Volatility estimation with functional gradient descent for very high-dimensional financial time series
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Bühlmann, Peter
ETH Zürich, Switzerland
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…
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Language
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Classification
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Economics
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License
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License undefined
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Persistent URL
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https://susi.usi.ch/usi/documents/318042