Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/83376
Authors: 
Jánský, Ivo
Rippel, Milan
Year of Publication: 
2011
Series/Report no.: 
IES Working Paper 27/2011
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.
Subjects: 
VaR
risk analysis
conditional volatility
conditional coverage
garch
egarch
tarch
moving average process
autoregressive process
JEL: 
C51
C52
C53
C58
G01
G24
Document Type: 
Working Paper
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