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EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk


  • Chlebus Marcin

    () (Faculty of Economic Science, University of Warsaw, WarsawPoland)


In the study, the two-step EWS-GARCH models to forecast Value-at-Risk is presented. The EWS-GARCH allows different distributions of returns or Value-at-Risk forecasting models to be used in Value-at-Risk forecasting depending on a forecasted state of the financial time series. In the study EWS-GARCH with GARCH(1,1) and GARCH(1,1), with the amendment to the empirical distribution of random errors as a Value-at-Risk model in a state of tranquillity and empirical tail, exponential or Pareto distributions used to forecast Value-at-Risk in a state of turbulence were considered. The evaluation of Value-at-Risk forecasts was based on the Value-at-Risk forecasts and the analysis of loss functions. Obtained results indicate that EWS-GARCH models may improve the quality of Value-at-Risk forecasts generated using the benchmark models. However, the choice of best assumptions for the EWS-GARCH model should depend on the goals of the Value-at-Risk forecasting model. The final selection may depend on an expected level of adequacy, conservatism and costs of the model.

Suggested Citation

  • Chlebus Marcin, 2017. "EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk," Central European Economic Journal, Sciendo, vol. 3(50), pages 01-25, December.
  • Handle: RePEc:vrs:ceuecj:v:3:y:2017:i:50:p:01-25:n:1

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    References listed on IDEAS

    1. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 515-526, November.
    2. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2007. "A robust VaR model under different time periods and weighting schemes," Review of Quantitative Finance and Accounting, Springer, vol. 28(2), pages 187-201, February.
    3. Timotheos Angelidis & Alexandros Benos, 2008. "Value-at-Risk for Greek Stocks," Multinational Finance Journal, Multinational Finance Journal, vol. 12(1-2), pages 67-104, March-Jun.
    4. Ozun, Alper & Cifter, Atilla & Yilmazer, Sait, 2007. "Filtered Extreme Value Theory for Value-At-Risk Estimation," MPRA Paper 3302, University Library of Munich, Germany.
    5. 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(4), pages 436-452, March.
    6. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 75, European Central Bank.
    7. Marcin Chlebus, 2016. "One-Day Prediction of State of Turbulence for Portfolio. Models for Binary Dependent Variable," Working Papers 2016-01, Faculty of Economic Sciences, University of Warsaw.
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    Cited by:

    1. Mateusz BuczyƄski & Marcin Chlebus, 2019. "Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states," Working Papers 2019-12, Faculty of Economic Sciences, University of Warsaw.

    More about this item


    value-at-risk; state of turbulence; GARCH; tail distributions; market risk;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation


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