A new algorithm for calibrating agent-based models is proposed, which employs a popular gradient boosting framework. Machine learning techniques are not used to develop a surrogate model, but rather assist in narrowing down the parameter space during the search for optimal parameters. Our...

The analysis and measurement of poverty is a crucial and unsolved issue in the field of social science. This work aims to measure poverty as a multidimensional notion using a new composite indicator. However, subjective choices as different weighting schemes on the indicator's construction could...

The Polynomial Chaos Expansion (PCE) technique recovers a finite second order random variable exploiting suitable linear combinations of orthogonal polynomials which are functions of a given stochastic quantity $\xi$, hence acting as a kind of random basis. The PCE methodology has been developed...

This paper sets forth a novel approach to calculate the levelized cost of electricity (LCOE) using a probabilistic model that accounts for endogenous input parameters. The approach is applied to the example of a nuclear and gas power project. Monte Carlo simulation results show that a...

In this paper we discuss the pricing of Constant Maturity Credit Default Swaps (CMCDS) under single sided jump models. The CMCDS offers default protection in exchange for a floating premium which is periodically reset and indexed to the market spread on a CDS with constant maturity tenor written...

In a context of an ever-changing regulatory environment over the last years, Banks have witnessed the draft and publication of several regulatory guidelines and requirements in order to frame and structure their internal Risk Management.Among these guidelines, one has been specifically designed...

The paper discusses the lower partial moments for the return of the investment portfolio which consists of the assets whose random incomes are modeled by variance-gamma, gamma distributions and constants.The formulas depend on values of generalized hypergeometric functions. As a corollary, the...

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of...

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of...

This paper explores the ability of the Machine Learning (ML) techniques to calibrate models that replicate the outputs of the Vasicek credit risk model. This model measures the loss distribution of a portfolio made up of loans that can be exposed to multiple systemic factors and it is widely...