A Bayesian analysis of time-varying jump risk in S&P 500 returns and options

Andrew Carverhill and Dan Luo

Journal of Financial Markets, 2023, vol. 64, issue C

Abstract: We examine time-varying jump risk for modeling stock price dynamics and cross-sectional option prices. We explore jump-diffusion specifications with two independently evolving processes for stochastic volatility and jump intensity, respectively. We explicitly impose time-series consistency in model estimation using a Markov Chain Monte Carlo (MCMC) method. We find that both the jump size and standard deviation of jump size premia are more prominent under time-varying jump risk. Simultaneous jumps in returns and volatility help reconcile the time series of returns, volatility, and jump intensities. Finally, independent time-varying jump intensities improve the cross-sectional fit of option prices, especially at longer maturities.

Keywords: Time-varying jump risk; Markov Chain Monte Carlo; Risk premium; Time-series consistency; Option pricing (search for similar items in EconPapers)
JEL-codes: G12 G13 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finmar:v:64:y:2023:i:c:s1386418122000751

DOI: 10.1016/j.finmar.2022.100786

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