An evaluation of volatility forecasting techniques

Brailsford, Tim; Faff, Robert W.

doi:10.1016/0378-4266(95)00015-1

Cited by 375 publications

(229 citation statements)

References 30 publications

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“…The choice of an appropriate criterion for volatility forecast evaluation is under active investigation (Andersen et al, 1999;Brailsford and Faff, 1996;Lopez, 2001;Taylor, 1999;among others).…”

Section: Implied Volatility and Forecast Evaluationmentioning

confidence: 99%

Forecasting volatility

Gospodinov

and

Jiang

2006

Journal of Forecasting

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In this paper, we investigate the time series properties of S&P 100 volatility and the forecasting performance of different volatility models. We consider several nonparametric and parametric volatility measures, such as implied, realized and model-based volatility, and show that these volatility processes exhibit an extremely slow mean-reverting behavior and possible long memory. For this reason, we explicitly model the near-unit root behavior of volatility and construct median unbiased forecasts by approximating the finite-sample forecast distribution using bootstrap methods. Furthermore, we produce prediction intervals for the next-period implied volatility that provide important information about the uncertainty surrounding the point forecasts. Finally, we apply intercept corrections to forecasts from misspecified models which dramatically improve the accuracy of the volatility forecasts.

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Section: Implied Volatility and Forecast Evaluationmentioning

confidence: 99%

Forecasting volatility

Gospodinov

and

Jiang

2006

Journal of Forecasting

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“…On the other hand, dozens of papers investigate whether improvements over GARCH as a benchmark are possible using non-linear models or artificial intelligence techniques (e.g. West and Cho, 1995;Brailsford and Faff, 1996;Donaldson and Kamstra, 1997;Neely and Weller, 2002). Considering the overwhelming evidence for long-term dependence in volatility (i.e.…”

Section: Introductionmentioning

confidence: 99%

Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching

Lux

Kaizoji

2007

Journal of Economic Dynamics and Control

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Abstract:We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions of up to 100 days ahead. In most respects, the long memory models (ARFIMA, FIGARCH and the recently introduced multifractal model) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have quite a number of cases with dramatic failures of their forecasts, the multifractal model does not suffer from this shortcoming and its performance practically always improves upon the naïve forecast provided by historical volatility. As a somewhat surprising result, we also find that, for FIGARCH and ARFIMA models, pooled estimates (i.e. averages of parameter estimates from a sample of time series) give much better results than individually estimated models.

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“…Journal of Business provides superior forecasts of volatility (see Brailsford and Faff 1996). This method is used extensively in the literature.…”

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confidence: 99%