Advanced Search
MyIDEAS: Login to save this paper or follow this series

A Bayesian semiparametric approach to pricing the S&P 500 index options

Contents:

Author Info

  • Marcin Kacperczyk; Paul Damien; Stephen Walker

Abstract

Several studies incorporating estimated volatilities into option pricing formulas have appeared in the literature. However, the models described in these studies tend to perform quite poorly in out-of-sample tests. In particular, significant departures from the observed prices can be seen for the deep out-of-the-money short-term call options where mispricing seems to be somewhat excessive. This paper develops a new family of semiparametric Bayesian models. A particular member from this family that includes a nonparametric component is used to model option prices with the aim of improving the out-of-sample predictions. The principal advantage of injecting a nonparametric component into the model is that wide ranges of kurtosis in the observed asset prices are allowed, leading to lower pricing errors in out-of-sample predictions; that is, significant departures from normality in the underlying distribution of the asset prices when modeled lead to reliable forecasts. A detailed comparative empirical analysis with recent approaches to this problem is made for European out-of-the-money call options for which maturity does not exceed 40 days; it is for this subset of options that the pricing errors from other approaches are significant. The results indicate that the semiparametric Bayesian approach does better in terms of out-of-sample valuation errors compared with other approaches to the problem. Also, consistent with evidence reported in recent literature, for the group of short-term options exhibiting similar moneyness, pricing errors tend to decrease with the time to maturity.

Download Info

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.

Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Winter Meetings with number 202.

as in new window
Length:
Date of creation: 11 Aug 2004
Date of revision:
Handle: RePEc:ecm:nawm04:202

Contact details of provider:
Phone: 1 212 998 3820
Fax: 1 212 995 4487
Email:
Web page: http://www.econometricsociety.org/pastmeetings.asp
More information through EDIRC

Related research

Keywords: option pricing; Bayesian nonparametric approach; variance regression;

Find related papers by JEL classification:

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:ecm:nawm04:202. 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: (Christopher F. Baum).

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.