Shows the advantages of operational risk data analysis
Introduces an impartial method for identifying the risk classes for operational risk losses
Uses the R software to implement the proposed procedures
Giovanni De Luca is a Professor of Economic Statistics and was coordinator of the bachelor degree in Statistics (until November 2019) at Parthenope University, Naples, Italy, where he has taught since 2003. He received his Ph.D. in Mathematical and Statistical Methods from the University of Perugia in 1997. From 1999 to 2002, he worked as an Assistant Professor at the University of Verona. His research interests include time series analysis and statistics for financial markets. Much of his work is focused on the modeling of the dependence structure among variables. He has also investigated mixture models for improving volatility prediction.
Danilo Carità obtained his Ph.D. in Economics, Sustainability and Statistics in 2018. He holds a bachelor´s degree in Statistics and a master´s degree in Quantitative Methods for Economics. He has participated in international conferences and contributed to the Econometric Research in Finance journal.
Francesco Martinelli is a senior financial quantitative analyst manager at UBI Banca. For 20 years, he has worked in the field of quantitative analysis applied to financial markets, in risk management, particularly market risk, credit risk, operational risk and counterparty risk sectors, asset management and the process of validation of internal models. He is also an expert on the estimation of the integrated macro-financial model.
This concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. The authors identify the risk classes by applying a pooling rule based on statistical tests of goodness-of-fit, use the theory of the mixture of distributions to analyze the loss severities, and apply copula functions for risk class aggregation. Lastly, they assess operational risk data in order to estimate the so-called capital-at-risk that represents the minimum capital requirement that a bank has to hold. The book is primarily intended for quantitative analysts and risk managers, but also appeals to graduate students and researchers interested in bank risks.
1 The Operational Risk.- 2 Identification of the Risk Classes.- 3 Severity Analysis.- 4 Frequency Analysis.- 5 Convolution and Risk Class Aggregation.- 6 Conclusions.
Giovanni De Luca is a Professor of Economic Statistics and was coordinator of the bachelor degree in Statistics (until November 2019) at Parthenope University, Naples, Italy, where he has taught since 2003. He received his Ph.D. in Mathematical and Statistical Methods from the University of Perugia in 1997. From 1999 to 2002, he worked as an Assistant Professor at the University of Verona. His research interests include time series analysis and statistics for financial markets. Much of his work is focused on the modeling of the dependence structure among variables. He has also investigated mixture models for improving volatility prediction.
Danilo Carità obtained his Ph.D. in Economics, Sustainability and Statistics in 2018. He holds a bachelor's degree in Statistics and a master's degree in Quantitative Methods for Economics. He has participated in international conferences and contributed to the Econometric Research in Finance journal.
Francesco Martinelli is a senior financial quantitative analyst manager at UBI Banca. For 20 years, he has worked in the field of quantitative analysis applied to financial markets, in risk management, particularly market risk, credit risk, operational risk and counterparty risk sectors, asset management and the process of validation of internal models. He is also an expert on the estimation of the integrated macro-financial model.
Über den Autor
Giovanni De Luca is a Professor of Economic Statistics and was coordinator of the bachelor degree in Statistics (until November 2019) at Parthenope University, Naples, Italy, where he has taught since 2003. He received his Ph.D. in Mathematical and Statistical Methods from the University of Perugia in 1997. From 1999 to 2002, he worked as an Assistant Professor at the University of Verona. His research interests include time series analysis and statistics for financial markets. Much of his work is focused on the modeling of the dependence structure among variables. He has also investigated mixture models for improving volatility prediction.
Danilo Carità obtained his Ph.D. in Economics, Sustainability and Statistics in 2018. He holds a bachelor's degree in Statistics and a master's degree in Quantitative Methods for Economics. He has participated in international conferences and contributed to the Econometric Research in Finance journal.
Francesco Martinelli is a senior financial quantitative analyst manager at UBI Banca. For 20 years, he has worked in the field of quantitative analysis applied to financial markets, in risk management, particularly market risk, credit risk, operational risk and counterparty risk sectors, asset management and the process of validation of internal models. He is also an expert on the estimation of the integrated macro-financial model.
Inhaltsverzeichnis
1 The Operational Risk.- 2 Identification of the Risk Classes.- 3 Severity Analysis.- 4 Frequency Analysis.- 5 Convolution and Risk Class Aggregation.- 6 Conclusions.
Klappentext
This concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. The authors identify the risk classes by applying a pooling rule based on statistical tests of goodness-of-fit, use the theory of the mixture of distributions to analyze the loss severities, and apply copula functions for risk class aggregation. Lastly, they assess operational risk data in order to estimate the so-called capital-at-risk that represents the minimum capital requirement that a bank has to hold. The book is primarily intended for quantitative analysts and risk managers, but also appeals to graduate students and researchers interested in bank risks.
Shows the advantages of operational risk data analysis
Introduces an impartial method for identifying the risk classes for operational risk losses
Uses the R software to implement the proposed procedures
