Size does matter. A study on the required window size for optimal quality market risk models

Mateusz Buczyński and Marcin Chlebus ()

No 2020-09, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: When it comes to market risk models, should we use full data that we possess or rather find a sufficient subsample? We have conducted a study of different fixed moving window’s lengths (from 300 to 2000 observations) for three Value-at-Risk models: historical simulation, GARCH and CAViaR model for three different indexes: WIG20, S&P500 and FTSE100. Testing samples contained 250 observations, each ending with the end of years 2015-2019. We have also addressed the subjectivity of choosing the window’s size by testing change points detection algorithms: binary segmentation and Pelt; to find the best matching cut-off point. Results indicate that the size of the training sample greater than 900-1000 observations doesn’t increase the quality of the model, while the lengths lower than such cut-off provide unsatisfactory results and decrease model’s conservatism. Change point detection methods provide more accurate models. Applying the algorithms with every model’s recalculation provides results better by on average 1 exceedance. Our recommendation is to use GARCH or CAViaR model with recalculated window size.

Keywords: Value at Risk; historical simulation; CAViaR; GARCH; forecast comparison; sample size (search for similar items in EconPapers)
JEL-codes: C52 C53 C58 G32 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2020
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2020-09

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