Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling
| Title: | Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling |
|---|---|
| Authors: | Tore Sell; Hans Julius Skaug; Jel Code C |
| Contributors: | The Pennsylvania State University CiteSeerX Archives |
| Source: | http://www2.economics.smu.edu.sg/events/Paper/ToreSellandKleppe.pdf. |
| Publication Year: | 2009 |
| Collection: | CiteSeerX |
| Subject Terms: | Accelerated Sequential Importance Sampling; Heston Model |
| Description: | Simulated maximum likelihood has proved to be a valuable tool for fitting the log-normal stochastic volatility model to financial returns time series. In this paper, we develop a methodology that generalizes these methods to more general stochastic volatility models that are naturally cast in terms of a positive volatility process. The methodology relies on combining two well known methods for evaluating the likelihood function – Sequential importance sampling and Laplace importance sampling. Two example models are considered, showing that the likelihood function can be evaluated using Monte Carlo methods even for non-Gaussian latent processes such as square-root diffusions. |
| Document Type: | text |
| File Description: | application/pdf |
| Language: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.8057; http://www2.economics.smu.edu.sg/events/Paper/ToreSellandKleppe.pdf |
| Availability: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.8057; http://www2.economics.smu.edu.sg/events/Paper/ToreSellandKleppe.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| Accession Number: | edsbas.3684BC91 |
| Database: | BASE |