Maximum likelihood estimation of SDEs is dicult because in general the transition density function of these processes is not known in closed form, and has to be approximated some- how. An approximation based on Ecient Importance Sampling (EIS) is detailed. Monte Carlo experiments, based on widely used diusion processes, evaluate its performance against an al- ternative IS strategy, showing that EIS is at least equivalent, if not superior, while allowing a greater exibility needed when examining more complicated models.
Efficient Importance Sampling Maximum Likelihood Estimation of Stochastic Differential Equations
ROSSI, EDUARDO
2004-01-01
Abstract
Maximum likelihood estimation of SDEs is dicult because in general the transition density function of these processes is not known in closed form, and has to be approximated some- how. An approximation based on Ecient Importance Sampling (EIS) is detailed. Monte Carlo experiments, based on widely used diusion processes, evaluate its performance against an al- ternative IS strategy, showing that EIS is at least equivalent, if not superior, while allowing a greater exibility needed when examining more complicated models.File in questo prodotto:
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