Bayesian Tail Risk Forecasting using Realised GARCH

Christian Contino and Richard Gerlach

No 2014-05, Working Papers from University of Sydney Business School, Discipline of Business Analytics

Abstract: A Realised Volatility GARCH model is developed within a Bayesian framework for the purpose of forecasting Value at Risk and Conditional Value at Risk. Student-t and Skewed Student-t return distributions are combined with Gaussian and Student-t distributions in the measurement equation in a GARCH framework to forecast tail risk in eight international equity index markets over a four year period. Three Realised Volatility proxies are considered within this framework. Realised Volatility GARCH models show a marked improvement compared to ordinary GARCH for both Value at Risk and Conditional Value at Risk forecasting. This improvement is consistent across a variety of data, volatility model speci_cations and distributions, and demonstrates that Realised Volatility is superior when producing volatility forecasts. Realised Volatility models implementing a Skewed Student-t distribution for returns in the GARCH equation are favoured.

Keywords: Realised Volatility; Value-at-Risk; CVaR; High-Frequency Data; Expected Shortfall; Risk Management; GARCH (search for similar items in EconPapers)
Date: 2014-10-10
New Economics Papers: this item is included in nep-ets, nep-for and nep-rmg
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Citations: View citations in EconPapers (1)

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