Quantitative Operational Risk Models

Catalina Bolance has been Associate Professor of Quantitative Methods for Economics and Management Science of the Department of Econometrics at University of Barcelona since 2001. She received a PhD in Economics, an M.A. in Marketing, and a B.Sc. in Statistics at the University of Barcelona. She is currently member of the research group Risk in Finance and Insurance and is a specialist in applied nonparametric methods. She has coauthored several undergraduate books on applied statistics that are widely used in Spanish universities and has also published in high-quality scientific journals like Insurance: Mathematics and Economics, Statistics, and Astin Bulletin-The Journal of the International Actuarial Association, the journal of the International Actuarial Association. She has supervised many Master's and Ph.D. theses with an outstanding tutoring record. Since 2010 she has participated in a project of the London School of Economics on long-term care insurance sponsored by the AXA research fund. In 2004 she received the insurance international prize awarded by MAPFRE. Montserrat Guillen has been Chair Professor of Quantitative Methods at the University of Barcelona since 2001 and director of the research group on Risk in Finance and Insurance. She received an M.S. degree in Mathematics and Mathematical Statistics in 1987, and a Ph.D. degree in Economics from the University of Barcelona in 1992. She also received theM.A. degree in Data Analysis from the University of Essex, United Kingdom. She was Visiting Research faculty at the University of Texas at Austin (USA) in 1994. She holds a visiting professor position at the University of Paris II, where she teaches Insurance Econometrics. Her research focuses on actuarial statistics and quantitative risk management. Since 2005 she has been an associate editor for the Journal of Risk and Insurance, the official journal of the American Risk an

Understanding Operational Risk. Operational Risk Data and Parametric Models. Semiparametric Model for Operational Risk Severities. Combining Operational Risk Data Sources. Data Study. Underreporting. Combining Underreported Internal and External Data. A Guided Practical Example.