Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance

Datos Bibliográficos

ID19313645
AutoresA Ronald Gallant (0009-0006-0658-6454, University of North Carolina at Chapel Hill), Chien-Te Hsu, Chiente Hsu (Credit Suisse (Switzerland)), George Tauchen (0000-0003-0518-3611, Duke University)
Año1999
Volumen81
Número4
Páginas617-631
Fecha de publicación1999-11-01
Open AccessNo
TipoARTICLE
RevistaThe Review of Economics and Statistics (JOURNAL)
Identificadores de la revistaISSN: 0034-6535 • E-ISSN: 1530-9142
EditorialMIT Press - Journals (PUBLISHER)
DOI10.1162/003465399558481
OpenAlexW2127243480
IdiomaEN
Citas recibidas11
Referencias citadas32

Acommon 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

Autoregressive conditional heteroskedasticity · Conditional expectation · Conditional variance · Econometrics · Economics · Range (aeronautics) · Realized variance · Statistics · Stochastic volatility · Variance (accounting) · Volatility (finance) · Complex Systems and Time Series Analysis · Computer Science · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics

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