Range-based estimation of stochastic volatility models

被引:651
作者
Alizadeh, S [1 ]
Brandt, MW [1 ]
Diebold, FX [1 ]
机构
[1] Univ Penn, Philadelphia, PA 19104 USA
关键词
D O I
10.1111/1540-6261.00454
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
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.
引用
收藏
页码:1047 / 1091
页数:45
相关论文
共 60 条
[1]  
ALIZADEH S, 1998, THESIS U PENNSYLVANI
[2]  
Andersen T.G., 2000, Risk Mag., V18, P105
[3]   Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns [J].
Andersen, Torben G. ;
Bollerslev, Tim .
JOURNAL OF FINANCE, 1997, 52 (03) :1203-1203
[4]   The distribution of realized stock return volatility [J].
Andersen, TG ;
Bollerslev, T ;
Diebold, FX ;
Ebens, H .
JOURNAL OF FINANCIAL ECONOMICS, 2001, 61 (01) :43-76
[5]   Answering the skeptics: Yes, standard volatility models do provide accurate forecasts [J].
Andersen, TG ;
Bollerslev, T .
INTERNATIONAL ECONOMIC REVIEW, 1998, 39 (04) :885-905
[6]   The distribution of realized exchange rate volatility [J].
Andersen, TG ;
Bollerslev, T ;
Diebold, FX ;
Labys, P .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2001, 96 (453) :42-55
[7]   GMM and QML asymptotic standard deviations in stochastic volatility models: Comments on Ruiz (1994) [J].
Andersen, TG ;
Sorensen, BE .
JOURNAL OF ECONOMETRICS, 1997, 76 (1-2) :397-403
[8]  
ANDERSEN TG, 2001, 200101 NBER
[9]  
[Anonymous], VOLATILITY
[10]  
[Anonymous], 1999, COINTEGRATION CAUSAL