Non-linear modelling and forecasting of S&P 500 volatility

Peter Verhoeven, Berndt Pilgram, Michael McAleer and Alistair Mees

Mathematics and Computers in Simulation (MATCOM), 2002, vol. 59, issue 1, 233-241

Abstract: This paper investigates the use of a flexible forecasting method based on non-linear Markov modelling and canonical variate analysis, and the use of a prediction algorithm to forecast conditional volatility. We assess the dynamic behaviour of the model by forecasting volatility of a stock index. It is found that the non-linear non-parametric model based on canonical variate analysis forecasts stock index volatility significantly better than the GJR-GARCH(1,1)-t model due to the flexibility in accommodating multiple dynamic patterns in volatility which are not captured by its parametric counterpart.

Keywords: Non-linear Markov modelling; Non-parametric model; Parametric model; Volatility forecasting (search for similar items in EconPapers)
Date: 2002
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/S0378475401004116
Full text for ScienceDirect subscribers only

Related works:
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:matcom:v:59:y:2002:i:1:p:233-241

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().