Splines for financial volatility |
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Authors: | Francesco Audrino, Peter Bü hlmann |
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Affiliation: | University of St Gallen, Switzerland; Eidgenössische Technische Hochschule Zürich, Switzerland |
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Abstract: | Summary. 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. |
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Keywords: | Boosting B-splines Conditional variance Financial time series Generalized auto-regressive conditional heteroscedasticity model Volatility |
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