Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes

@article{Durham2002NumericalTF,
  title={Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes},
  author={Garland B. Durham and A. Gallant},
  journal={Journal of Business \& Economic Statistics},
  year={2002},
  volume={20},
  pages={297 - 338}
}
Stochastic differential equations often provide a convenient way to describe the dynamics of economic and financial data, and a great deal of effort has been expended searching for efficient ways to estimate models based on them. Maximum likelihood is typically the estimator of choice; however, since the transition density is generally unknown, one is forced to approximate it. The simulation-based approach suggested by Pedersen (1995) has great theoretical appeal, but previously available… Expand
Bayesian inference for nonlinear multivariate diffusion models observed with error
Maximum Likelihood Estimation for Integrated Diffusion Processes
Bayesian inference for diffusion processes: using higher-order approximations for transition densities
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 199 REFERENCES
Bayesian Estimation of Continuous-Time Finance Models 1 Introduction
Maximum Likelihood Estimation of Non-Linear Continuous-Time Term-Structure Models
Bayesian investigation of continuous -time finance models
Simulation estimation of continuous-time models with applications to finance
Bayesian Analysis of Stochastic Volatility Models
Estimation of stochastic volatility models via Monte Carlo maximum likelihood
A note on the existence of a closed form conditional transition density for the Milstein scheme
Finite Sample Properties of the Efficient Method of Moments
Estimation of Continuous Time Models for Stock Returns and Interest Rates
...
1
2
3
4
5
...