Simulated nonparametric estimation of continuous time models of asset prices and returns

Altissimo, Filippo and Mele, Antonio (2004) Simulated nonparametric estimation of continuous time models of asset prices and returns. Discussion paper (476). Financial Markets Group, The London School of Economics and Political Science, London, UK.

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Abstract

This paper introduces a new parameter estimator of dynamic models in which the state is a multidimensional, continuous-time, partially observed Markov process. The estimator minimizes appropriate distances between nonparametric joint (and/or conditional) densities of sample data and nonparametric joint (and/or conditional) densities estimated from data simulated out of the model of interest. Sample data and model-simulated data are smoothed with the same kernel. This makes the estimator: 1) consistent independently of the amount of smoothing; and 2) asymptotically root-T normal when the smoothing parameter goes to zero at a reasonably mild rate. When the underlying state is observable, the estimator displays the same asymptotic efficiency properties as the maximum-likelihood estimator. In the partially observed case, we derive conditions under which efficient estimators can be implemented with the help of auxiliary prediction functions suggested by standard asset pricing theories. The method is flexible, fast to implement and possesses finite sample properties that are well approximated by the asymptotic theory.

Item Type:	Monograph (Discussion Paper)
Official URL:	https://www.fmg.ac.uk/
Additional Information:	© 2004 The Authors
Divisions:	Financial Markets Group
Subjects:	H Social Sciences > HG Finance H Social Sciences > HB Economic Theory
JEL classification:	G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing; Trading volume; Bond Interest Rates C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C32 - Time-Series Models C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C15 - Statistical Simulation Methods; Monte Carlo Methods; Bootstrap Methods
Date Deposited:	30 Jul 2009 14:53
Last Modified:	11 Dec 2024 18:38
URI:	http://eprints.lse.ac.uk/id/eprint/24674

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