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Does Economic Policy Uncertainty Lead Systemic Financial Risk? A Comparative Assessment Mikhail Stolbov Maria Shchepeleva MGIMO-University Bank of Russia Alexander Karminsky NRU HSE 2nd World Congress of Comparative Economics Saint Petersburg 16th June 2017 Outline • • • • • Motivation Methodology Data Results Takeaways and policy implications 2 Motivation • Systemic risk is a central category in macrofinancial research • Yet, most of the studies on systemic risk are empirical (“measurement (almost) without theory”) • Origins of systemic risk remain underexplored 3 Motivation • We depart from the premise that uncertainty boils down to risk if information about the likelihood of events to happen is known (Knight, 1921) • Systemic risk is a special case but we still need knowledge about probability distributions (though almost always deviating from the Gaussian one) to estimate it • Incoherent and inconsistent decisions in the field of economic policy may aggravate sentiment in financial markets, making ex ante assessment of these probability distributions more complicated and thereby fueling systemic risk build-up. Hence, economic policy uncertainty can be a precursor for systemic risk 4 Motivation • To our knowledge, there have been very few attempts to marry uncertainty and systemic risk in the literature: – Dicks and Fulghieri (2016) propose a theoretical model, showing how uncertainty aversion among investors triggers systemic runs on financial institutions – On the empirical side, Manzo (2014), Bernal et al. (2016) and Sun et al. (2016) investigate interrelations between different systemic risk measures (CDS spreads, delta-conditional value-atrisk and St. Louis Fed Financial Stress Index, respectively) and a proxy for economic policy uncertainty (Economic Policy Uncertainty Index by Baker et al. (2016)) – The EPU index measures covers policy-related uncertainty in major newspapers 5 Motivation In this paper, we follow the empirical track, aiming to study lead-lag linkages between economic policy uncertainty (EPU index) and systemic risk for nine European economies in January 2010-September 2016: • • • • • • • • • France Germany Ireland Italy Netherlands Russia Spain Sweden UK 6 Methodology Our analysis is split into 4 stages • We derive a novel composite systemic risk measure for the sample countries by means of a dynamic factor model • Conventional and nonparametric (Diks and Panchenko, 2006) Granger causality tests are conducted for the composite systemic risk measure and the EPU index in the time domain • Then, we assess lead-lag patterns between the variables in the timefrequency domain, applying continuous wavelet decomposition (CWT) and wavelet coherence • Finally, we study these linkages in a data-rich environment, specifying Bayesian VARs and obtaining impulse-response functions and forecast error decompositions 7 Data To build the composite measure, we consider three dimensions of systemic risk: • Domestic financial sector fragility • Sovereign creditworthiness • Vulnerability to external shocks associated with sudden capital outflows All the variables in these categories have been standardized so that their increase signifies a surge in systemic risk 8 Data Domestic financial sector fragility • LRMES (LRMES) • • Leverage (LEVERAGE) • Market Capitalization • (MCAP) • SRISK (SRISK) • Volatility (VOLAT) • Aggregate probability of default (PD) Sovereign creditworthiness 5-year CDS spreads (CDS) 10-year government bond yields (GOVBOND) Vulnerability to external shocks • National stock market index correlation with the world market (WMCorr) • The Diebold-Yilmaz net connectedness index for stock market volatilities (DY) • Changes in allocations to a country’s stock market by international investment funds (EQALLOC) • Changes in allocations to a country’s bond market by international investment funds (BONDALLOC) 9 Data In the data-rich setting, the interrelation between the composite systemic risk measure (DF) and the EPU index is estimated by means of the BVAR model with endogenous and exogenous Exogenous variablesEndogenous • OECD composite leading indicator (CLI) • EPU index (EPU) • Composite systemic risk measure (DF) • Unemployment rate (U) • Consumer price index (CPI) • Growth rate of industrial production (IP) • • • • VIX index (VIX) TED spread (TED) US yield curve (YCURVE) Composite systemic stress index for the Eurozone (CISS) • IMF commodity price index (COMINDEX) 10 Results: stage 1 • Based on the maximum likelihood ratios, we opt for a dynamic factor model with a single factor • Domestic financial sector fragility and sovereign creditworthiness are by far more important than vulnerability to external shocks, as regards the relative significance of the three risk dimensions • LEVERAGE, MCAP and SRISK have the largest factor loadings in dimension 1; CDS is more important in dimension 2; WMCorr and DY are the most salient in dimension 3 • The dynamics of the composite systemic risk measures (DF) tend to co-move (except for Ireland and Russia), exhibiting common hikes in the late 2011 and mid-2012 11 Results: stage 1 12 Results: stage 2 • Conventional Granger causality tests underscore causality running from the composite systemic risk measure to national EPU indices for Russia and Spain (at the 1% level), Germany (at 5%) and for France and Italy (at 10%) • Nonparametric tests confirm the findings in case of France, Germany and Italy, but for Spain, Sweden and the UK, the EPU indices are found to drive systemic risk; no causality is found for Russia 13 Results: stage 3 • Against the backdrop of these conflicting results, we assume that the causal directions may vary over time spans • Hence, it is feasible to investigate lead-lag relations in the time-frequency domain, rather than confine to the time domain • To this end, we perform continuous wavelet decomposition of both series and compute wavelet coherences which are localized correlation coefficients in the time-frequency domain • The wavelet coherences also show if the two series move in- or out-of-phase 14 Results: stage 3 An example of wavelet coherence analysis for France • Horizontal axis – time (in months) • Vertical axis – frequency (from higher to lower) • Arrows indicate if DF and EPU move in-phase (to the right) or out-of-phase (to the left) • Warmer colors in delineated areas – wavelet coherences significant at 5% • The arrows tend to point to the right or slightly right-down or left-up if DF leads EPU in- or out-of-phase. This evidence is relatively weak, albeit supportive of the findings in the time-domain causality tests 15 Results: stage 3 • In the time-frequency space, there is more evidence that economic policy uncertainty leads systemic risk, in particular, over lower frequencies, i.e. over time horizons embracing more than 12 months • This evidence is particularly pronounced in case of Ireland, the Netherlands, the UK and for Germany, Russia and Spain in the post2013 period 16 Results: stage 3 Ireland Netherlands 17 Results: stage 3 UK Germany 18 Results: stage 3 Russia Spain 19 Results: stage 4 • In the BVAR models, economic policy uncertainty tends to lead systemic risk in Ireland, Italy, Russia and Spain • In Russia, the EPU index explains up to 19% of the variance of DF; in Ireland, Italy and Spain – in the range of 6-7% • No significant effect of the EPU index on DF or vice versa is found for France, Germany, the Netherlands, Sweden and the UK 20 Results: stage 4 • The EPU index has an economically sizeable contractionary effect • In Ireland and Russia, it affects industrial production growth, explaining 21 and 3% of its variance, respectively • In Italy and the Netherlands, it leads to an increase in unemployment rate, accounting for about 4 and 2% of its variance • Finally, in Spain it acts in a roundabout way, fueling systemic risk first, which in its turn raises unemployment and suppresses industrial production growth • For the whole sample the EPU index leads systemic risk, which in its turn increases unemployment rate (pooled BVAR IRF estimates below) 21 Results: stage 4 22 Takeaways and policy implications • Economic policy uncertainty matters for systemic risk, especially in financially fragile economies, over longer time horizons and in data-richer environments • By leading systemic risk in these cases, economic policy uncertainty strengthens contractionary effects on the real sector • Macroprudential policies need to address uncertainty issues in order to deter systemic risk • In particular, they need to be largely preemptive and equipped with an efficient communication strategy to impact sentiment and expectations about economic policy, which is crucial to maintain stability in financial markets 23