| By: |
Malik, Sheheryar (Department of Economics, University of Warwick,);
Pitt, Michael K (Department of Economics, University of Warwick,) |
| Abstract: |
In this paper we provide a unified methodology in order to conduct
likelihood-based inference on the unknown parameters of a general class of
discrete-time stochastic volatility models, characterized by both a leverage
e®ect and jumps in returns. Given the non-linear/non-Gaussian state-space
form, approximating the likelihood for the parameters is conducted with output
generated by the particle filter. Methods are employed to ensure that the
approximating likelihood is continuous as a function of the unknown parameters
thus enabling the use of Newton-Raphson type maximization algorithms. Our
approach is robust and efficient relative to alternative Markov Chain Monte
Carlo schemes employed in such contexts. In addition it provides a feasible
basis for undertaking the non-trivial task of model comparison. The technique
is applied to daily returns data for various stock price indices. We find
strong evidence in favour of a leverage effect in all cases. Jumps are an
important component in two out of the four series we consider. |
| Keywords: |
Particle filter ; Simulation ; SIR ; State space ; Leverage effect ; Jumps |
| Date: |
2009 |
| URL: |
http://d.repec.org/n?u=RePEc:wrk:warwec:897&r=ets |