Bayesian Maximum Likelihood Estimation in Fractional 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 12 in Parameter Estimation in Stochastic Volatility Models, 2022, pp 401-409 from Springer

Abstract: Abstract In mathematical finance, it is well accepted that volatility of a stock price is a stochastic process, not a constant. It is also known that volatility has long memory and clusters on high level. One way of modeling long memory is superposition of Ornstein–Uhlenbeck (supOU) processes as volatility models. The class of supOU processes can capture extremal clusters and long-range dependence. We consider volatility as a continuous model satisfying a stochastic differential equation driven by a persistent fractional Brownian motion. Long memory in volatility is a stylized fact in finance due to volatility clustering and persistence.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-03861-7_12

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DOI: 10.1007/978-3-031-03861-7_12

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