In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

In this paper we develop a new family of estimators of the covariance matrix that relies solely on forward-looking information. These estimators only use current price information from a cross-section of plain-vanilla options and employ different higher moments of the implied return...

We develop a new family of estimators of the covariance matrix that relies solely on forwardlooking information. It uses only current prices of plain-vanilla options. In an out-of-sample study we show that a minimum-variance strategy based on these fully-implied estimators outperforms several...

In this paper we develop the first estimator of the covariance matrix that relies solely on forward-looking information. This estimator only uses price information from a cross-section of plain-vanilla options. In an out-of-sample study for US blue-chip stocks we show that a minimum-variance...

In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...