Estimating and testing skewness in a stochastic volatility model

Cheol Woo Lee, Kyu Ho Kang

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we propose a novel approach to estimating and testing skewness in a stochastic volatility (SV) model. Our key idea is to replace a normal return error in the standard SV model with a split normal error. We show that this simple variation in the model brings about two large computational advantages. First, the stochastic volatility process can be simulated fast and efficiently using a one-block Gibbs sampling technique. Second, more importantly, this is the first to provide a marginal likelihood calculation method to formally test the coexistence of stochastic volatility and skewness in return errors within a Bayesian framework. We demonstrate the efficiency and reliability of our posterior sampling and model comparison methods through a simulation study. The simulation results show that neglecting skewness leads to inaccurate estimates on both the volatility process and conditional expected returns. Our empirical applications to daily stock return data provide a strong evidence of negative skewness.

Original languageEnglish
Pages (from-to)445-467
Number of pages23
JournalJournal of Empirical Finance
Volume72
DOIs
Publication statusPublished - 2023 Jun

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea funded by the Ministry of Science and ICT ( NRF-2022M3J6A1063595 ) and the Korea University Research Grant ( K2200821 ).

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Gibbs sampling
  • Heavy tail
  • Marginal likelihood
  • Split normal error

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

Fingerprint

Dive into the research topics of 'Estimating and testing skewness in a stochastic volatility model'. Together they form a unique fingerprint.

Cite this