UBI Banca used MATLAB and Statistics and Machine Learning Toolbox to develop a Merton-based credit model that determines the distribution of losses and the VaR at different confidence intervals. They delivered their findings to upper management in an intuitive graphical format.
Modafferi used MATLAB to import both market data, such as stock and stock index quotes, and internal data, such as decade rates, losses given default, and exposures at default. He created a default model with MATLAB, aggregating the data by sector. Statistics and Machine Learning Toolbox enabled Modafferi to perform sector regression and correlation analyses to assess the effects of diversification and concentration of the credit portfolio.
He used MATLAB and Statistics and Machine Learning Toolbox to model and run Monte Carlo simulations to assess precise estimates of the loss distribution by analyzing the data convergence. The simulations enabled him to determine the entity of the losses at different confidence intervals over a specific time period.
Using MATLAB and Statistics and Machine Learning Toolbox, Modafferi developed a factor model that distinguishes between systematic and specific risk. Along with easing the computational burden, the model enabled Modafferi to obtain more efficient risk estimates and obtain more insight into the nature of the portfolio risk.
MATLAB helped Modafferi display the simulation results in analytical and graphical formats, including histograms, loss distributions, and convergence graphics. He delivered his findings as part of a quarterly risk management report that the bank management uses to make strategic decisions, such as rebalancing sectors in UBI’s portfolios.
Modafferi is also using MATLAB and related toolboxes to develop an internal pricing system that helps evaluate various portfolios of the trading and banking books as well as hedging policies to test and integrate external suites.