Using daily range data to calibrate volatility diffusions and extract the forward integrated variance
Abstract
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 that 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.
Type
Journal articlePermalink
https://hdl.handle.net/10161/1999Collections
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