The sample skewness and kurtosis of macroeconomic and financial time series are routinely scrutinized in the early stages of model-building and are often the central topic of studies in economics and finance. Notwithstanding the availability of several robust estimators, most scholars in...

Value at Risk (VaR) is the most popular market risk measure as it summarizes in one figure the exposure to different risk factors. It had been around for over a decade when Expected Shortfall (ES) emerged to correct its shortcomings. Both risk measures can be estimated under several models. We...

This paper derives the moment functions of the truncated skewed type III generalized logistic (SGL). These are then applied in finance for the development of value-at-risk, expected shortfall and downside risk measures for investment returns and values. The SGL distribution provides and good fit...

We elaborate on the tail conditional expectation, the tail conditional variance, and the tail variance when the residuals follow the standardized Pearson type-IV distribution. If the probability density function describing the data generation process is continuous and the moments are finite,...

This paper develops an approach based on Gram-Charlier-like expansions for modeling financial series to take in due account features such as leptokurtosis. A Gram-Charlier-like expansion adjusts the moments of interest of a given distribution via its own orthogonal polynomials. This approach,...

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...

A simple and commonly used method to approximate the total claim distribution of a (possible weakly dependent) insurance collective is the normal approximation. In this article, we investigate the error made when the normal approximation is plugged in a fairly general distribution-invariant risk...

In optimization problems appearing in fields such as economics, finance, or engineering, it is often important that a risk measure of a decision-dependent random variable stays below a prescribed level. At the same time, the underlying probability distribution determining the risk measure's...