This paper documents nonlinear cross-sectional dependence in the term structure of U.S. Treasury yields and points out risk management implications. The analysis is based on a Kalman filter estimation of a two-factor affine model which specifies the yield curve dynamics. We then apply a broad...

We study the classical geometric mean reversion process which has been used to model commodity prices by various authors in Economics and Finance. We obtain certain regularity results which guarantee positivity and the existence of a stationary distribution. More important we derive an...

This paper proposes a simple maximum likelihood regression estimator that outperforms Least Squares in terms of efficiency and mean square error for a large number of skewed and/or heavy tailed error distributions

Income distribution analysis can be conducted from the point of view of the comparisons between different geographical regions, family types or socio-economic groups it can also be carried out to assess the effects of an economic policy over time. The paper presents the results of a research on...

This paper proposes a self-calibrated sparse learning approach for estimating a sparse target vector, which is a product of a precision matrix and a vector, and investigates its application to finance to provide an innovative construction of relative-volatility-managed portfolio (RVMP). The...

This paper studies the mean-variance (MV) portfolio problems under static and dynamic settings, particularly for the case that the number of assets ($p$) is larger than the number of observations ($n$). We prove that the classical plug-in estimation seriously distorts the optimal MV portfolio in...

The classical stochastic frontier panel data models provide no mechanism for disentangling individual time-invariant unobserved heterogeneity from inefficiency. Greene (2005a, b) proposed the ‘true' fixed-effects specification, which distinguishes these two latent components while allowing for...

A flexible framework for the analysis of tail events is proposed. The framework contains tail moment measures that allow for Expected Shortfall (ES) estimation. Connecting the implied tail thickness of a family of distributions with the quantile and expectile estimation, a platform for risk...

Lévy subordinated hierarchical Archimedean copulas (LSHAC) are flexible models in high dimensional modeling. However, there is limited literature discussing their applications, largely due to the challenges in estimating their structures and their parameters. In this paper, we propose a...

This paper describes a general procedure for constructing a class of asymmetric distributions based on symmetric parametric distribution families, and investigates some inherent relationships between the constructed asymmetric distribution and the original symmetric ones. This procedure is used...