Splines for financial volatility

Francesco Audrino () and Peter Bühlmann

Journal Of The Royal Statistical Society Series B, 2009, vol. 71, issue 3, pages 655-670

Abstract: We propose a flexible generalized auto-regressive conditional heteroscedasticity type of model for the prediction of volatility in financial time series. The approach relies on the idea of using multivariate "B"-splines of lagged observations and volatilities. Estimation of such a "B"-spline basis expansion is constructed within the likelihood framework for non-Gaussian observations. As the dimension of the "B"-spline basis is large, i.e. many parameters, we use regularized and sparse model fitting with a boosting algorithm. Our method is computationally attractive and feasible for large dimensions. We demonstrate its strong predictive potential for financial volatility on simulated and real data, and also in comparison with other approaches, and we present some supporting asymptotic arguments. Copyright (c) 2009 Royal Statistical Society.

Date: 2009

Downloads: (external link)
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9868.2009.00696.x link to full text (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Splines for Financial Volatility (2007)
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: http://EconPapers.repec.org/RePEc:bla:jorssb:v:71:y:2009:i:3:p:655-670

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1369-7412

Access Statistics for this article

Journal Of The Royal Statistical Society Series B is edited by C. Robert and A. T. A. Wood

More articles in Journal Of The Royal Statistical Society Series B from Royal Statistical Society
Series data maintained by Christopher F. Baum ().