The objective of this paper is to present a framework for ESG integration and analyze the consequences of considering environmental, social and governance (ESG) factors in the optimization of investment portfolios. We use a multivariate normal distribution of returns and we generate portfolios...

We discuss how mixtures of Gamma distributions with mixing probabilities, shape and rate parameters depending on features can be fitted with neural networks. We develop two versions of the EM algorithm for fitting so-called Gamma Mixture Density Networks, which we call the EM network boosting...

While environmental, social, and governance (ESG) trading activity has been a distinctive feature of financial markets, the debate if ESG scores can also convey information regarding a company's riskiness remains open. Regulatory authorities, such as the European Banking Authority (EBA), have...

In this paper, based on the cumulative distribution function (CDF) method (Jradiet al., 2021) for finding the optimal quantile when estimating stochastic frontier models(SFM) with normal-exponential composite error term, we derive an expression to findthe optimal quantile for the SFM with...

A basic assumption of the classic reinsurance model is that the distribution of the loss is precisely known. In practice, only partial information is available for the loss distribution due to the lack of data and estimation error. We study a distributionally robust reinsurance problem by...

If the elements of the choice set in a decision model involving randomness are not arbitrary, but restricted appropriately, an expected utility ordering of them can be represented by a mean standard deviation ranking function. These restrictions can apply to the form of, or can specify...

In this paper, we find that the upside asymmetry calculated based on a new distribution-based asymmetry measure proposed by Jiang, Wu, Zhou, and Zhu (2020) is negatively related to average future returns in the crosssection of Chinese stock returns. By contrast, when using a conventional...

In this paper, we recall actuarial and financial applications of sums of dependent random variables that follow a non-Gaussian mean-reverting process and contemplate distribution approximations. Our work complements previous related studies restricted to lognormal random variables; we revisit...

Optimization of distortion riskmetrics with distributional uncertainty has wide applications in finance and operations research. Distortion riskmetrics include many commonly applied risk measures and deviation measures, which are not necessarily monotone or convex. One of our central findings is...

This paper investigates whether multivariate crash risk (MCRASH), defined as exposure to extreme realizations of multiple systematic factors, is priced in the cross-section of expected stock returns. We derive an extended linear model with a positive premium for MCRASH and we empirically confirm...