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Title: Essays on empirical performance of affine jump-diffusion option pricing models
Author: Zhang, Xiang
ISNI:       0000 0003 7074 9466
Awarding Body: Oxford University
Current Institution: University of Oxford
Date of Award: 2012
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This thesis examines the empirical performance of option pricing models in the continuous- time affine jump-diffusion (AID) class. In models of this class, the underlying returns are governed by stochastic volatility diffusions and/or jumps and the dynamics of the whole system has affine dependence on the state variables. The thesis consists of three essays. The first essay calibrates a wide range of AID option pricing models to S&P 500 index options. The aim is to empirically identify how best to structure two types of risk components- stochastic volatility and jumps - within the framework of multi-factor AID specifications. Our specification analysis shows that the specifications with more-than-two diffusions perform well and that a three-factor specification should be preferred, in which jump intensities are allowed to depend on an independent diffusion process. Having identified the well-performing pricing model specifications, the second essay examines how such a model can be used to forecast realized volatility using only option prices as an input. To do so, the dynamics of volatility implied by the model are used to construct a forecasting equation in which the spot volatilities extracted from observed option prices act as the key predictors. The analysis indicates that the option-based multi-factor forecasting model outperforms other popular models in forecasting realized volatility of S&P 500 Index returns over most of the short-term horizons considered. The final essay investigates if a two-factor AJD model can fit option pricing patterns generated by a single-factor long memory volatility model. Our simulation experiments show that this model does well in this respect. Remarkably, however, at the fitted parameter values it does not generate the volatility auto-correlation patterns that are characteristic of long-memory volatility models.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available