Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model.

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ID: 69874
2018
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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.
Reference Key
sampid2018refiningplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sampid, Marius Galabe;Hasim, Haslifah M;Dai, Hongsheng;
Journal PloS one
Year 2018
DOI
10.1371/journal.pone.0198753
URL
Keywords
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