This paper studies how people make inference about a state of the world when the information structure includes additional, payoff-irrelevant states. For example, learning about effort from observed performance may require accounting for the otherwise irrelevant role of luck. This creates an...

We propose a bootstrap-based robust high-confidence level upper bound (Robust H-CLUB) for assessing the risks of large portfolios. The proposed approach exploits rank-based and quantile-based estimators, and can be viewed as a robust extension of the H-CLUB method (Fan et al., 2015). Such an...

We propose a quantile--based method to estimate the parameters (i.e. locations, dispersions, co--dispersions and the tail index) of an elliptical distribution, and a battery of tests for model adequacy. The method is suitable for vast dimensions since the estimators for the location vector and...

Statistical inferences for weights of the global minimum variance portfolio (GMVP) are of both theoretical and practical relevance for mean-variance portfolio selection. Daily realized GMVP weights depend only on realized covariance matrix computed from intraday highfrequency returns. In this...

We examine Markov-switching autoregressive models where the commonly used Gaussian assumption for disturbances is replaced with a skew-normal distribution. This allows us to detect regime changes not only in the mean and the variance of a specified time series, but also in its skewness. A...