Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance
AbstractA 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.
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Bibliographic InfoPaper provided by Duke University, Department of Economics in its series Working Papers with number 00-04.
Date of creation: 2000
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Other versions of this item:
- A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999. "Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 617-631, November.
- NEP-ALL-2000-10-05 (All new papers)
- NEP-ETS-2000-10-05 (Econometric Time Series)
- NEP-FMK-2000-10-05 (Financial Markets)
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