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Bruno Gagliano: Asymptotic theory for discretely observed stochastic volatility models
Adviser: Sara van de Geer
February 2008
Abstract:
This thesis investigates the estimation of parameters for discretely observed stochastic volatility models. The main concern is to give a general methodology for estimating the unknown parameters from a discrete set of observations of the stock price. Two estimation methods, the minimum contrast and estimating functions, are introduced and it is shown that, under certain assumptions, the estimators obtained are consistent and asymptotically normal. Finally, a series of simulations is performed to confirm the results and an application to real-world stock data is made.
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