Marcin Chlebus

(English) PDF


In the study, two-step EWS-GARCH models to forecast Value-at-Risk are analysed. The following models were considered: the EWS-GARCH models with lognormal, Weibull or Gamma distributions as a distributions in a state of turbulence, and with GARCH(1,1) or GARCH(1,1) with the amendment to empirical distribution of random error models as models used in a state of tranquillity.
The evaluation of the quality of the Value-at-Risk forecasts was based on the Value-at-Risk forecasts adequacy (the excess ratio, the Kupiec test, the Christoffersen test, the asymptotic test of unconditional coverage and the backtesting criteria defined by the Basel Committee) and the analysis of loss functions (the Lopez quadratic loss function, the Abad & Benito absolute loss function, the 3rd version of Caporin loss function and the function of excessive costs). Obtained results show that the EWSGARCH models with lognormal, Weibull or Gamma distributions may compete with EWS-GARCH models with exponential and empirical distributions. The EWS-GARCH model with lognormal, Weibull or Gamma distributions are relatively less conservative, but using them is less expensive than using the other EWS-GARCH models.


Value-at-Risk, GARCH models, regime switching, forecasting, market risk


Abad P., Benito S., (2013), A Detailed Comparison Of Value At Risk Estimates, Mathematics and Computers in Simulation, 94, 258–276.

Abad P., Benito S., López, C., (2014), A Comprehensive Review Of Value At Risk Methodologies, The Spanish Review of Financial Economics, 12 (1), 15–32.

Alexander C., Lazar E., (2006), Normal Mixture GARCH(1,1): Applications To Exchange Rate Modelling, Journal of Applied Econometrics, 21, 307–336.

Alexander C., (2008), Market Risk Analysis, John Wiley & Sons, Chichester.

Allison P., (2005), Logistic Regression Using SAS: Theory And Application, John Wiley & Sons, Cary, NC: SAS Inst.

Angelidis T., Benos A., Degiannakis S., (2007), A Robust Var Model Under Different Time Periods And Weighting Schemes, Review of Quantitative Finance and Accounting, 28 (2), 187–201.

Basel Committee on Banking Supervision, (2006), International Convergence of Capital Measurement and Capital Standards. Revised framework. Comprehensive Version., Online access at 16 November 2013.

Basel Committee on Banking Supervision, (2011), Revisions to the Basel II market risk framework – updated as of 31 December 2010, Online access at 16 November 2013.

Bollerslev T., (1986), Generalized Autoregressive Conditional Heteroscedasticity, Journal of Econometrics, 31, 307–327.

Brownlees C. T., Engle R. F., Kelly B. T., (2011), A Practical Guide To Volatility Forecasting Through Calm And Storm, SSRN Electronic Journal SSRN Journal, Online access at 16 November 2013.

Cai J., (1994), A Markov Model Of Switching-Regime ARCH, Journal of Business Economic Statistics, 12(3), 309–316.

Caporin M., (2008), Evaluating Value-At-Risk Measures In The Presence Of Long Memory Conditional Volatility, Journal of Risk, 10, 79–110.

Chan F., Mcaleer M., (2002), Maximum Likelihood Estimation Of STAR and STAR-GARCH Models: Theory And Monte Carlo Evidence, 17 (5), 509–534.

Chlebus M., (2016a), One-Day Prediction Of State Of Journal Of Applied Econometrics Turbulence For Portfolio. Models For Binary Dependent Variable, Working Papers 2016-01, Faculty of Economic Sciences, University of Warsaw.

Chlebus M., (2016b), EWS-GARCH: New Regime Switching Approach To Forecast Value-At-Risk, Working Papers 2016-06, Faculty of Economic Sciences, University of Warsaw.

Degiannakis S., Floros C., Livada A., (2012), Evaluating Value-At-Risk Models Before And After The Financial Crisis Of 2008, Managerial Finance, 38 (4), 436–452.

Dimitrakopoulos D. N., Kavussanos M. G., Spyrou S. I., (2010), Value At Risk Models For Volatile Emerging Markets Equity Portfolios, The Quarterly Review of Economics and Finance, 50 (4), 515–526.

Engle R., (2001), GARCH 101: The Use Of ARCH/GARCH Models In Applied Econometrics, Journal of Economic Perspectives, 15 (4), 157–168.

Engle R., (2004), Risk And Volatility: Econometric Models And Financial Practice, American Economic Review, 94 (3), 405–420.

Engle R., Manganelli S., (1999), Caviar: Conditional Autoregressive Value At Risk By Regression Quantiles, NBER Working Paper 7341.

Engle R., Manganelli S., (2001), Value-At-Risk Models In Finance, Frankfurt am Main 2001. On Line (available at Social Science Research Network). Online access at 16 November 2013.

Gray S. F., (1996), Modeling The Conditional Distribution Of Interest Rates As A Regime-Switching Process, Journal of Financial Economics, 42 (1), 27–62.

Hamilton J. D., Susmel R., (1994), Autoregressive Conditional Heteroskedasticity And Changes In Regime, Journal of Econometrics, 64 (1–2), 307–333.

Lopez J. A., (1999), Methods for Evaluating Value-at-Risk Estimates, Federal Reserve Bank of San Francisco Economic Review, 2, 3–17.

Marcucci J., (2005), Forecasting Stock Market Volatility With Regime-Switching GARCH Model, Studies in Nonlinear Dynamics & Econometrics, 9 (4), 1–55.

McAleer M., Jiménez-Martin J., Amaral T. P., (2009), Has The Basel II Accord Encouraged Risk Management During The 2008-09 Financial Crisis?, SSRN Electronic Journal SSRN Journal, Online access at 16 November 2013.

Nelson D., (1991), Conditional Heteroscedasticity In Asset Returns: A New Approach, Econometrica, 59, 347–370.

Ozun A., Cifter A., Yilmazer S., (2010), Filtered Extreme-Value Theory For Value-At-Risk Estimation: Evidence From Turkey, The Journal of Risk Finance, 11 (2), 164–179.

Panjer H. H., (2006), Operational Risk: Modeling Analytics, Wiley Interscience, New York.

Back to top
© 2019–2022 Copyright by Statistics Poland, some rights reserved. Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) Creative Commons — Attribution-ShareAlike 4.0 International — CC BY-SA 4.0