Time-varying higher-order conditional moments and forecasting intraday VaR and Expected Shortfall

A. Tolga Ergün and Jongbyung Jun

The Quarterly Review of Economics and Finance, 2010, vol. 50, issue 3, 264-272

Abstract: We estimate several GARCH- and Extreme Value Theory (EVT)-based models to forecast intraday Value-at-Risk (VaR) and Expected Shortfall (ES) for S&P 500 stock index futures returns for both long and short positions. Among the GARCH-based models we consider is the so-called Autoregressive Conditional Density (ARCD) model, which allows time-variation in higher-order conditional moments. ARCD model with time-varying conditional skewness parameter has the best in-sample fit among the GARCH-based models. The EVT-based model and the GARCH-based models which take conditional skewness and kurtosis (time-varying or otherwise) into account provide accurate VaR forecasts. ARCD model with time-varying conditional skewness parameter seems to provide the most accurate ES forecasts.

Keywords: Density; estimation; Higher-order; conditional; moments; Intraday; Value-at-Risk; and; Expected; Shortfall (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (16)

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