Returns in financial assets display consistent excess kurtosis and skewness, implying the presence of large fluctuations not forecasted by Gaussian models. This paper applies a resampling method based on the bootstrap and a bias-correction step to improve Value-at-Risk (VaR) forecasting ability...

Conflicting objectives becomes a common issue in many supply chain network optimization problems. In this paper, a new model is formulated to design a green supply chain network through a new mixed integer linear programming problem. Uncertain demand and stochastic environmental respect levels...

The present study aims at modelling market risk for four commodities, namely West Texas Intermediate (WTI) crude oil, natural gas, gold and corn for the period 2007-2017. To this purpose, we use Extreme Value Theory (EVT) together with a set of Conditional Auto-Regressive Logit (CARL) models to...

We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given time period. This requires a conditional valuation of the portfolio given the state of the world at a later time, a problem that is particularly challenging if the portfolio...

In this paper, we measure the size and the direction of the spillover effects among European commercial banks, with respect to their size, geographical position, income sources, and systemic importance for the period from 2006 to 2016, using a state-dependent sensitivity value-at-risk model,...