Journal Article

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

Renò, Roberto, Corsi, Fulvio

2012

Overview

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.

• Publisher: Taylor & Francis Group,American Statistical Association,Taylor & Francis Ltd
• Subject: Economic forecasting models / Economic models / Economic statistics / Financial leverage / Forecasting models / Forecasting techniques / Fractional Brownian motion / Jumps / Leverage / Leverage effect / Markov analysis / Modeling / Multifactor models / Parametric models / Price volatility / Statistical forecasts / Stochastic models / Studies / Volatility / Volatility forecasting