Kernel Based Nonlinear Canonical Analysis
We consider a kernel based approach to nonlinear canonical correlation analysis and its implementation for time series. We deduce various diagnostics for reversible processes and gaussian processes. The method is first applied to a stimulated series satisfying a diffusion equation allowing us to estimate nonparametrically the drift and volatility functions. The second application involves high frequency data on stock returns.
(This abstract was borrowed from another version of this item.)
|Date of creation:||1998|
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