index generated through the principal component analysis based on the data sets developed ( Arbatli et al. , 2017 ) on consumer confidence index in Japan, using the nonlinear methodology of ARDL, developed by Shin et al. (2014) . The financial stress index and share price index were major control variables used in this study. Further, we have fragmented the period into subperiods while considering the Asian economic and global financial crises to study the robustness of the exercise. The study establishes the long-run cointegrating behavior among the observations based on the nonlinear ARDL model. Our results confirm asymmetric impact of the overall policy uncertainty index on the CCI in Japan. A 10% escalation in the overall policy uncertainty in the long-run diminishes the consumer confidence index by 39%. Similarly, a 10% decrease in the overall uncertainty index increases consumer confidence by 4%. The asymmetry Wald test parameter confirms the existence of asymmetric behavior in the consumer confidence index by the concerned set of the explanatory variables. The robustness tests confirm the existence of asymmetric behavior in the consumer price index by the overall uncertainty index in conjunction with other control variables. Therefore, this study demonstrates how heightened uncertainty can affect outcomes related to consumer behavior. Our evidence and discussion suggest that credible policy prescriptions can favourably influence consumers in the Japanese context, which can indirectly foster macroeconomic performance. Analysis of the consumer confidence indicator has become increasingly useful because of its underlying co-movement with economic movements. Studies have extended the analysis of consumer behavior, which is not clearly obtained in the established data ( Bergman and Worm 2020 ; Ozdemir et al. 2021 ). This study provides important insights into how consumers react to changes in indicators in their way of assessing market movements. Moreover, our results suggest that periods of the global financial crisis are important in impacting consumer confidence. Further research is needed for establishing the underlying mechanisms. We posit the importance of the global crisis in impacting consumer confidence levels. After periods of financial crisis, consumer confidence levels tend to fall, which may impact a country's long-term financial situation. The results suggest that confidence levels deteriorated owing to the impact of the crisis. However, households react differently to a positive shock vis-à-vis negative shock. When recovery gains momentum, household confidence boosts and has interesting implications for policy research. Our analysis, based on aggregate data, helped identify the undercurrent association between consumer confidence and uncertainty. Collecting micro-level data could enhance the scope of the analysis, particularly across countries. Follow-up research with high-frequency data can be used for testing the robustness of our findings.

Based on the empirical findings of this study, we can make subsequent policy propositions. Policymakers must take steps to reinforce institutions through which better levels of transparency and communication can be maintained. Public law, order, and management practices should be better guides on consumers' expectations and confidence. The evaluation shows that government policy decisions tend to significantly influence consumer confidence. Thus, policy planners should focus on the adoption of strategies inculcating credibility. In the Japanese context, credible policy plans include medium-term reforms in the trade and investment sectors, which would create investments related to trade. This would foster consumer confidence in the trajectory of Japanese macroeconomic policies. Future policy efforts in Japan should also be directed toward lessening uncertainty originating from monetary policy prescriptions. The Bank of Japan should periodically update its communication framework so that there is transparency that boosts consumer expectations and confidence levels. This study's results expand the scope of investors' decision-making as it provides an in-depth understanding of the drivers of consumers' confidence in the economy of Japan. This relates to greater level of understanding of consumers' saving consumption plans. Such explorations can be crucial in developing incentives to raise the level of consumers' expectations in matters related to public policy. For policy analysts, there appears to be a prospect to draw policies competent in utilising the relationship between policy uncertainty and stock market situation and its association with consumer confidence. Provided that consumer confidence is a major driver of the real economy, steps should be taken to enhance consumer confidence levels in Japan. Such policy initiatives include education, awareness, and empowerment. If such measures are taken, levels of consumers' confidence and of well-being will increase. These steps would lessen the adversative implications of uncertainty. This would allow consumers to plan for expenditures while focusing on the long-run expectations of employment and income opportunities.

Future research could explore the impact of uncertainties on consumer expectations by considering the heterogeneity of age, income, and educational background. Another direction for future research could be the use of high-frequency data on mixed sampling, which would provide significant insights into the heterogeneity of consumer behavior. Further research could also analyse how uncertainty in the stock market represented by volatility has spill over in the formation of consumer expectations.

Descriptive statistics