Out-of-sample density forecasts with affine jump diffusion models

Jaeho Yun

Journal of Banking & Finance, 2014, vol. 47, issue C, 74-87

Abstract: We conduct out-of-sample density forecast evaluations of the affine jump diffusion models for the S&P 500 stock index and its options’ contracts. We also examine the time-series consistency between the model-implied spot volatilities using options & returns and only returns. In particular, we focus on the role of the time-varying jump risk premia. Particle filters are used to estimate the model-implied spot volatilities. We also propose the beta transformation approach for recursive parameter updating. Our empirical analysis shows that the inconsistencies between options & returns and only returns are resolved by the introduction of the time-varying jump risk premia. For density forecasts, the time-varying jump risk premia models dominate the other models in terms of likelihood criteria. We also find that for medium-term horizons, the beta transformation can weaken the systematic effect of misspecified AJD models using options & returns.

Keywords: Density forecasts; Time-series consistency; Affine jump diffusion; Time-varying jump risk premia; Particle filters; Beta transformation (search for similar items in EconPapers)
JEL-codes: C14 C22 C53 G13 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:47:y:2014:i:c:p:74-87

DOI: 10.1016/j.jbankfin.2014.06.024

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