Publication detail

Value at Risk forecasting with the ARMA-GARCH family of models in times of increased volatility

Type: IES Working Papers
Year: 2011
Number: 27
Published in: IES Working Papers 27/2011
Publishing place: Prague
Keywords: VaR, risk analysis, conditional volatility, conditional coverage, garch, egarch, tarch, moving average process, autoregressive process
JEL codes: C51, C52, C53, C58, G01, G24
Suggested Citation: Jánský, I., Rippel, M. (2011). “Value at Risk forecasting with the ARMA-GARCH family of models in times of increased volatility” IES Working Paper 27/2011. IES FSV. Charles University.
Abstract: The paper evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and 2009 on data from six world stock indices - DJI, GSPC, IXIC, FTSE, GDAXI and N225. The models model mean using the ARMA processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their forecasting accuracy is evaluated on the out-of-sample data, which are more volatile. The main aim of the paper is to test whether a model estimated on data with lower volatility can be used in periods with higher volatility. The evaluation is based on the conditional coverage test and is performed on each stock index separately. The primary result of the paper is that the volatility is best modelled using a GARCH process and that an ARMA process pattern cannot be found in analyzed time series.
Downloadable: WP 2011_27_Jansky_Rippel




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