Luis Ortiz Gracia is visiting professor at the Universitat de Barcelona School of Economics. He obtained a PhD in Mathematics from the Polytechnic University of Catalonia. His fields of research are Computational Finance and Quantitative Risk Management, with particular interests on wavelets-based methods for option pricing and aggregate risk measurement. He teaches Computational Aspects of Risk Management in the Master of Mathematics in Finance at the Autonomous University of Barcelona and Advanced Risk Quantification in the Master of Actuarial and Financial Sciences at the University of Barcelona. He led the Financial Mathematics and Risk Control research group at the Centre de Recerca Matemàtica and carried out research stays at the CWI in the Netherlands as well as in the School of Mathematics and Physics at the University of Queensland in Australia. Before he moved to the academia, he spent some years working on quantitative projects in several private firms within the fields of information technology, business and finance.
Dia i hora:
Dimecres 29 de novembre de 2017 a les 15:00.
Auditori del CRM, Facultat de Ciències, campus de la UAB.
1 hora aproximadament
L'assistència a aquest seminari és gratuïta.
Per motius d'aforament us agrairem que us enregistreu en el següent formulari: enllaç
Financial companies need to evaluate and to manage risks originated from their business activities. In particular, the credit risk underlying the credit portfolio is often the largest risk in a bank and its measure is used to assign capital in order to absorb potential losses arising from the credit portfolio. The Merton model is the basis of the Basel II approach. It is a Gaussian one-factor model such that default events are driven by a latent common factor that is assumed to follow a Gaussian distribution. Under this model, loss only occurs when an obligor defaults in a fixed time horizon. If we assume certain homogeneity conditions, this one factor model leads to a simple analytical asymptotic approximation for the loss distribution and Value at Risk(VaR), also called the Asymptotic Single Risk Factor(ASRF) model. This approximation works well for many small exposures but can underestimate risks in the presence of exposure concentrations. Name concentration (or exposure concentration) arises from an unequal distribution of loans to single borrowers. Monte Carlo simulation is a standard method for measuring the risk of a credit portfolio. However, this method is very time-consuming when the size of the portfolio increases. In order to overcome this computational complexity, several methods have been developed during the last years. We present a new method particularly very well suited for concentrated portfolios.