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

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  • Marcin Chlebus

    () (Faculty of Economic Sciences, University of Warsaw)

Abstract

In the study a proposal of two-step EWS-GARCH models to forecast Value-at-Risk is presented. The EWS-GARCH allows different distributions of returns 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 returns 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 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 back-testing 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 proposed in the study the function of excessive costs). Obtained results indicate that the EWS-GARCH models may improve the quality of the Value-at-Risk forecasts generated using benchmark models. However, the choice of best assumptions for an 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 a model.

Suggested Citation

  • Marcin Chlebus, 2016. "EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk," Working Papers 2016-06, Faculty of Economic Sciences, University of Warsaw.
  • Handle: RePEc:war:wpaper:2016-06
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    File URL: http://www.wne.uw.edu.pl/index.php/download_file/2440/
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    References listed on IDEAS

    as
    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 Buczyński & 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

    Keywords

    Value-at-Risk; GARCH; forecasting; state of turbulence; regime switching; risk management; risk measure; market risk.;

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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