Download Systemic risk in European Economies: Deciphering Leading

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
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Motivation
Methodology
Data
Results
Takeaways and policy implications
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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
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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
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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
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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:
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France
Germany
Ireland
Italy
Netherlands
Russia
Spain
Sweden
UK
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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
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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
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Data
Domestic financial sector
fragility
• LRMES (LRMES)
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• Leverage (LEVERAGE)
• Market Capitalization
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(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)
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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)
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VIX index (VIX)
TED spread (TED)
US yield curve (YCURVE)
Composite systemic stress index for
the Eurozone (CISS)
• IMF commodity price index
(COMINDEX)
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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
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Results: stage 1
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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
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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
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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
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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
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Results: stage 3
Ireland
Netherlands
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Results: stage 3
UK
Germany
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Results: stage 3
Russia
Spain
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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
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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)
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Results: stage 4
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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
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