Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling

Bibliographic Data

ID19417464
AuthorsFulvio Corsi (0000-0003-2683-4479, University of St.Gallen), Roberto Renò (0000-0002-1470-2420, University of Siena)
Year2012
Volume30
Issue3
Pages368-380
Publication date2012-07-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2012.663261
OpenAlexW2070516830
LanguageEN
Citations received6
References cited45

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. This article has online supplementary materials

Autoregressive conditional heteroskedasticity · Econometrics · Economics · Forward volatility · Implied volatility · Jump · Leverage effect · Physics · Statistics · Stochastic volatility · Volatility risk premium · Complex Systems and Time Series Analysis · Computer Science · Financial Risk and Volatility Modeling · Mathematics · Stochastic processes and financial applications

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