The paper develops a tail risk forecasting model that incorporates the wealth of economic and financial information available to risk managers. The approach can be viewed as a regularized extension of the two-stage GARCH-EVT model of McNeil and Frey (2000) where we permit a time-varying...

Trust in the reported data of contagious diseases in real time is important for policy makers. Media and politicians have cast doubt on Chinese reported data on COVID-19 cases. We find Chinese confirmed infections match the distribution expected in Benford’s Law and are similar to that seen in...

This paper shows the wage offer distribution can be nonparametrically identified from the gaps between any two wage offers received by a worker for those who received two or more offers in a short period of time. While nonparametric estimation is infeasible in practice because it requires an...

This study provides empirical evidence for the efficacy of deriving firms' earnings forecasts from predictions of the complete, conditional probability density function (pdf). Relative to cross-sectional earnings forecasts based on OLS regressions, improvements of accuracy, bias and measures for...

Marketing researchers are increasingly taking advantage of the instrumental variable (IV)-free Gaussian copula approach. They use this method to identify and correct endogeneity when estimating regression models with non-experimental data. The Gaussian copula approach’s original presentation...

In developing countries, crop insurance is a major factor of sustainability for farming business. Due to the lack of infrastructure and of claims experts in these regions, parametric crop insurance is the favorite way of providing insurance. Parametric insurance has the advantage of reducing the...

We introduce a structural vector autoregressive (SVAR) model with each of the mutually independent errors following a skewed generalized t-distribution that is more flexible than a Student's t-distribution typically considered. Hence, the effect of potential distributional misspecification is...

We present a general methodology based on skew t copulas and Bayesian inference for modelling extreme multivariate dependent losses and the regulatory capital for operational risk. Current approaches fail to model both asymmetric dependence and accurate extreme upper tail dependence (e.g. 99.9%...

A class of local linear kernel density estimators based on weighted least squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition to...

This paper studies the theoretical properties and implementation issues with the risk-neutral density (RND) estimator based on the mixture-of-lognormal (MLN) approach. First, we establish the consistency and asymptotic normality of the MLN method under the correct choice of mixtures and propose...