|
Activity Number:
|
462
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Wednesday, August 5, 2009 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Business and Economic Statistics Section
|
| Abstract - #303308 |
|
Title:
|
Bayesian Filtering for Jump-Diffusions
|
|
Author(s):
|
Andrew Golightly*+
|
|
Companies:
|
Newcastle University
|
|
Address:
|
School of Maths & Stats, Newcastle Upon Tyne, International, NE1 7RU, United Kingdom
|
|
Keywords:
|
Particle filter ; Markov chain Monte Carlo ; Stochastic differential equation ; Jump component
|
|
Abstract:
|
In this talk, the problem of sequentially learning parameters governing discretely observed jump-diffusions is explored. The estimation framework involves the introduction of latent points between every pair of observations to allow a sufficiently accurate Euler-Maruyama approximation of the underlying (but unavailable) transition densities. Particle filtering algorithms are then implemented to sample the posterior distribution of the latent data and the model parameters online. The methodology uses a simple extension of the modified diffusion bridge construct examined by Durham & Gallant (2002) to impute missing data points. We apply the method to the estimation of parameters governing a stochastic volatility (SV) model with jumps. As well as using S&P 500 index data, a simulation study is provided.
|
