Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-27T14:03:01.677Z Has data issue: false hasContentIssue false

OPTIMIZING POLICYMAKERS’ LOSS FUNCTIONS IN CRISIS PREDICTION: BEFORE, WITHIN OR AFTER?

Published online by Cambridge University Press:  21 February 2019

Peter Sarlin
Affiliation:
RiskLab at Arcada and Hanken School of Economics, and Silo.AI
Gregor von Schweinitz*
Affiliation:
Halle Institute for Economic Research (IWH) and University of Leipzig
*
Address correspondence to: Gregor von Schweinitz, Department of Macroeconomics, Halle Institute for Economic Research, Kleine Märkerstr. 8, 06108 Halle (Saale), Germany. e-mail: [email protected]. Phone: +49 345 7753 744.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.

Type
Articles
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© Cambridge University Press 2019

Footnotes

Research of Gregor von Schweinitz was partly funded by the European Regional Development Fund through the programme “Investing in your Future” and by the IWH Speed Project 2014/02. Parts of this work have been completed at the Financial Stability Surveillance Division of the ECB DG Macroprudential Policy and Financial Stability. The authors are grateful for the suggestions of two anonymous referees, useful comments from Bernd Amann, Carsten Detken, Makram El-Shagi, Jan-Hannes Lang, Tuomas Peltonen, and Peter Welz, and discussion at the following seminars and conferences: Third HenU-INFER Workshop on Applied Macroeconomics, IWH Economic Research Seminar, Goethe University Brown Bag Seminar, ECB Financial Stability Seminar, Deutsche Bundesbank Early-Warning Modeling Seminar and the 2015 CEUS Workshop. An online appendix to this paper as well as replication material are supplied at https://risklab.fi/publications/thresholdoptimization.

References

Alessi, L. and Detken, C. (2011) Quasi real time early warning indicators for costly asset price boom/bust cycles: A role for global liquidity. European Journal of Political Economy 27(3), 520533.CrossRefGoogle Scholar
Berg, A. and Pattillo, C. (1999) What caused the Asian crises: An early warning system approach. Economic Notes 28(3), 285334.CrossRefGoogle Scholar
Betz, F., Opricŭ, S., Peltonen, T. A. and Sarlin, P. (2014) Predicting distress in European banks. Journal of Banking & Finance 45, 225241.CrossRefGoogle Scholar
Bussière, M. and Fratzscher, M. (2006) Towards a new early warning system of financial crises. Journal of International Money and Finance 25(6), 953973.CrossRefGoogle Scholar
Bussière, M. and Fratzscher, M. (2008) Low probability, high impact: Policy making and extreme events. Journal of Policy Modeling 30(1), 111121.CrossRefGoogle Scholar
Chawla, N., Japkowicz, N. and Kotcz, A. (2004) Editorial: Special issue on learning from imbalanced data sets. SIGKDD Explorations 6(1), 16.CrossRefGoogle Scholar
Davidson, R. and MacKinnon, J. G. (2000) Bootstrap tests: How many bootstraps? Econometric Reviews 19(1), 5568.CrossRefGoogle Scholar
Davis, E. P. and Karim, D. (2008) Comparing early warning systems for banking crises. Journal of Financial Stability 4(2), 89120.CrossRefGoogle Scholar
Demirgüç-Kunt, A. and Detragiache, E. (2000) Monitoring banking sector fragility: A multivariate logit approach. The World Bank Economic Review 14(2), 287307.CrossRefGoogle Scholar
Drehmann, M. and Juselius, M. (2014) Evaluating early warning indicators of banking crises: Satisfying policy requirements. International Journal of Forecasting 30(3), 759780.CrossRefGoogle Scholar
El-Shagi, M., Knedlik, T. and von Schweinitz, G. (2013) Predicting financial crises: The (statistical) significance of the signals approach. Journal of International Money and Finance 35, 76103.CrossRefGoogle Scholar
Frankel, J. A. and Rose, A. K. (1996) Currency crashes in emerging markets: An empirical treatment. Journal of International Economics 41(3), 351366.CrossRefGoogle Scholar
Fuertes, A.-M. andKalotychou, E. (2007) Optimal design of early warning systems for sovereign debt crises. International Journal of Forecasting 23(1), 85100.CrossRefGoogle Scholar
Herndon, T., Ash, M. and Pollin, R. (2014) Does high public debt consistently stifle economic growth? A critique of Reinhart and Rogoff. Cambridge Journal of Economics 38(2), 257279.CrossRefGoogle Scholar
Holopainen, M. and Sarlin, P. (2015) Toward Robust Early-Warning Models: A Horse Race, Ensembles and Model Uncertainty. Bank of Finland Discussion Paper 06/2015.Google Scholar
Kaminsky, G. L. and Reinhart, C. M. (1999) The twin crises: The causes of banking and balance-of-payments problems. American Economic Review 89(3), 473500.CrossRefGoogle Scholar
King, G. and Zeng, L. (2001) Logistic regression in rare events data. Political Analysis 9(2), 137163.CrossRefGoogle Scholar
Knedlik, T. and von Schweinitz, G. (2012) Macroeconomic imbalances as indicators for debt crises in Europe. JCMS: Journal of Common Market Studies 50(5), 726745.Google Scholar
Kumar, M., Moorthy, U. and Perraudin, W. (2003) Predicting emerging market currency crashes. Journal of Empirical Finance 10(4), 427454.CrossRefGoogle Scholar
Lo Duca, M. and Peltonen, T. A. (2013) Assessing systemic risks and predicting systemic events. Journal of Banking & Finance 37(7), 21832195.CrossRefGoogle Scholar
Maalouf, M. and Siddiqi, M. (2014) Weighted logistic regression for large-scale imbalanced and rare events data. Knowledge-Based Systems 59, 142148.CrossRefGoogle Scholar
Manski, C. F. and Lerman, S. R. (1977) The estimation of choice probabilities from choice based samples. Econometrica 45(8), 19771988.CrossRefGoogle Scholar
Oommen, T., Baise, L. G. and Vogel, R. M. (2011) Sampling bias and class imbalance in maximum-likelihood logistic regression. Mathematical Geosciences 43(1), 99120.CrossRefGoogle Scholar
Prentice, R. L. and Pyke, R. (1979) Logistic disease incidence models and case-control studies. Biometrika 66(3), 403411.CrossRefGoogle Scholar
Riccetti, L., Russo, A. and Gallegati, M. (2018) Financial regulation and endogenous macroeconomic crises. Macroeconomic Dynamics 22(4), 896930.CrossRefGoogle Scholar
Sarlin, P. (2013) On policymakers’ loss functions and the evaluation of early warning systems. Economics Letters 119(1), 17.CrossRefGoogle Scholar
Savage, L. J. (1951) The theory of statistical decision. Journal of the American Statistical Association 46(253), 5567.CrossRefGoogle Scholar
Wald, A. (1950) Statistical Decision Functions. New York: Wiley.Google Scholar
Wilks, D. S. (2011) Statistical Methods in the Atmospheric Sciences, 3rd ed. International Geophysics Series, Vol. 100. Academic Press.Google Scholar