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Volatility forecasting for risk management

Volatility forecasting for risk management
Volatility forecasting for risk management
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 decision making.
internal risk management models, asset return volatility, value at risk models, forecasting, univariate and multivariate GARCH models
0277-6693
1-22
Brooks, Chris
2be5f663-66b8-43d2-903c-6f800e6e2385
Persand, Gita
d60c4b3f-fd3b-4b0a-892f-3c4eb992f15d
Brooks, Chris
2be5f663-66b8-43d2-903c-6f800e6e2385
Persand, Gita
d60c4b3f-fd3b-4b0a-892f-3c4eb992f15d

Brooks, Chris and Persand, Gita (2003) Volatility forecasting for risk management. Journal of Forecasting, 22 (1), 1-22. (doi:10.1002/for.841).

Record type: Article

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 decision making.

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More information

Published date: 2003
Keywords: internal risk management models, asset return volatility, value at risk models, forecasting, univariate and multivariate GARCH models

Identifiers

Local EPrints ID: 35865
URI: https://eprints.soton.ac.uk/id/eprint/35865
ISSN: 0277-6693
PURE UUID: ee4c842c-3526-46a0-a4ea-91d55fbe5fbc

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Date deposited: 23 May 2006
Last modified: 17 Jul 2017 15:46

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