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We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental factor models (FFMs). FFMs are the typical benchmark in the asset management industry and depart from the usual statistical factor models and the factor models with observed...
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A popular risk measure, conditional value-at-risk (CVaR), is called expected shortfall (ES) in financial applications. The research presented involved developing algorithms for the implementation of linear regression for estimating CVaR as a function of some factors. Such regression is called...
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) and studies the properties of the Lasso and adaptive Lasso as estimators of this model. The parameters of the model are … finite sample properties of the Lasso by deriving upper bounds on the estimation and prediction errors that are valid with … of non zero increments grows slower than √T . By simulation experiments we investigate the properties of the Lasso and …
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We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top …
Persistent link: https://www.econbiz.de/10011309448
high dimensional by construction and sparse by assumption, is estimated using the Lasso. We apply this method to the …
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