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Publications in refereed journals

[11] Bank business models at zero interest rates, joint with Andre Lucas and Julia Schaumburg.
(JBES download) (ECB wp) (Web Appendix)
Journal of Business and Economic Statistics, forthcoming.
Different types of banks respond differently to falling long-term nominal interest rates.
Our business model classification results are available here

[10] Do negative interest rates make banks less safe?, joint with Federico Nucera, Andre Lucas, and Julia Schaumburg.
(EL download) (ECB wp)
Economics Letters, Volume 159, Oct 2017, Pages 112-115.
The financial stability impact of negative central bank policy rates depends on banks' business models.
Policy rate cuts below zero trigger different risk responses than a cut to zero.

[9] Global credit risk: World, country, and industry factors, joint with Siem Jan Koopman and Andre Lucas.
(JAE download) (ECB wp) (online appendix)
Journal of Applied Econometrics, Volume 32, Issue 2, Mar 2017, p. 296–317.
A global default risk map. Reports time-varying pd's and risk drivers for >20k firms in 41 countries over 35 years.
Credit risk deviations from macro-implied levels are informative from a financial stability surveillance perspective.

[8] Modeling financial sector joint tail risk in the euro area, joint with Andre Lucas and Xin Zhang.
(JAE download) (ECB wp) (online appendix)
Journal of Applied Econometrics, Volume 32, Issue 1, Jan/Feb 2017, p. 171–191.
A score-driven block-equicorrelation copula model to quickly and reliably infer joint and conditional tail risks
from high-dimensional financial data. Used e.g. in the ECB's May 2017 Financial Stability Review, p. 86-88.

[7] The information in systemic risk rankings, joint with Federico Nucera, Siem Jan Koopman and Andre Lucas.
(JEF download) (ECB wp)
Journal of Empirical Finance, Sep 2016, Vol. 38, p. 461–475.
A principal components-based methodology to combine alternative systemic risk rankings, reducing model risk.
Summarized in the ECB's Nov 2015 Financial Stability Review, p. 80-82.

[6] Evaluating the impact of unconventional monetary policy measures:
Empirical evidence from the ECB's Securities Markets Programme,
Journal of Financial Economics, joint with Fabian Eser, Jan 2016, Vol. 119 (1), p. 147–167.
(JFE download) (a recent version). An earlier ECB working paper (under a different title) is here.
This study is referred to in a WSJ blog, private sector research letters, the ECB Monthly Bulletin, and the ECB Research Bulletin.

[5] Observation driven mixed-measurement dynamic factor models with an application to credit risk,
The Review of Economics and Statistics, joint with Drew Creal, Siem Jan Koopman, and Andre Lucas, Dec 2014, Vol. 96 (5), p. 898–915.
(REStat download) (online appendix) (a recent version)
Introduces observation-driven mixed-measurement dynamic factor models in a credit risk context.
Computer code is here.

[4] Conditional euro area sovereign default risk,
Journal of Business and Economic Statistics, joint with Andre Lucas and Xin Zhang, Vol 32 (2), 2014, p. 271-284.
(JBES download) (a recent version)
The paper is referred to in a WSJ article, the ECB Monthly Bulletin, the ECB Research Bulletin.
Figure 4 from this paper is updated quarterly for the ESRB risk dashboard.

[3] Nowcasting and forecasting global financial sector stress and credit market dislocation,
International Journal of Forecasting, joint with Siem Jan Koopman and Andre Lucas, Vol 30 (3), 2014, p. 741-758.
(IJF download) (online appendix) (a recent version)
A framework for nowcasts and out-of-sample forecasts of financial sector aggregate pd's.
Figure 7 on risk deviations is updated quarterly for financial stability monitoring purposes.

[2] Dynamic factor models with macro, frailty, and industry effects for U.S. default counts: the credit crisis of 2008,
Journal of Business and Economic Statistics, joint with Siem Jan Koopman and Andre Lucas, Vol 30 (4), Dec 2012, p. 521-532.
(JBES download) (a recent version) (Web Appendix)
Introduces parameter-driven mixed-measurement dynamic factor models.
Provides a variance decomposition of non-Gaussian default data. "Job market" paper.

[1] Modeling frailty-correlated default using many macroeconomic covariates,
Journal of Econometrics, joint with Siem Jan Koopman and Andre Lucas, Volume 162 (2), June 2011, p. 312-325.
(JoE download) (a recent version)
Considers the risk measurement and out-of-sample forecasting of U.S. corporate default counts.
Best is to add a single latent (frailty) factor to multiple principal components from macro data.

[0] Credit Risk and State Space Methods
(My Tinbergen Institute 2011 PhD thesis)
.. received a "eervolle vermelding" from the Royal Dutch Academy of the Sciencies as a runner-up for the
2015 "Christiaan Huygens" dissertation award in Economics/Econometrics/Actuarial Sciences.
Nominated earlier for the 2014 "KVS Penning".

Citations

Google scholar link.

RePEc link.

ECB working papers

... are (here)

Selected contributions to central bank publications

"New Quantitative Measures of Systemic Risk," ECB Financial Stability Review, Special Feature E, Dec 2010. (SF E)

"Systemic Risk Methodologies," ECB Financial Stability Review, Special Feature C, June 2011. (SF C)

"New Methodologies for Systemic Risk Measurement," ECB Research Bulletin 12, Spring 2011. (ECB RB)

"Conditional probabilities and contagion measures for euro area sovereign default risk," ECB Research Bulletin 17, Spring 2013. (ECB RB)

Summary "Fourth ECB workshop on non-standard monetary policy measures," ECB Research Bulletin 19, Fall 2013. (ECB RB) (conf link)

Discussion of "Bank funding and financial stability," by P. Gai, A. Haldane, S. Kapadia, and B. Nelson, Reserve Bank of Australia. (2013 RBA conference volume)

Joint article "The determinants of euro area sovereign bond yield spreads during the crisis," ECB Monthly Bulletin May 2014.

"The information in systemic risk rankings," ECB Financial Stability Review, Nov 2015, p. 80-82. (FSR).

"A comparison of market-based indicators of banking system stress," ECB Financial Stability Review, May 2017, p. 84-85. (FSR).