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Statistics seminar 2017: "Stress Testing and Risk Integration in Banks – A Statistical Framework and Practical Software Guide (in Matlab and R)"

Seminario di Statistica

16/02/2017 dalle 14:30 alle 15:30

Dove Dipartimento di Scienze Statistiche "P. Fortunati" - Via Belle Arti 41 - Aula Seminari 1° piano

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Relatore
Tiziano Bellini, PhD – Ernst & Young London

Abstract
The book provides a comprehensive view of risk management by focusing on the stress testing process. Using a bottom-up risk integration strategy, it presents a multi-country bank prototype to assess bank solvency in periods both long (economic capital) and short (liquidity mismatching).
Following the perspective of commercial banks, an easy to implement statistical framework is provided based on information available in the risk management practice. The following tools are at the very heart of the proposed stress testing system:
•    Vector auto-regression (VAR) and global vector auto-regression (GVAR) are used to model macroeconomic scenarios.
•    Asset and liability management (A&LM) and Value at risk (VaR) allow us to measure interest rate, market and liquidity risks. The assessment is based on the above macroeconomic scenarios.
•    Credit portfolio modelling is used to assess the impact of adverse economic conditions on loans, mortgages and other credit facilities. A transmission mechanics is crucial to mimic real world connections between real world and financial economy. On this, regulatory ratios are examined as a critical step of the overall stress testing process.
•    Risk integration and reverse stress testing finalise the framework by allowing analysts to investigate bank weaknesses following a holistic perspective.

Keywords:
Vector auto-regression (VAR), asset and liability management (A&LM), credit portfolio modelling, holistic risk assessment, reverse stress testing.

Key references:
[1] Alessandri, P. and Drehmann, M. (2010). An economic capital model integrating credit and interest rate risk in the banking book. Journal of Banking and Finance, 34(4), 730–742.
[2] Bellini, T. (2013). Integrated bank risk modeling: a bottom-up statistical framework. European Journal of Operational Research, 230, 385–398.
[3] Breuer, T., Jandacka, M., Mencia, J., and Summer, M. (2012). A systematic approach to multi-period stress testing of portfolio credit risk. Journal of Banking and Finance, 36, 332–340.
[4] Castren, O., Dees, S., and Zaher, F. (2010). Stress-testing euro area corporate default probabilities using a global macroeconomic model. Journal of Financial Stability, 6, 64–74.
[5] Drehmann, M., Stringa, M., and Sorensen, S. (2010). The integrated impact of credit and interest rate risk on banks: A dynamic framework and stress testing application. Journal of Banking and Finance, 34, 713–729.
[6] Lutkepohl, H. (1991). Introduction to Multiple Time Series Analysis. Springer-Verlag, Berlin.
[7] Pesaran, M., Schuermann, T., and Weiner, S. (2004). Modeling regional interde-pendencies using a global error-correcting macroeconometric model. Journal of Business and Economic Statistics, 22, 129–162.

Book reviews:
"Stress Testing and Risk Integration in Banks is a book that both finance academics and risk management experts have long sought. It bridges a substantial gap between risk theory and banking practice by paving the way for sound quantitative approaches in the area." --
Niklas F Wagner, University of Passau

"This book is highly practical and rigorous in its clear and refreshing coverage of current risk issues faced by global banks. Combining Matlab/R code, relevant exercises and business cases, it is comprehensive in scope and operationally highly relevant." --
Gary van Vuuren, Aviva Investors, London and North West University, South Africa

"Stress Testing and Risk Integration in Banks reveals the important connections between risk management and stress testing in the banking industry. These days, in which the industry is in the verge of its deepest change in decades, this book provides a much-needed framework to apply stress testing in practical terms." --
Juan Ignacio Peña, Universidad Carlos III

Contact person
Alessandra Luati