Asymptotic expansion for Barndorff-Nielsen and Shephard's stochastic volatility model

Hiroki Masuda, Nakahiro Yoshida

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

With the help of a general methodology of asymptotic expansions for mixing processes, we obtain the Edgeworth expansion for log-returns of a stock price process in Barndorff-Nielsen and Shephard's stochastic volatility model, in which the latent volatility process is described by a stationary non-Gaussian Ornstein - Uhlenbeck process (OU process) with invariant selfdecomposable distribution on ℝ+. The present result enables us to simultaneously explain non-Gaussianity for short time-lags as well as approximate Gaussianity for long time-lags. The Malliavin calculus formulated by Bichteler, Gravereaux and Jacod for processes with jumps and the exponential mixing property of the OU process play substantial roles in order to ensure a conditional type Cramér condition under a certain truncation. Owing to several inherent properties of OU processes, the regularity conditions for the expansions can be verified without any difficulty, and the coefficients of the expansions up to any order can be explicitly computed.

Original languageEnglish
Pages (from-to)1167-1186
Number of pages20
JournalStochastic Processes and their Applications
Volume115
Issue number7
DOIs
Publication statusPublished - Jul 2005

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Asymptotic expansion for Barndorff-Nielsen and Shephard's stochastic volatility model'. Together they form a unique fingerprint.

Cite this