State price density (SPD) contains important information concerning market expectations. In existing literature, a constrained estimator of the SPD is found by nonlinear least squares in a suitable Sobolev space...

State price density (SPD) contains important information concerning market expectations. In existing literature, a constrained estimator of the SPD is found by nonlinear least squares in a suitable Sobolev space. We improve the behavior of this estimator by implementing a covariance structure...

We study the efficient estimation of nonparametric regressions with conditional heteroskedasticity in a time series setting. We introduce a weighted local polynomial regression smoother that takes account of the dynamic heteroskedasticity. The effect of weighting on nonparametric regressions is...

Recently, there has been much discussion about replicability and credibility. By integrating the full research record, increasing statistical power, reducing bias and enhancing credibility, meta-analysis is widely regarded as 'best evidence'. Through Monte Carlo simulation, closely calibrated on...

Financial analysts assume that the reliability of predictions derived from regression analysis improves with sample size. This is generally true because larger samples tend to produce less noisy results than smaller samples. But this is not always the case. Some observations are more relevant...

Motivated by the seminal theory of Sure Independence Screening (Fan and Lv, 2008, SIS), we investigate here another popular and classical variable screening method, namely, Forward Regression (FR). Our theoretical analysis reveals that FR can identify all relevant predictors consistently, even...

Investors sometimes have strong convictions that a distinctive economic regime will prevail in the period ahead and therefore would like to form a portfolio that reflects the expected returns, standard deviations, and correlations of assets during such a regime. To do so, they typically isolate...

In extreme value statistics, the tail index is an important measure to gauge the heavy-tailed behavior of a distribution. Under Pareto-type distributions, we employ the logarithmic function to link the tail index to the linear predictor induced by covariates, which constitutes the tail index...

In financial practice, it is important to understand the dependence structure between the returns of individual assets and the market index. This particularly true under extreme situations. Theoretically, this amounts to regress the dependence relationship against a set of pre-specified...

Insurance claims data usually contains a large number of zeros and exhibits fat-tail behavior. Misestimation of one end of the tail impacts the other end of the tail of the claims distribution; such can affect both the adequacy of premiums and needed reserves to hold. In addition, insured...