Range-based estimation of stochastic volatility models

• 成果类型: Article
• 署名作者: Alizadeh, S; Brandt, MW; Diebold, FX
• 署名单位: University of Pennsylvania
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/1540-6261.00454
• 发表日期: 2002
• 页码: 1047-1091
• 关键词: maximum-likelihood; RETURN VOLATILITY; asset prices; options; variance; DIFFUSIONS; DYNAMICS; MARKET; time

摘要

We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range-based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range-based Gaussian quasi-maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine the dynamics of daily exchange rate volatility and find the evidence points strongly toward two-factor models with one highly persistent factor and one quickly mean-reverting factor.