Option pricing with asymmetric heteroskedastic normal mixture models

Jeroen V.K. Rombouts and Lars Stentoft

International Journal of Forecasting, 2015, vol. 31, issue 3, 635-650

Abstract: We propose an asymmetric GARCH in mean mixture model and provide a feasible method for option pricing within this general framework by deriving the appropriate risk neutral dynamics. We forecast the out-of-sample prices of a large sample of options on the S&P 500 index from January 2006 to December 2011, and compute dollar losses and implied standard deviation losses. We compare our results to those of existing mixture models and other benchmarks like component models and jump models. Using the model confidence set test, the overall dollar root mean squared error of the best performing benchmark model is significantly larger than that of the best mixture model.

Keywords: Asymmetric heteroskedastic models; Finite mixture models; Option pricing; Out-of-sample prediction; Statistical fit (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207014001782
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Option Pricing with Asymmetric Heteroskedastic Normal Mixture Models (2010)
Working Paper: Option Pricing with Asymmetric Heteroskedastic Normal Mixture Models (2010)
Working Paper: Option pricing with asymmetric heteroskedastic normal mixture models (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:31:y:2015:i:3:p:635-650

DOI: 10.1016/j.ijforecast.2014.09.002

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().