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Forecasting volatility: does continuous time do better than discrete time?

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  • Carles Bretó

    ()

  • Helena Veiga

    ()

Abstract

In this paper we compare the forecast performance of continuous and discrete-time volatility models. In discrete time, we consider more than ten GARCH-type models and an asymmetric autoregressive stochastic volatility model. In continuous-time, a stochastic volatility model with mean reversion, volatility feedback and leverage. We estimate each model by maximum likelihood and evaluate their ability to forecast the two scales realized volatility, a nonparametric estimate of volatility based on highfrequency data that minimizes the biases present in realized volatility caused by microstructure errors. We find that volatility forecasts based on continuous-time models may outperform those of GARCH-type discrete-time models so that, besides other merits of continuous-time models, they may be used as a tool for generating reasonable volatility forecasts. However, within the stochastic volatility family, we do not find such evidence. We show that volatility feedback may have serious drawbacks in terms of forecasting and that an asymmetric disturbance distribution (possibly with heavy tails) might improve forecasting.

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Bibliographic Info

Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws112518.

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Date of creation: Jul 2011
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Handle: RePEc:cte:wsrepe:ws112518

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Related research

Keywords: Asymmetry; Continuous and discrete-time stochastic volatility models; GARCH-type models; Maximum likelihood via iterated filtering; Particle filter; Volatility forecasting;

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