No abstract

In earlier studies, the estimation of the volatility of a stock using information on the daily opening, closing, high and low prices has been developed; the additional information in the high and low prices can be incorporated to produce unbiased (or near-unbiased) estimators with substantially lower variance than the simple open--close estimator. This paper tackles the more difficult task of estimating the correlation of two stocks based on the daily opening, closing, high and low prices of each. If we had access to the high and low values of some linear combination of the two log prices, then we could use the univariate results via polarization, but this is not data that is available. The actual problem is more challenging; we present an unbiased estimator which halves the variance.Comment: Published in at http://dx.doi.org/10.1214/07-AAP460 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

This paper is concerned with the estimation in the additive Cox model with time-dependent covariates when the number of additive components p is greater than the sample size n. By combining spline representation and the group lasso KEYWORDS

Summary Rejection inference aims to reduce sample bias and to improve model performance in credit scoring. We propose a semisupervised clustering approach as a new rejection inference technique. K‐prototype clustering can deal with mixed types of numeric and categorical characteristics, which are common in consumer credit data. We identify homogeneous acceptances and rejections and assign labels to part of the rejections according to the label of acceptances. We test the performance of various rejection inference methods in logit, support vector machine and random‐forests models based on data sets of real consumer loans. The predictions of clustering rejection inference show advantages over other traditional rejection inference methods. Inferring the label of the rejection from semisupervised clustering is found to help to mitigate the sample bias problem and to improve the predictive accuracy.