Enrico Moretto

A multiple network approach to corporate governance

In this work, we consider corporate governance (CG) ties among companies from a multiple network perspective. Such a structure naturally arises from the close interrelation between the Shareholding network and the Board of Directors network. In order to capture the simultaneous effects of both networks on CG, we propose to model the CG multiple network structure via tensor analysis. In particular, we consider the TOPHITS model, based on the PARAFAC tensor decomposition, to show that tensor techniques can be successfully applied in this context. By providing some empirical results from the Italian financial market in the univariate case, we then show that a tensor–based multiple network approach can reveal important information.

Exact and Approximated Option Pricing in a Stochastic Volatility Jump-diffusion Model

We propose a stochastic volatility jump-diffusion model for option pricing with contemporaneous jumps in both spot return and volatility dynamics. The model admits, in the spirit of Heston, a closed-form solution for European-style options. To evaluate more complex derivatives for which there is no explicit pricing expression, such as barrier options, a numerical methodology, based on an “exact algorithm” proposed by Broadie and Kaya, is applied. This technique is called exact as no discretisation of dynamics is required. We end up testing the goodness of our methodology using, as real data, prices and implied volatilities from the DJ Euro Stoxx 50 market and providing some numerical results for barrier options and their Greeks.

Exploiting Default Probabilities in a Structural Model with Nonconstant Barrier

Structural models have been developed and used in financial literature to assess the probability of default of corporations. This article aims at reversing this approach, using this probability as an input and investigating if the default barrier can be considered flat, as done in similar analysis, or should more appropriately be time-varying.