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Lorenza Menghetti: Density estimation, deconvolution and the stochastic volatility model
Adviser: Sara van de Geer
August 2007
Abstract:
The stochastic volatility model contains the stochastic volatility process observed at discrete time instance with vanishing gaps whose density is to be estimated. The volatility density based on logarithm of the squared process is estimated with the deconvolving kernel density estimator. Since the error density is supersmooth, the convergence is very slow.
This thesis studies the theoretical and empirical behaviour of the bias and the variance of the estimator. Empirical study suggests considering the bandwidth to be smaller than the theoretical bandwidth and confirms the slow rate of convergence.
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