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Splines for financial volatility
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DOI：10.1111/j.1467-9868.2009.00696.x.png)
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Abstract
En 中文
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.
Keywords:
Boosting
B-splines
Conditional variance
Financial time series
Generalized auto-regressive conditional heteroscedasticity model
Volatility
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