Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers
In this paper, we describe and compare two simulated Maximum Likelihood estimation methods for a basic stochastic volatility model. For both methods, the likelihood function is estimated using importance sampling techniques. Based on a Monte Carlo study, we assess which method is more effective. Further, we validate the two methods using diagnostic importance sampling test procedures. Stochastic volatility models with Gaussian and Student-t distributed disturbances are considered.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 8 (2004)
Issue (Month): 2 (May)
|Contact details of provider:|| Web page: http://www.degruyter.com|
|Order Information:||Web: http://www.degruyter.com/view/j/snde|
When requesting a correction, please mention this item's handle: RePEc:bpj:sndecm:v:8:y:2004:i:2:n:5. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla)
If references are entirely missing, you can add them using this form.