Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance
A common model for security price dynamics is the continuous time stochastic volatility model. For this model, Hull and White (1987) show that the price of a derivative claim is the conditional expectation of the Black-Scholes price with the forward integrated variance replacing the Black-Scholes variance. Implementing the Hull and White characterization requires both estimates of the price dynamics and the conditional distribution of the forward integrated variance given observed variables. Using daily data on close-to-close price movement and the daily range, we find that standard models do not fit the data very well and a more general three factor model does better, as it mimics the long-memory feature of financial volatility. We develop techniques for estimating the conditional distribution of the forward integrated variance given observed variables.
|Date of creation:||2000|
|Date of revision:|
|Contact details of provider:|| Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097|
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://econ.duke.edu/
When requesting a correction, please mention this item's handle: RePEc:duk:dukeec:00-04. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Department of Economics Webmaster)
If references are entirely missing, you can add them using this form.