Bayesian range-based estimation of stochastic volatility models

Published

Journal Article

Alizadeh, Brandt, and Diebold [2002. Journal of Finance 57, 1047-1091] propose estimating stochastic volatility models by quasi-maximum likelihood using data on the daily range of the log asset price process. We suggest a related Bayesian procedure that delivers exact likelihood based inferences. Our approach also incorporates data on the daily return and accommodates a nonzero drift. We illustrate through a Monte Carlo experiment that quasi-maximum likelihood using range data alone is remarkably close to exact likelihood based inferences using both range and return data. © 2005 Elsevier Inc. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Brandt, MW; Jones, CS

Published Date

  • December 1, 2005

Published In

Volume / Issue

  • 2 / 4

Start / End Page

  • 201 - 209

International Standard Serial Number (ISSN)

  • 1544-6123

Digital Object Identifier (DOI)

  • 10.1016/j.frl.2005.09.001

Citation Source

  • Scopus