Volatility estimation with functional gradient descent for very high-dimensional financial time series
- Bühlmann, Peter ETH Zürich, Switzerland
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2002
23
English
We propose a functional gradient descent algorithm (FGD) for estimating volatility and conditional covariances (given the past) for very high-dimensional financial time series of asset price returns. FGD is a kind of hybrid of nonparametric statistical function estimation and numerical optimization. Our FGD algorithm is computationally feasible in multivariate problems with dozens up to... Show more…
- Language
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- English
- Classification
- Economics
- License
- License undefined
- Identifiers
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- RERO DOC 5387
- Persistent URL
- https://susi.usi.ch/usi/documents/318042