Volatility forecasting for risk management

Chris Brooks, Gitanjali Persand

Research output: Contribution to journalArticle (Academic Journal)peer-review

102 Citations (Scopus)

Abstract

Recent research has suggested that forecast evaluation on the basis of standard statistical loss functions could prefer models which are sub-optimal when used in a practical setting. This paper explores a number of statistical models for predicting the daily volatility of several key UK financial time series. The out-of-sample forecasting performance of various linear and GARCH-type models of volatility are compared with forecasts derived from a multivariate approach. The forecasts are evaluated using traditional metrics, such as mean squared error, and also by how adequately they perform in a modern risk management setting. We find that the relative accuracies of the various methods are highly sensitive to the measure used to evaluate them. Such results have implications for any econometric time series forecasts which are subsequently employed in financial decisionmaking.
Original languageEnglish
Pages (from-to)1-22
Number of pages22
JournalJournal of Forecasting
Volume22
Issue number1
DOIs
Publication statusPublished - 1 Jan 2003

Keywords

  • internal risk management models
  • asset return volatility
  • Value at Risk models
  • forecasting
  • univariate and multivariate GARCH models

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