Impact of volatility jumps in a mean-reverting model: Derivative pricing and empirical evidence

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Abstract

This paper investigates the critical role of volatility jumps under mean reversion models. Based on the empirical tests conducted on the historical prices of commodities, we demonstrate that allowing for the presence of jumps in volatility in addition to price jumps is a crucial factor when confronting non-Gaussian return distributions. By employing the particle filtering method, a comparison of results drawn among several mean-reverting models suggests that incorporating volatility jumps ensures an improved fit to the data. We infer further empirical evidence for the existence of volatility jumps from the possible paths of filtered state variables. Our numerical results indicate that volatility jumps significantly affect the level and shape of implied volatility smiles. Finally, we consider the pricing of options under the mean reversion model, where the underlying asset price and its volatility both have jump components.

Original languageEnglish
Article number101112
JournalNorth American Journal of Economics and Finance
Volume52
DOIs
StatePublished - Apr 2020

Keywords

  • Implied volatility smiles
  • Mean reversion
  • Particle filtering
  • Stochastic volatility
  • Volatility jumps

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