Refining value-at-risk estimates using a Bayesian markov-switching gjr-garch copula-evt model

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Abstract

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student’s-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis.

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Sampid, M. G., Hasim, H. M., & Dai, H. (2018). Refining value-at-risk estimates using a Bayesian markov-switching gjr-garch copula-evt model. PLoS ONE, 13(6). https://doi.org/10.1371/journal.pone.0198753

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