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  • 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.
  • 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. (en)
Title
  • Value at Risk forecasting with the ARMA-GARCH family of models in times of increased volatility
  • Value at Risk forecasting with the ARMA-GARCH family of models in times of increased volatility (en)
skos:prefLabel
  • Value at Risk forecasting with the ARMA-GARCH family of models in times of increased volatility
  • Value at Risk forecasting with the ARMA-GARCH family of models in times of increased volatility (en)
skos:notation
  • RIV/00216208:11230/11:10098800!RIV12-GA0-11230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GAP403/10/1235), S, Z(MSM0021620841)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
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  • 237888
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  • RIV/00216208:11230/11:10098800
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • autoregressive process; moving average process; tarch; egarch; garch; conditional coverage; conditional volatility; risk analysis; VaR (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [E79C4D210EAF]
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Rippel, Milan
  • Jánský, Ivo
http://linked.open...n/vavai/riv/zamer
http://localhost/t...ganizacniJednotka
  • 11230
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