Managing extreme risks in tranquil and volatile markets using conditional extreme value theory
AbstractFinancial risk management typically deals with low probability events in the tails of asset price distributions. In order to capture the behavior of these tails, one should therefore rely on models that explicitly focus on the tails. Extreme value theory (EVT) based models do exactly that, and in this paper we apply both unconditional and conditional EVT models to the management of extreme market risks in stock markets. We find conditional EVT models to give particularly accurate Value-at-Risk measures, and a comparison with traditional (GARCH) approaches to calculate Value-at-Risk demonstrates EVT as being the superior approach both for standard and more extreme Value-at-Risk quantiles.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal International Review of Financial Analysis.
Volume (Year): 13 (2004)
Issue (Month): 2 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/inca/620166
Other versions of this item:
- Byström, Hans, 2001. "Managing Extreme Risks in Tranquil and Volatile Markets Using Conditional Extreme Value Theory," Working Papers 2001:18, Lund University, Department of Economics.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G19 - Financial Economics - - General Financial Markets - - - Other
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Baillie, Richard T. & DeGennaro, Ramon P., 1990.
"Stock Returns and Volatility,"
Journal of Financial and Quantitative Analysis,
Cambridge University Press, vol. 25(02), pages 203-214, June.
- Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998.
"Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management,"
Center for Financial Institutions Working Papers
98-10, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-081, New York University, Leonard N. Stern School of Business-.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Evis Këllezi & Manfred Gilli, 2000. "Extreme Value Theory for Tail-Related Risk Measures," FAME Research Paper Series rp18, International Center for Financial Asset Management and Engineering.
- McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
- Poon, Ser-Huang & Taylor, Stephen J., 1992. "Stock returns and volatility: An empirical study of the UK stock market," Journal of Banking & Finance, Elsevier, vol. 16(1), pages 37-59, February.
- Michael McAleer & Paulo Araújo Santos & Juan-Ángel Jiménez-Martín & Teodosio Pérez Amaral, 2011.
"GFC-Robust Risk Management Under the Basel Accord Using Extreme Value Methodologies,"
KIER Working Papers
782, Kyoto University, Institute of Economic Research.
- Juan-Angel Jimenez-Martin & Michael McAleer & Teodosio Perez Amaral & Paulo Araujo Santos, 2013. "GFC-Robust Risk Management under the Basel Accord using Extreme Value Methodologies," Tinbergen Institute Discussion Papers 13-070/III, Tinbergen Institute.
- Paulo Araújo Santos & Juan-Ángel Jiménez-Martín & Michael McAleer & Teodosio Pérez Amaral, 2011. "GFC-Robust Risk Management Under the Basel Accord Using Extreme Value Methodologies," Documentos del Instituto Complutense de AnÃ¡lisis EconÃ³mico 2011-27, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
- Paulo Araújo Santos & Juan-Ángel Jiménez-Martín & Michael McAleer & Teodosio Pérez Amaral, 2011. "GFC-Robust Risk Management Under the Basel Accord Using Extreme Value Methodologies," Working Papers in Economics 11/28, University of Canterbury, Department of Economics and Finance.
- Santos, P.A. & Jimenez-Martin, J-A. & McAleer, M.J. & Perez-Amaral, T., 2011. "GFC-Robust Risk Management Under the Basel Accord Using Extreme Value Methodologies," Econometric Institute Research Papers EI2011-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Chia-Lin Chang & Lydia González-Serrano & Juan-Ángel Jiménez-Martín, 2012.
"Currency Hedging Strategies Using Dynamic Multivariate GARCH,"
Documentos del Instituto Complutense de AnÃ¡lisis EconÃ³mico
2012-07, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, revised Feb 2012.
- Chia-Lin Chang & Lydia González-Serrano & Juan-Ángel Jiménez-Martín, 2011. "Currency Hedging Strategies Using Dynamic Multivariate GARCH," Documentos del Instituto Complutense de AnÃ¡lisis EconÃ³mico 2011-33, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
- Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
- Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
- Brännäs, Kurt & Quoreshi, Shahiduzzaman & Simonsen, Ola, 2002. "Extreme-Value Characteristics in Daily Time Series of Swedish Stock Returns," UmeÃ¥ Economic Studies 597, Umeå University, Department of Economics.
- Stavros Degiannakis & Christos Floros & Alexandra Livada, 2012. "Evaluating value-at-risk models before and after the financial crisis of 2008: International evidence," Managerial Finance, Emerald Group Publishing, vol. 38(3), pages 436-452, March.
- Marimoutou, Velayoudoum & Raggad, Bechir & Trabelsi, Abdelwahed, 2009. "Extreme Value Theory and Value at Risk: Application to oil market," Energy Economics, Elsevier, vol. 31(4), pages 519-530, July.
- Bertrand B. Maillet & Jean-Philippe R. Médecin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
- Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, vol. 22(2), pages 283-300.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.