Advanced Search
MyIDEAS: Login

A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models

Contents:

Author Info

  • Márcio Laurini

    (IBMEC Business School)

Abstract

In this paper we analyze a maximum likelihood estimator using data cloning for stochastic volatility models.This estimator is constructed using a hybrid methodology based on Integrated Nested Laplace Approximations to calculate analytically the auxiliary Bayesian estimators with great accuracy and computational efficiency, without requiring the use of simulation methods as Markov Chain Monte Carlo. We analyze the performance of this estimator compared to methods based in Monte Carlo simulations (Simulated Maximum Likelihood, MCMC Maximum Likelihood) and approximate maximum likelihood estimators using Laplace Approximations. The results indicate that this data cloning methodology achieves superior results over methods based on MCMC, and comparable to results obtained by the Simulated Maximum Likelihood estimator.

Download Info

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.
File URL: http://professores.ibmecrj.br/erg/dp/papers/dp201202.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Economics Research Group, IBMEC Business School - Rio de Janeiro in its series IBMEC RJ Economics Discussion Papers with number 2012-02.

as in new window
Length:
Date of creation: 16 Mar 2012
Date of revision:
Handle: RePEc:ibr:dpaper:2012-02

Contact details of provider:
Postal: Av. Pres. Wilson 118, 11 andar, Rio de Janeiro, RJ, Brazil, 20030-020
Web page: http://professores.ibmecrj.br/erg/
More information through EDIRC

Related research

Keywords: Stochastic Volatility: Data Cloning; Maximum Likelihood; MCMC; Laplace Approximations.;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:ibr:dpaper:2012-02. 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: (Márcio Laurini).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.