Volatility forecasting for risk management

By Chris Brooks, G. Persand. Journal of Forecasting, 2003

218 citations recorded by the source corpus.

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. Copyright © 2002 John Wiley & Sons, Ltd.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/52f538af28b2fc3ec9aa352fe19446754a2ad581

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Citations in the corpus

Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood (2004, background): “Brooks and Persand (2003) evaluated volatility forecasting in a financial risk management setting in terms of value-at-risk (VaR).”
Optimally Harnessing Inter-Day and Intra-Day Information for Daily Value-at-Risk Prediction (2013, unknown): “The essence of equations (9)-(10) is to state that the optimal α-quantile combination is the linear combination of quantiles q̂1,t and q̂2,t, given by the weights (λ0, λ1, λ2), that minimizes the conditional expected ‘tick’ loss over Λ.”
Modelling and forecasting volatility in the gold market (2012, background): “Brooks and Persand (2002, 2003) examine various ARCH and GARCH type models with respect to volatility forecasting.”
Comparison of Volatility Measures: A Risk Management Perspective (2010, unknown): “…of the fact that, despite the significant improvements in the evaluation of volatility predictions (e.g. Andersen & Bollerslev (1998)), a volatility evaluation metric might fail to asses the usefulness of volatility forecasts form a risk-management point of view (Brooks & Persand (2003)).”
The Use of GARCH Models in VaR Estimation (2004, background): “Moreover, Brooks and Persand [12] state that a VaR model which does not account for asymmetries in volatility specification, is most likely to generate inaccurate forecasts.”
Forecasting Stock Market Volatility with Regime-Switching GARCH Models (2005, methodology): “This result is however not new, since also Brooks and Persand (2003) find a no clear answer for most of the series 23We should thus consider the second stage of selection only for the one-day horizon. However, for completeness, we also apply such selection to all the other horizons, selecting the be”
Value-at-Risk Prediction: A Comparison of Alternative Strategies (2005, methodology): “…. ., Ht 1}, it is straightforward to show [Christoffersen (1998: Lemma 1)] that efficiency implies HtjF t 1 iid Berð Þ, t ¼ 1,2, . . . ,T, ð25Þ 12 Because Brooks and Persand (2003) used a normality assumption for VaR prediction and only considered one MGARCH model [the diagonal VEC form from…”
Stochastic volatility forecasting and risk management (2005, background): “Recent developments in risk management and financial engineering have highlighted the use of value at risk (VaR) as a popular approach to measuring market risk on a daily basis (JP Morgan, 1996; Jorion, 1997; Alexander, 2001; Brooks and Persand, 2002, 2003; Christoffersen, 2003; Partnoy, 2003).”