Density Estimation for Robust Financial Econometrics
Takada, Teruko
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https://hdl.handle.net/2142/85513
Description
Title
Density Estimation for Robust Financial Econometrics
Author(s)
Takada, Teruko
Issue Date
2001
Doctoral Committee Chair(s)
Koenker, Roger W.
Department of Study
Economics
Discipline
Economics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Economics, Theory
Language
eng
Abstract
Chapter 3 introduces an efficient and robust parametric inference which minimizes the Hellinger distance between two nonparametrically smoothed density estimates: the simulated model density and corresponding observed density. This approach generalizes work of Beran (1977) and Basu and Lindsay (1994) so that dependent data and simulated model densities are allowed, enabling the estimation without simple analytical criterion functions. In application to the lognormal stochastic volatility model, the proposed estimator is found to be competitive with the Markov-chain Monte Carlo approach of Jacquier, Polson, and Rossi (1994).
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