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Discrete Time Stochastic Volatility Model

dc.contributor.advisorMadan, Dilipen_US
dc.contributor.advisorKedem, Benjaminen_US
dc.contributor.authorTang, Guojingen_US
dc.description.abstractIn this dissertation we propose a new model which captures observed features of asset prices. The model reproduces the skewness and fat tails of asset returns by introducing a discretized variance gamma process as the driving innovation process, in addition to a double gamma process to reflect the stochastic nature of volatility coefficients. The leverage effect between returns and volatilities is built in by a polynomial function describing the relationship between these two variables. One application of this model is to price volatility contracts whose payoffs depend on realized variance or volatility. Because of the scarcity of market quotes and consequent unavailability of risk neutral calibration, we propose a new scheme of pricing based on the model estimated from historical data. The estimation of the model parameters is carried out by maximizing likelihood function, which is calculated through a combination of Expectation-Maximization and Particle Filter algorithm. The resulting distribution is transformed by concave distortions, the extent of which reflects the risk aversion level of market.en_US
dc.format.extent737759 bytes
dc.titleDiscrete Time Stochastic Volatility Modelen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentMathematical Statisticsen_US
dc.subject.pquncontrolledConcave distortionen_US
dc.subject.pquncontrolledParticle Filteren_US
dc.subject.pquncontrolledState Space Modelen_US
dc.subject.pquncontrolledStochastic Volatilityen_US
dc.subject.pquncontrolledVolatility Swapen_US

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