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

Alexander Beck, Young Shin Aaron Kim, Svetlozar Rachev, M. Feindt, Frank Fabozzi

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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-atrisk 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-atrisk, the idiosyncratic differences in average value-at-risk are compared between the models.

Original languageEnglish
Pages (from-to)167-177
Number of pages11
JournalStudies in Nonlinear Dynamics and Econometrics
Volume17
Issue number2
DOIs
StatePublished - Apr 2013

Keywords

  • ARMA-GARCH model
  • Average value-at-risk (AVaR)
  • High-frequency.
  • Tempered stable distribution

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