Ebook Stress Testing and Risk Integration in Banks: A Statistical Framework and Practical Software Guide (in Matlab and R) By Tiziano Bellini
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Stress Testing and Risk Integration in Banks: A Statistical Framework and Practical Software Guide (in Matlab and R) By Tiziano Bellini
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- Amazon Sales Rank: #557920 in Books
- Published on: 2016-11-17
- Original language: English
- Dimensions: 9.02" h x .88" w x 5.98" l,
- Binding: Hardcover
- 316 pages
Review
"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
From the Back Cover
Stress Testing and Risk Integration in Banks provides a comprehensive view of the risk management activity by means of the stress testing process. An introduction to multivariate time series modeling paves the way to scenario analysis in order to assess a bank resilience against adverse macroeconomic conditions. Assets and liabilities are jointly studied to highlight the key issues that a risk manager needs to face. A multi-national bank prototype is used all over the book for diving into market, credit, and operational stress testing.
Interest rate, liquidity and other major risks are also studied together with the former to outline how to implement a fully integrated risk management toolkit. Examples, business cases, and exercises worked in Matlab and R facilitate readers to develop their own models and methodologies.
About the Author Tiziano Bellini received his Ph.D. in Statistics from the Università degli Studi di Milano after being visiting Ph.D. student at the London School of Economics. He gained a wide risk management experience across Europe, in London and New York.He currently holds a Senior Management position at EY Financial Advisory Services in London. Previously he worked at HSBC headquarter, Prometeia and for other Italian leading Companies. He is also a guest Lecturer at the London School of Economics. Formerly, he served as Lecturer at the Università degli Studi di Bologna and Università degli Studi di Parma. He has published in European Journal of Operational Research, Computational Statistics and Data Analysis, and other top-reviewed Journals. He has given numerous training courses, seminars and conference presentations on statistics, risk management and quantitative methods.
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