We study conditional correlations between pairs of risks in normal variance mixture models, which are widely used in risk management and finance. In particular, we examine up- and down-correlations defined as the conditional correlation between the sum of risks and an individual component,...

Graphical models have demonstrated exceptional performance in uncovering the conditional dependence structure among a given set of variables. This paper introduces two novel graphical modeling techniques: Gslope and Tslope, which use the Sorted L1-Penalized Estimator (Slope) to directly estimate...

In this paper, we investigate the calibration of the smoothing parameter in an exponentially weighted moving average (EWMA) for realized covariance matrices. Although it is the crucial determinant in steering the dynamics of the EWMA, little attention is drawn on its calibration in many...

Introduction to empirical data analysis -- Regression analysis -- Analysis of variance -- Discriminant analysis -- Logistic regression -- Contingency analysis -- Factor analysis -- Cluster analysis -- Conjoint analysis.

We consider the best quadratic unbiased estimators of the integrated variance in the presence of independent market microstructure noise. We establish the asymptotic normality of a feasible best quadratic unbiased estimator under the assumption of constant volatility and show that it is...

Markowitz’s celebrated mean-variance portfolio optimization theory assumes that the means and covariances of the underlying asset returns are known. In practice, they are unknown and have to be estimated from historical data. Plugging the estimates into the efficient frontier that assumes...

The large majority of the criteria for model selection are functions of the usual variance estimate for a regression model. The validity of the usual variance estimate depends on some assumptions, most critically the validity of the model being estimated. This is often violated in model...