Banking Systems Simulation

Presents information sources and methodologies for modeling and simulating banking system stability Combining both academic and institutional knowledge and experience, Banking Systems Simulation: Theory, Practice, and Application of Modeling Shocks, Losses, and Contagion presents banking system risk modeling clearly within a theoretical framework. Written from the global financial perspective, the book explores single bank risk, common bank exposures, and contagion, and how these apply on a systemic level. Zedda approaches these simulation methods logically by providing the basic building blocks of modeling and simulation, and then delving further into the individual techniques that make up a systems model. In addition, the author provides clear and detailed explanations of the foundational research into the mathematical and legal concepts used to analyze banking risk problems, measures and data for representing the main banking risk sources, and the major problems researchers are likely to encounter. There are numerous software descriptions throughout, with references and tools to help readers gain a proper understanding of the presented techniques and possibly develop new applications and research. The book concludes with an appendix that features real-world datasets and models. In addition, this book: • Provides a comprehensive overview of methods for analyzing models and simulating risk for banking and financial systems • Provides a clear presentation of the technical and legal concepts used in banking regulation • Presents unique insights from an expert’s perspective, with specific coverage of assessing risks and developing what-if analyses at the systems level • Concludes with a discussion of applications, including banking systems regulation what-if tests, cost-benefit analysis, evaluations of banking systems stability effects on public finances, dimensioning, and risk-based contributions for Deposit Guarantee Schemes (DGS) and Resolution Funds Banking Systems Simulation: Theory, Practice, and Application of Modeling Shocks, Losses, and Contagion is ideal for banking researchers focusing on computational methods of analysis as well as an appropriate reference for graduate-level students in banking, finance, and computational methods. Stefano Zedda is Researcher in Financial Mathematics at the University of Cagliari in Italy and qualified as associate professor in banking and corporate finance. His research is mainly focused on quantitative analyses for banking and finance, with a particular focus on banking systems modeling and simulation. In 2008, Zedda developed the mathematical modeling and software implementation of the Systemic Model for Banking Originated Losses (SYMBOL), further developed during his activity at the European Commission. The Commission subsequently adopted it as a standard tool for testing banking regulation proposals. Stefano Zedda’s research interests include banking, financial mathematics, and statistics, specifically simulation of banking and financial systems stability, banking regulation impact assessment, and interactive agent simulation.

The purpose of this paper is to develop a model framework for the analysis of interactions between banking sector risk, sovereign risk, corporate sector risk, real economic activity, and credit growth for 15 European countries and the United States. It is an integrated macroeconomic systemic risk model framework that draws on the advantages of forward-looking contingent claims analysis (CCA) risk indicators for the banking systems in each country, forward-looking CCA risk indicators for sovereigns, and a GVAR model to combine the banking, the sovereign, and the macro sphere. The CCA indicators capture the nonlinearity of changes in bank assets, equity capital, credit spreads, and default probabilities. They capture the expected losses, spreads and default probability for sovereigns. Key to the framework is that sovereign credit spreads, banking system credit risk, corporate sector credit risk, economic growth, and credit variables are combined in a fully endogenous setting. Upon estimation and calibration of the global model, we simulate various negative and positive shock scenarios, particularly to bank and sovereign risk. The goal is to use this framework to analyze the impact and spillover of shocks and to help identify policies that would mitigate banking system, sovereign credit risk and recession risk—policies including bank capital increases, purchase of sovereign debt, and guarantees.

Simulation has become a tool difficult to substitute in many scientific areas like manufacturing, medicine, telecommunications, games, etc. Finance is one of such areas where simulation is a commonly used tool; for example, we can find Monte Carlo simulation in many financial applications like market risk analysis, portfolio optimization, credit risk related applications, etc. Simulation in Computational Finance and Economics: Tools and Emerging Applications presents a thorough collection of works, covering several rich and highly productive areas of research including Risk Management, Agent-Based Simulation, and Payment Methods and Systems, topics that have found new motivations after the strong recession experienced in the last few years. Despite the fact that simulation is widely accepted as a prominent tool, dealing with a simulation-based project requires specific management abilities of the researchers. Economic researchers will find an excellent reference to introduce them to the computational simulation models. The works presented in this book can be used as an inspiration for economic researchers interested in creating their own computational models in their respective fields.

For junior- and senior-level simulation courses in engineering, business, or computer science. While most books on simulation focus on particular software tools, Discrete Event System Simulation examines the principles of modeling and analysis that translate to all such tools. This language-independent text explains the basic aspects of the technology, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments.

Tiivistelmä.

Daniel Beimborn develops a formal model in order to explore cooperative sourcing activities in the banking industry. Together with survey data from the German banking industry, the model is used in simulation studies which allow for compound analyses of causes and effects of cooperative sourcing.