Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion).
J ROY STAT SOC B
333 - 361.
The objective of the paper is to present a novel methodology for likelihood-based inference for discretely observed diffusions. We propose Monte Carlo methods, which build on recent advances on the exact simulation of diffusions, for performing maximum likelihood and Bayesian estimation.
|Title:||Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)|
|Keywords:||Cox-Ingersoll-Ross model, EM algorithm, graphical models, Markov chain Monte Carlo methods, Monte Carlo maximum likelihood, retrospective sampling, STOCHASTIC DIFFERENTIAL-EQUATIONS, COUPLED CHEMICAL-REACTIONS, MONTE-CARLO EVALUATION, MAXIMUM-LIKELIHOOD, EM ALGORITHM, MODELS, TIME, INFERENCE, VOLATILITY, VARIANCE|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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