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Range‐Based Estimation of Stochastic Volatility Models
Open accessAbstract
We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range‐based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range‐based Gaussian quasi‐maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine the dynamics of daily exchange rate volatility and find the evidence points strongly toward two‐factor models with one highly persistent factor and one quickly mean‐reverting factor.
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The pricing of options and corporate liabilities
Black, F., & Scholes, M. (1973) · Journal of Political Economy · doi:10.1086/260062 · Cited by 29,074
Cited by
Oil price uncertainty and clean energy stock returns: New evidence from crude oil volatility index
Dutta, A. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.07.050 · Cited by 325
Metadata
- Authors
- Alizadeh, S., Brandt, M. W., & Diebold, F. X.
- Venue
- The Journal of Finance
- Publisher
- Wiley
- Published
- 2002-06
- Volume
- 57
- Issue
- 3
- Pages
- 1047-1091
- ISSN
- 0022-1082 (ISSN-L)
- 1540-6261
- OpenAlex
- W3125666236
- Cited by
- 1,215
- References
- 1
- FWCI
- 62.84
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Alizadeh, S., Brandt, M. W., & Diebold, F. X. (2002). Range‐Based Estimation of Stochastic Volatility Models. The Journal of Finance. https://doi.org/10.1111/1540-6261.00454
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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