Niet beschikbaar in het Nederlands

Bernd Schwaab

Research

Division: Financial Research
Current Position: Senior Economist
Fields of interest: Financial Economics,Mathematical and Quantitative Methods,Macroeconomics and Monetary Economics
Email: bernd.schwaab@ecb.europa.eu

Biography

Education: 2007-2010
Ph.D Economics, Tinbergen Institute and VU University Amsterdam, the Netherlands; 2005-2007
M.Phil Economics, Tinbergen Institute Amsterdam, the Netherlands; 2003-2005
M.A. Economics, Clark University, Worcester, MA/USA; 1998-2001
B.A. Banking & Finance, DHBW Mannheim, Germany
Professional experience: 2023-
Principal Economist - Financial Research Division, Directorate General Research, European Central Bank; 2018-2022
Senior Economist - Financial Research Division, Directorate General Research, European Central Bank; 2010-2017
Economist - Financial Research Division, Directorate General Research, European Central Bank; 1998-2001
Deutsche Bank AG Mannheim
Awards: 2015
"Eervolle vermelding" from the Royal Dutch Academy of the Sciences as a runner-up for the 2015 "Christiaan Huygens" dissertation award for best Dutch PhD thesis in Economics/ Econometrics/ Actuarial Sciences completed between 2010 and 2014.

ECB publications

13 February 2023

WORKING PAPER SERIES - No. 2780

Dynamic nonparametric clustering of multivariate panel data

English

Abstract: We introduce a new dynamic clustering method for multivariate panel data char-acterized by time-variation in cluster locations and shapes, cluster compositions, and, possibly, the number of clusters. To avoid overly frequent cluster switching (flickering), we extend standard cross-sectional clustering techniques with a penalty that shrinks observations towards the current center of their previous cluster as-signment. This links consecutive cross-sections in the panel together, substantially reduces flickering, and enhances the economic interpretability of the outcome. We choose the shrinkage parameter in a data-driven way and study its misclassification properties theoretically as well as in several challenging simulation settings. The method is illustrated using a multivariate panel of four accounting ratios for 28 large European insurance firms between 2010 and 2020.
JEL Code: C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C38 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Classification Methods, Cluster Analysis, Principal Components, Factor Models
G22 : Financial Economics→Financial Institutions and Services→Insurance, Insurance Companies, Actuarial Studies

16 January 2023

RESEARCH BULLETIN - No. 103

The safe asset potential of EU-issued bonds

English

Abstract: A safe asset is of high credit quality, retains its value in bad times and is traded in liquid markets. We show that bonds issued by the European Union (EU) are widely considered to be of high credit quality, and that their yield spread over German Bunds remained contained during the 2020 COVID-19 pandemic recession. Recent issuances under the EU’s SURE and NGEU initiatives helped improve EU bonds' market liquidity from previously low levels, also reducing liquidity risk premia. Eurosystem purchases and holdings of EU bonds did not impair market liquidity. Currently, one obstacle to EU bonds achieving a genuine euro-denominated safe asset status, approaching that of Bunds, lies in the one-off, time-limited nature of the EU’s COVID-19-related policy responses.
JEL Code: G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
H63 : Public Economics→National Budget, Deficit, and Debt→Debt, Debt Management, Sovereign Debt

16 August 2022

WORKING PAPER SERIES - No. 2712

Can EU bonds serve as euro-denominated safe assets?

English

Abstract: A safe asset is of high credit quality, retains its value in bad times, and is traded in liquid markets. We show that bonds issued by the European Union (EU) are widely considered to be of high credit quality, and that their yield spread over German Bunds remained contained during the 2020 Covid-19 pandemic recession. Recent issuances and taps under the EU’s SURE and NGEU initiatives helped improve EU bonds' market liquidity from previously low levels, also reducing liquidity risk premia. Eurosystem purchases and holdings of EU bonds did not impair market liquidity. Currently, one obstacle to EU bonds achieving a genuine euro-denominated safe asset status, approaching that of Bunds, lies in the one-off, time-limited nature of the EU’s Covid-19-related policy responses.
JEL Code: E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
H63 : Public Economics→National Budget, Deficit, and Debt→Debt, Debt Management, Sovereign Debt

22 September 2021

RESEARCH BULLETIN - No. 87.1

A novel risk management perspective for macroprudential policy

English

Abstract: When considering the use of macroprudential instruments to manage financial imbalances, macroprudential policymakers face an intertemporal trade-off between facilitating short-term expected growth and containing medium-term downside risks to the economy. To assist policymakers in assessing this trade-off, in this article we propose a risk management framework which extends the well-known notion of growth-at-risk to consider the entire predictive real GDP growth distribution, with a view to quantifying the macroprudential policy stance. A novel empirical model fitted to euro area data allows us to study direct and indirect interactions between financial vulnerabilities, financial stress and real GDP growth, incorporating non-linear amplification effects among all variables. Our framework can support policymakers by facilitating model-based macro-financial stress tests and model-based assessments of when to adjust macroprudential instruments.
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
Network: Research Task Force (RTF)

29 July 2021

WORKING PAPER SERIES - No. 2577

Dynamic clustering of multivariate panel data

English

Abstract: We propose a dynamic clustering model for uncovering latent time-varying group structures in multivariate panel data. The model is dynamic in three ways. First, the cluster location and scale matrices are time-varying to track gradual changes in cluster characteristics over time. Second, all units can transition between clusters based on a Hidden Markov model (HMM). Finally, the HMM’s transition matrix can depend on lagged time-varying cluster distances as well as economic covariates. Monte Carlo experiments suggest that the units can be classified reliably in a variety of challenging settings. Incorporating dynamics in the cluster composition proves empirically important in an a study of 299 European banks between 2008Q1 and 2018Q2. We find that approximately 3% of banks transition per quarter on average. Transition probabilities are in part explained by differences in bank profitability, suggesting that low interest rates can lead to long-lasting changes in financial industry structure.
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models

2 June 2021

WORKING PAPER SERIES - No. 2565

The risk management approach to macro-prudential policy

English

Abstract: Macro-prudential authorities need to assess medium-term downside risks to the real economy, caused by severe financial shocks. Before activating policy measures, they also need to consider their short-term negative impact. This gives rise to a risk management problem, an inter-temporal trade-off between expected growth and downside risk. Predictive distributions are estimated with structural quantile vector autoregressive models that relate economic growth to measures of financial stress and the financial cycle. An empirical study with euro area and U.S. data shows how to construct indicators of macro-prudential policy stance and to assess when interventions may be beneficial.
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
Network: Research Task Force (RTF)

31 May 2021

WORKING PAPER SERIES - No. 2561

Euro area sovereign bond risk premia during the Covid-19 pandemic

English

Abstract: We decompose euro area sovereign bond yields into five distinct components: i) expected future short-term risk-free rates and a term premium, ii) default risk premium, iii) redenomination risk premium, iv) liquidity risk premium, and a v) segmentation (convenience) premium. Identification is achieved by considering sovereign bond yields jointly with other rates, including sovereign credit default swap spreads with and without redenomination as a credit event feature. We apply our framework to study the impact of European Central Bank (ECB) monetary policy and European Union (E.U.) fiscal policy announcements during the Covid-19 pandemic recession. We find that both monetary and fiscal policy announcements had a pronounced effect on yields, mostly through default, redenomination, and segmentation premia. While the ECB's unconventional monetary policy announcements benefited some (vulnerable) countries more than others, owing to unprecedented flexibility in implementing bond purchases, the E.U.’s fiscal policy announcements lowered yields more uniformly.
JEL Code: C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
Network: Research Task Force (RTF)

20 May 2021

WORKING PAPER SERIES - No. 2556

A risk management perspective on macroprudential policy

English

Abstract: Macroprudential policymakers assess medium-term downside risks to the real economy arising from financial imbalances and implement policies aimed at managing those risks. In doing so, they face an inherent intertemporal trade-off between the expected growth and downside risks. This paper reviews the literature on Growth-at-Risk, embeds it in the wider literature on macroprudential policy, and proposes an empirical risk management framework that combines insights from the two literatures, by forecasting the entire real GDP growth distribution with a structural quantile vector autoregressive model. It accounts for direct and indirect interactions between financial vulnerabilities, financial stress and real GDP growth and allows for potential non-linear amplification effects. The framework provides policymakers with a macro-financial stress test to monitor downside risks to the economy and a macroprudential stance metric to quantify when interventions may be beneficial.
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
Network: Discussion papers

20 May 2021

DISCUSSION PAPER SERIES - No. 14

A risk management perspective on macroprudential policy

English

Abstract: Macroprudential policymakers assess medium-term downside risks to the real economy arising from financial imbalances and implement policies aimed at managing those risks. In doing so, they face an inherent intertemporal trade-off between the expected growth and downside risks. This paper reviews the literature on Growth-at-Risk, embeds it in the wider literature on macroprudential policy, and proposes an empirical risk management framework that combines insights from the two literatures, by forecasting the entire real GDP growth distribution with a structural quantile vector autoregressive model. It accounts for direct and indirect interactions between financial vulnerabilities, financial stress and real GDP growth and allows for potential non-linear amplification effects. The framework provides policymakers with a macro-financial stress test to monitor downside risks to the economy and a macroprudential stance metric to quantify when interventions may be beneficial.
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models

11 February 2021

WORKING PAPER SERIES - No. 2524

Modeling extreme events: time-varying extreme tail shape

English

Abstract: We propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation in the tail shape parameters. The score-driven updates used improve the expected Kullback-Leibler divergence between the model and the true data generating process on every step even if the GPD only fits approximately and the model is mis-specified, as will be the case in any finite sample. This is confirmed in simulations. Using the model, we find that Eurosystem sovereign bond purchases during the euro area sovereign debt crisis had a beneficial impact on extreme upper tail quantiles, leaning against the risk of extremely adverse market outcomes while active.
JEL Code: C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions

26 September 2019

RESEARCH BULLETIN - No. 62

Unconventional monetary policy operations – to what extent is there an upside for central bank balance sheet risks?

English

Abstract: This article studies this question by revisiting the Eurosystem's experience during the euro area sovereign debt crisis between 2010 and 2012. In some instances, the Eurosystem was able to remove excess risk from parts of its balance sheet by extending the scale of its operations, in line with Bagehot's well-known assertion that occasionally "the brave plan is the safe plan."
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
Network: Research Task Force (RTF)

25 January 2019

WORKING PAPER SERIES - No. 2225

Risk endogeneity at the lender/investor-of-last-resort

English

Abstract: We address the question to what extent a central bank can de-risk its balance sheet by unconventional monetary policy operations. To this end, we propose a novel risk measurement framework to empirically study the time-variation in central bank portfolio credit risks associated with such operations. The framework accommodates a large number of bank and sovereign counterparties, joint tail dependence, skewness, and time-varying dependence parameters. In an application to selected items from the consolidated Eurosystem's weekly balance sheet between 2009 and 2015, we find that unconventional monetary policy operations generated beneficial risk spill-overs across monetary policy operations, causing overall risk to be nonlinear in exposures. Some policy operations reduced rather than increased overall risk.
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models

6 November 2018

WORKING PAPER SERIES - No. 2193

Bank to sovereign risk spillovers across borders: evidence from the ECB’s Comprehensive Assessment

English

Abstract: We study spillovers from bank to sovereign risk in the euro area using difference specifications around the European Central Bank’s release of stress test results for 130 significant banks on October 26, 2014. We document that following this information release bank equity prices in stressed countries declined. Surprisingly, bank risk in stressed countries was not absorbed by their sovereigns but spilled over to non-stressed euro area sovereigns. As a result, in non-stressed countries, the co-movement between sovereign and bank risk increased. This suggests that market participants perceived that bank risk is shared within the euro area.
JEL Code: C68 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computable General Equilibrium Models
F34 : International Economics→International Finance→International Lending and Debt Problems

22 November 2017

RESEARCH BULLETIN - No. 40

Bank business models at negative interest rates

Bernd Schwaab

English

Abstract: Not all banks are the same. They differ in terms of size, complexity, organisation, activities, funding choices and geographical reach. This article shows how changes in the yield curve and reductions in the ECB’s deposit facility rate (DFR) to negative values have affected different types of banks in different ways, thus giving rise to different market perceptions of banks’ risks.
JEL Code: C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
G20 : Financial Economics→Financial Institutions and Services→General
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages

8 September 2017

WORKING PAPER SERIES - No. 2098

Do negative interest rates make banks less safe?

English

Abstract: We study the impact of increasingly negative central bank policy rates on banks’ propensity to become undercapitalized in a financial crisis (‘SRisk’). We ﬁnd that the risk impact of negative rates is moderate, and depends on banks’ business models: Banks with diversiﬁed income streams are perceived by the market as less risky, while banks that rely predominantly on deposit funding are perceived as more risky. Policy rate cuts below zero trigger different SRisk responses than an earlier cut to zero.
JEL Code: G20 : Financial Economics→Financial Institutions and Services→General
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
Network: Research Task Force (RTF)

29 June 2017

WORKING PAPER SERIES - No. 2084

Bank business models at zero interest rates

English

Abstract: We propose a novel observation-driven ﬁnite mixture model for the study of banking data. The model accommodates time-varying component means and covariance matrices, normal and Student’s t distributed mixtures, and economic determinants of time-varying parameters. Monte Carlo experiments suggest that units of interest can be classiﬁed reliably into distinct components in a variety of settings. In an empirical study of 208 European banks between 2008Q1–2015Q4, we identify six business model components and discuss how their properties evolve over time. Changes in the yield curve predict changes in average business model characteristics.
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
Network: Research Task Force (RTF)

10 June 2016

WORKING PAPER SERIES - No. 1922

Global credit risk: world country and industry factors

English

Abstract: We investigate the dynamic properties of systematic default risk conditions for firms in different countries, industries and rating groups. We use a high-dimensional nonlinear non-Gaussian state space model to estimate common components in corporate defaults in a 41 country sample between 1980Q1-2014Q4, covering both the global financial crisis and euro area sovereign debt crisis. We find that macro and default-specific world factors are a primary source of default clustering across countries. Defaults cluster more than what shared exposures to macro factors imply, indicating that other factors also play a signicant role. For all firms, deviations of systematic default risk from macro fundamentals are correlated with net tightening bank lending standards, suggesting that bank credit supply and systematic default risk are inversely related.
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models

13 January 2016

WORKING PAPER SERIES - No. 1875

The information in systemic risk rankings

English

Abstract: We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic components from a selection of key systemic risk rankings that have been proposed recently. We use a sample of 113 listed financial sector firms in the European Union over the period 2002-2013. The implied ranking from the principal components is less volatile than most individual risk rankings and leads to less turnover among the top ranked institutions. We also find that price-based rankings and fundamentals-based rankings deviated substantially and for a prolonged time in the period leading up to the financial crisis. We test the adequacy of our newly pooled systemic risk ranking by relating it to credit default swap premia.
JEL Code: E : Macroeconomics and Monetary Economics

6 August 2015

WORKING PAPER SERIES - No. 1837

Modeling financial sector joint tail risk in the euro area

English

Abstract: We develop a novel high-dimensional non-Gaussian modeling framework to infer measures of conditional and joint default risk for numerous financial sector firms. The model is based on a dynamic Generalized Hyperbolic Skewed-t block-equicorrelation copula with time-varying volatility and dependence parameters that naturally accommodates asymmetries, heavy tails, as well as non-linear and time-varying default dependence. We apply a conditional law of large numbers in this setting to define joint and conditional risk measures that can be evaluated quickly and reliably. We apply the modeling framework to assess the joint risk from multiple defaults in the euro area during the 2008-2012 financial and sovereign debt crisis. We document unprecedented tail risks between 2011-2012, as well as their steep decline following subsequent policy actions.
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes

19 December 2013

WORKING PAPER SERIES - No. 1626

Observation driven mixed-measurement dynamic factor models with an application to credit risk

English

Abstract: We propose a dynamic factor model for mixed-measurement and mixed-frequency panel data. In this framework time series observations may come from a range of families of parametric distributions, may be observed at different time frequencies, may have missing observations, and may exhibit common dynamics and cross-sectional dependence due to shared exposure to dynamic latent factors. The distinguishing feature of our model is that the likelihood function is known in closed form and need not be obtained by means of simulation, thus enabling straightforward parameter estimation by standard maximum likelihood. We use the new mixed-measurement framework for the signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody
JEL Code: C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill

11 December 2013

WORKING PAPER SERIES - No. 1621

Conditional and joint credit risk

English

Abstract: We propose an empirical framework to assess joint and conditional probabilities of credit events from CDS prices observed in the market. Our model is based on a dynamic skewed-t distribution that captures many salient features of CDS data, including skewed and heavy-tailed changes in the price of CDS protection, as well as dynamic volatilities and correlations that ensure that uncertainty and risk dependence can increase in times of stress. We apply the framework to euro area sovereign CDS spreads during the euro area debt crisis. Our results reveal significant time-variation in distress dependence and spill-over effects. We investigate in particular market perceptions of joint and conditional risks around announcements of Eurosystem non-standard monetary policy measures, and document strong reductions in joint risk.
JEL Code: C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill
Network: Macroprudential Research Network

10 September 2013

WORKING PAPER SERIES - No. 1587

Assessing asset purchases within the ECB’s securities markets programme

English

Abstract: We assess the yield impact of asset purchases within the ECB’s Securities Markets Programme in five euro area sovereign bond markets during 2010-11. Identification is non-trivial and based on time series panel data regression on predetermined purchases and control covariates. In addition to large and economically significant announcement effects, we find an average impact at the five year maturity per e1 bn of bond purchases of approximately -1 to -2 bps (Italy), -3 bps (Ireland), -4 to -6 bps (Spain), -6 to -9 bps (Portugal), and up to -17 to -21 bps (Greece). The impact depends on market size and a default risk signal, and is approximately -3 basis points at a five-year maturity for purchases of 1/1000 of the respective debt market. Bond yield volatility is lower on intervention days for most SMP countries, due to less extreme movements occurring when the Eurosystem is active as a buyer. A dynamic specification points to both transitory and longer-lived effects from purchases.
JEL Code: C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates

15 August 2012

WORKING PAPER SERIES - No. 1459

Dynamic factor models with macro, frailty and industry effects for US default counts: the credit crisis of 2008

English

Abstract: We develop a high-dimensional and partly nonlinear non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into a set of latent components that correspond with macroeconomic/financial, default-specific (frailty), and industry-specific effects. Discrete default counts together with macroeconomic and financial variables are modeled simultaneously in this framework. In our empirical study based on defaults of U.S. firms, we find that approximately 35 percent of default rate variation is due to systematic and industry factors. Approximately one third of systematic variation is captured by macroeconomic/financial factors. The remainder is captured by frailty (about 40 percent) and industry (about 25 percent) effects. The default-specific effects are particularly relevant before and during times of financial turbulence. For example, we detect a build-up of systematic risk over the period preceding the 2008 credit crisis.
JEL Code: C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
Network: Macroprudential Research Network

13 April 2011

WORKING PAPER SERIES - No. 1327

Systemic risk diagnostics: coincident indicators and early warning signals

English

Abstract: We propose a novel framework to assess financial system risk. Using a dynamic factor framework based on state-space methods, we construct coincident measures (
JEL Code: G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
Network: Macroprudential Research Network

External publications

2023

European Economic Review

Euro area sovereign bond risk premia before and during the Covid-19 pandemic