Titlebook: Parameter Estimation in Stochastic Volatility Models; Jaya P. N. Bishwal Book 2022 The Editor(s) (if applicable) and The Author(s), under

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Jaya P. N. Bishwal of advanced suspension concepts, high structural stiffnesses of body and chassis components and extensive tuning work on the vehicle proving grounds. One of the key challenges in vehicle engineering is to optimize vehicle driving comfort without degrading the steering and handling performance.

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,Estimation in Gamma-Ornstein –Uhlenbeck Stochastic Volatility Model,rift 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.

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Maximum Quasi-Likelihood Estimation in Fractional Levy Stochastic Volatility Model,ctionally integrated exponential GARCH, in short FIECOGARCH process based on discrete observations. We deal with a compound Poisson FIECOGARCH process and study the asymptotic behavior of the maximum quasi-likelihood estimator. We show that the resulting estimators are consistent and asymptotically

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Bayesian Maximum Likelihood Estimation in Fractional Stochastic Volatility Model,y 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