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

Milan Rippel () and Ivo Jánský
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Milan Rippel: Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic, http://ies.fsv.cuni.cz/

No 2011/27, Working Papers IES from Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies

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.

Keywords: VaR; risk analysis; conditional volatility; conditional coverage; garch; egarch; tarch; moving average process; autoregressive process (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 C58 G01 G24 (search for similar items in EconPapers)
Pages: 14
Date: 2011-07, Revised 2011-07
New Economics Papers: this item is included in nep-ets, nep-for, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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