Is CAViaR model really so good in Value at Risk forecasting? Evidence from evaluation of a quality of Value-at-Risk forecasts obtained based on the: GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and the historical simulation models depending on the stability of financial markets

Mateusz Buczyński and Marcin Chlebus ()

No 2017-29, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: In the literature, there is no consensus which Value-at-Risk forecasting model is the best for measuring a market risk in banks. In the study an analysis of Value-at-Risk forecasting models quality over varying economic stability periods for main indices from stock exchanges was conducted. The VaR forecasts from GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and historical simulation models in periods with contrasting volatility trends (increasing, constantly high and decreasing) for countries economically developed (the USA – S&P 500, Germany - DAX and Japan – Nikkei 225) and economically developing (China – SSE COMP, Poland – WIG20 and Turkey – XU100) were compared. The data samples used in the analysis were selected from period 01.01.1999 – 24.03.2017. To assess the VaR forecasts quality: excess ratio, Basel traffic light test, coverage tests (Kupiec test, Christoffersen test), Dynamic Quantile test, cost functions and Diebold-Marino test were used. Obtained results shows that the quality of Value-at-Risk forecasts for the models varies depending on a volatility trend. However, GARCH-st (1,1) and QML-GARCH(1,1) were found as the most robust models to the different volatility periods. The results shows, as well that the CAViaR model forecasts were less appropriate in the increasing volatility period. Moreover, no significant differences for the VaR forecasts quality were found for the developed and developing countries.

Keywords: risk management; value at risk; GARCH; CAViaR; historical simulation; quality of model assessment (search for similar items in EconPapers)
JEL-codes: C52 C53 C58 G32 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2017
New Economics Papers: this item is included in nep-ban, nep-ets, nep-for and nep-rmg
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