Empirical analysis of ARMA-GARCH models in market risk estimation on high-frequency US data

Beck Alexander (), Kim Young Shin Aaron, Rachev Svetlozar, Feindt Michael and Frank Fabozzi ()
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Beck Alexander: Karlsruhe Institute of Technology, Karlsruher Str 88, 76139 Karlsruhe, Germany
Kim Young Shin Aaron: KIT, Germany
Rachev Svetlozar: University of Karlsruhe
Feindt Michael: Karlsruhe Insitute of Technology and Phi-T

Studies in Nonlinear Dynamics & Econometrics, 2013, vol. 17, issue 2, 167-177

Abstract: In this paper, we examine the S&P 500 index log-returns on short intraday time scales with three different ARMA-GARCH models. In order to forecast market risk, we describe the innovation process with tempered stable distributions which we compare to commonly used methods in financial modeling. Value-at-risk backtests are provided where we find that models based on the tempered stable innovation assumption significantly outperform traditional models in forecasting risk on short time-scales. In addition to value-at-risk, the idiosyncratic differences in average value-at-risk are compared between the models.

Keywords: tempered stable distribution; ARMA-GARCH model; average value-at-risk (AVaR); high-frequency (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (6)

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DOI: 10.1515/snde-2012-0033

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