Estimation in Gamma-Ornstein –Uhlenbeck Stochastic Volatility Model

Jaya P. N. Bishwal
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Jaya P. N. Bishwal: University of North Carolina at Charlotte, Department of Mathematics and Statistics

Chapter Chapter 6 in Parameter Estimation in Stochastic Volatility Models, 2022, pp 295-305 from Springer

Abstract: Abstract We generalize the Ornstein–Uhlenbeck process to include non-normal innovations. First we study the asymptotic behavior of the ratio estimator of the drift parameter in Gamma-Ornstein–Uhlenbeck volatility process based on observations of the price process. This model captures the stylized facts as it preserves both jumps in the volatility process. We study the behavior of the moment estimators.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-03861-7_6

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DOI: 10.1007/978-3-031-03861-7_6

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