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Using daily range data to calibrate volatility diffusions and extract the forward integrated variance

dc.contributor.author Hsu, G Tauchen with Chiente
dc.contributor.author Gallant, AR
dc.date.accessioned 2010-03-09T15:38:50Z
dc.date.issued 1999-11-01
dc.identifier.issn 0034-6535
dc.identifier.uri https://hdl.handle.net/10161/1999
dc.description.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.
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartof Review of Economics and Statistics
dc.title Using daily range data to calibrate volatility diffusions and extract the forward integrated variance
dc.type Journal article
dc.relation.journal The Review of Economics and Statistics
pubs.begin-page 617
pubs.end-page 631
pubs.issue 4
pubs.organisational-group Duke
pubs.organisational-group Economics
pubs.organisational-group Faculty
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 81


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