Abba An Agent Based Model Of The Banking System
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ABBA: An Agent-Based Model of the Banking System
Author: Mr.Jorge A. Chan-Lau
language: en
Publisher: International Monetary Fund
Release Date: 2017-06-15
A thorough analysis of risks in the banking system requires incorporating banks’ inherent heterogeneity and adaptive behavior in response to shocks and changes in business conditions and the regulatory environment. ABBA is an agent-based model for analyzing risks in the banking system in which banks’ business decisions drive the endogenous formation of interbank networks. ABBA allows for a rich menu of banks’ decisions, contingent on banks’ balance sheet and capital position, including dividend payment rules, credit expansion, and dynamic balance sheet adjustment via risk-weight optimization. The platform serves to illustrate the effect of changes on regulatory requirements on solvency, liquidity, and interconnectedness risk. It could also constitute a basic building block for further development of large, bottom-up agent-based macro-financial models.
ABBA: An Agent-Based Model of the Banking System
Author: Mr.Jorge A Chan-Lau
language: en
Publisher: International Monetary Fund
Release Date: 2017-06-09
A thorough analysis of risks in the banking system requires incorporating banks’ inherent heterogeneity and adaptive behavior in response to shocks and changes in business conditions and the regulatory environment. ABBA is an agent-based model for analyzing risks in the banking system in which banks’ business decisions drive the endogenous formation of interbank networks. ABBA allows for a rich menu of banks’ decisions, contingent on banks’ balance sheet and capital position, including dividend payment rules, credit expansion, and dynamic balance sheet adjustment via risk-weight optimization. The platform serves to illustrate the effect of changes on regulatory requirements on solvency, liquidity, and interconnectedness risk. It could also constitute a basic building block for further development of large, bottom-up agent-based macro-financial models.
Quantitative Methods for ESG Finance
A quantitative analyst’s introduction to the theory and practice of ESG finance In Quantitative Methods for ESG Finance, accomplished risk and ESG experts Dr. Cyril Shmatov and Cino Robin Castelli deliver an incisive and essential introduction to the quantitative basis of ESG finance from a quantitative analyst’s perspective. The book combines the theoretical and mathematical bases underlying risk factor investing and risk management with accessible discussions of ESG applications. The authors explore the increasing availability of non-traditional data sources for quantitative analysts and describe the quantitative/statistical techniques they’ll need to make practical use of these data. The book also offers: A particular emphasis on climate change and climate risks, both due to its increasing general importance and accelerating regulatory change in the space Practical code examples in a Python Jupyter notebook that use publicly available data to demonstrate the techniques discussed in the book Expansive discussions of risk factor investing, portfolio construction, ESG scoring, new ESG-driven financial products, and new financial risk management applications, particularly those making use of the proliferation of “alternative data”, both text and images A must-read guide for quantitative analysts, investment managers, financial risk managers, investment bankers, and other finance professionals with an interest in ESG-driven investing, Quantitative Methods for ESG Finance will also earn a place on the bookshelves of graduate students of business and finance.