Abstract

We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forecasting performance can be significantly improved by introducing a persistent leverage effect with a long-range dependence similar to that of volatility itself. We also find a strongly significant positive impact of lagged jumps on volatility, which however is absorbed more quickly. We then estimate continuous-time stochastic volatility models that are able to reproduce the statistical features captured by the discrete-time model. We show that a single-factor model driven by a fractional Brownian motion is unable to reproduce the volatility dynamics observed in the data, while a multifactor Markovian model fully replicates the persistence of both volatility and leverage effect. The impact of jumps can be associated with a common jump component in price and volatility.

Publication Type:	Article
Additional Information:	This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Business & Economic Statistics on 20 July 2012, available online: http://wwww.tandfonline.com/10.1080/07350015.2012.663261
Publisher Keywords:	Fractional Brownian motion, Jumps, Leverage effect, Multifactor models, Volatility forecasting
Subjects:	H Social Sciences > HB Economic Theory
Departments:	School of Policy & Global Affairs > Economics
Related URLs:	Author

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