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A Stochastic Volatility Model With Realized Measures for Option Pricing

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  • Giacomo Bormetti
  • Roberto Casarin
  • Fulvio Corsi
  • Giulia Livieri

Abstract

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence—the crucial parameter in pricing Standard and Poor’s 500 Index options.

Suggested Citation

  • Giacomo Bormetti & Roberto Casarin & Fulvio Corsi & Giulia Livieri, 2020. "A Stochastic Volatility Model With Realized Measures for Option Pricing," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 856-871, October.
    • Handle: RePEc:taf:jnlbes:v:38:y:2020:i:4:p:856-871
    DOI: 10.1080/07350015.2019.1604371
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    Cited by:

    1. Ballestra, Luca Vincenzo & D’Innocenzo, Enzo & Guizzardi, Andrea, 2024. "A new bivariate approach for modeling the interaction between stock volatility and interest rate: An application to S&P500 returns and options," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1185-1194.
    2. Isabel Casas & Helena Veiga, 2021. "Exploring Option Pricing and Hedging via Volatility Asymmetry," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1015-1039, April.
    3. Luca Vincenzo Ballestra & Enzo D’Innocenzo & Andrea Guizzardi, 2024. "Score-Driven Modeling with Jumps: An Application to S&P500 Returns and Options," Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 375-406.
    4. Raanju R. Sundararajan & Wagner Barreto‐Souza, 2023. "Student‐t stochastic volatility model with composite likelihood EM‐algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(1), pages 125-147, January.
    5. Augustyniak, Maciej & Badescu, Alexandru & Bégin, Jean-François, 2023. "A discrete-time hedging framework with multiple factors and fat tails: On what matters," Journal of Econometrics, Elsevier, vol. 232(2), pages 416-444.

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