Stochastic Volatility Models: Methods of Pricing, Hedging and Estimation

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 1 in Parameter Estimation in Stochastic Volatility Models, 2022, pp 1-77 from Springer

Abstract: Abstract Stochastic volatility models are partially observed diffusions and are hidden Markov models when the driving noises are Brownian motions. The chapter is concerned with the study of statistics, econometrics, and financial engineering of high-frequency financial data. The development of increasingly complex financial products requires the use of advanced statistical methods. The purpose of the chapter is to present generalized bootstrap methods for estimation, calibration, and Malliavin calculus methods for pricing, hedging of derivative products (on equities, interest rate, credit risk), and portfolio optimization. Special attention will be paid to models in high dimension, models with jumps, models with long memory in stochastic volatility models

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-03861-7_1

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DOI: 10.1007/978-3-031-03861-7_1

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