1099-131X/asset/FOR_centre.gif?v=1&s=fb7cea1724d946a894d6a628c36664a1df5c300d)
Research article
Validation of volatility models
Article first published online: 4 DEC 1998
DOI: 10.1002/(SICI)1099-131X(1998090)17:5/6<349::AID-FOR701>3.0.CO;2-X
Copyright © 1998 John Wiley & Sons, Ltd.
Issue
1099-131X/asset/cover.gif?v=1&s=9c5c7e58eaaad51e6136554684ff8c6d0edb1574 "Journal of Forecasting")
Journal of Forecasting
Special Issue: Neural Networks and Financial Economics
Volume 17, Issue 5-6, pages 349–368, September - November 1998
Additional Information
How to Cite
Magdon-Ismail, M. and Abu-Mostafa, Y. S. (1998), Validation of volatility models. Journal of Forecasting, 17: 349–368. doi: 10.1002/(SICI)1099-131X(1998090)17:5/6<349::AID-FOR701>3.0.CO;2-X
Publication History
- Issue published online: 4 DEC 1998
- Article first published online: 4 DEC 1998
- Abstract
- References
- Cited By
Keywords:
- validation;
- volatility prediction;
- maximum likelihood
Abstract
In forecasting a financial time series, the mean prediction can be validated by direct comparison with the value of the series. However, the volatility or variance can only be validated by indirect means such as the likelihood function. Systematic errors in volatility prediction have an ‘economic value’ since volatility is a tradable quantity (e.g. in options and other derivatives) in addition to being a risk measure. We analyse the fidelity of the likelihood function as a means of training (in sample) and validating (out of sample) a volatility model. We report several cases where the likelihood function leads to an erroneous model. We correct for this error by scaling the volatility prediction using a predetermined factor that depends on the number of data points. Copyright © 1998 John Wiley & Sons, Ltd.