Time-varying higher-order conditional moments and forecasting intraday VaR and Expected Shortfall
AbstractWe 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.
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Bibliographic InfoArticle provided by Elsevier in its journal The Quarterly Review of Economics and Finance.
Volume (Year): 50 (2010)
Issue (Month): 3 (August)
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Web page: http://www.elsevier.com/locate/inca/620167
Density estimation Higher-order conditional moments Intraday Value-at-Risk and Expected Shortfall;
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