Network Theory And Financial Risk

With new innovations in the field, this new edition of Network Theory and Financial Risk has been fully updated and expanded. A hands-on guide to analysing and modelling financial networks, authors Kimmo Soramäki and Samantha Cook provide an in-depth introduction to network theory and examine the general tools for network analysis. [Resumen de editor]

This book presents the latest findings on network theory and agent-based modeling of economic and financial phenomena. In this context, the economy is depicted as a complex system consisting of heterogeneous agents that interact through evolving networks; the aggregate behavior of the economy arises out of billions of small-scale interactions that take place via countless economic agents. The book focuses on analytical modeling, and on the econometric and statistical analysis of the properties emerging from microscopic interactions. In particular, it highlights the latest empirical and theoretical advances, helping readers understand economic and financial networks, as well as new work on modeling behavior using rich, agent-based frameworks. Innovatively, the book combines observational and theoretical insights in the form of networks and agent-based models, both of which have proved to be extremely valuable in understanding non-linear and evolving complex systems. Given its scope, the book will capture the interest of graduate students and researchers from various disciplines (e.g. economics, computer science, physics, and applied mathematics) whose work involves the domain of complexity theory.

Modern financial systems exhibit a high degree of interdependence, with connections between financial institutions stemming from both the asset and the liability sides of their balance sheets. Networks--broadly understood as a collection of nodes and links between nodes--can be a useful representation of financial systems. By modeling economic interactions, network analysis can better explain certain economic phenomena. In this chapter, Allen and Babus argue that the use of network theories can enrich our understanding of financial systems. They explore several critical issues. First, they address the issue of systemic risk, by studying two questions: how resilient financial networks are to contagion, and how financial institutions form connections when exposed to the risk of contagion. Second, they consider how network theory can be used to explain freezes in the interbank market. Third, they examine how social networks can improve investment decisions and corporate governance, based on recent empirical results. Fourth, they examine the role of networks in distributing primary issues of securities. Finally, they consider the role of networks as a form of mutual monitoring, as in microfinance.

Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.

Modern financial systems exhibit a high degree of interdependence. There are different possible sources of connections between financial institutions, stemming from both the asset and the liability side of their balance sheet. For instance, banks are directly connected through mutual exposures acquired on the interbank market. Likewise, holding similar portfolios or sharing the same mass of depositors creates indirect linkages between financial institutions. Broadly understood as a collection of nodes and links between nodes, networks can be a useful representation of financial systems. By providing means to model the specifics of economic interactions, network analysis can better explain certain economic phenomena. In this paper we argue that the use of network theories can enrich our understanding of financial systems. We review the recent developments in financial networks, highlighting the synergies created from applying network theory to answer financial questions. Further, we propose several directions of research. First, we consider the issue of systemic risk. In this context, two questions arise: how resilient financial networks are to contagion, and how financial institutions form connections when exposed to the risk of contagion. The second issue we consider is how network theory can be used to explain freezes in the interbank market of the type we have observed in August 2007 and subsequently. The third issue is how social networks can improve investment decisions and corporate governance. Recent empirical work has provided some interesting results in this regard. The fourth issue concerns the role of networks in distributing primary issues of securities as, for example, in initial public offerings, or seasoned debt and equity issues. Finally, we consider the role of networks as a form of mutual monitoring as in microfinance.

Risk control and derivative pricing have become of major concern to financial institutions, and there is a real need for adequate statistical tools to measure and anticipate the amplitude of the potential moves of the financial markets. Summarising theoretical developments in the field, this 2003 second edition has been substantially expanded. Additional chapters now cover stochastic processes, Monte-Carlo methods, Black-Scholes theory, the theory of the yield curve, and Minority Game. There are discussions on aspects of data analysis, financial products, non-linear correlations, and herding, feedback and agent based models. This book has become a classic reference for graduate students and researchers working in econophysics and mathematical finance, and for quantitative analysts working on risk management, derivative pricing and quantitative trading strategies.

A clear understanding of what we know, don't know, and can't know should guide any reasonable approach to managing financial risk, yet the most widely used measure in finance today--Value at Risk, or VaR--reduces these risks to a single number, creating a false sense of security among risk managers, executives, and regulators. This book introduces a more realistic and holistic framework called KuU --the K nown, the u nknown, and the U nknowable--that enables one to conceptualize the different kinds of financial risks and design effective strategies for managing them. Bringing together contributions by leaders in finance and economics, this book pushes toward robustifying policies, portfolios, contracts, and organizations to a wide variety of KuU risks. Along the way, the strengths and limitations of "quantitative" risk management are revealed. In addition to the editors, the contributors are Ashok Bardhan, Dan Borge, Charles N. Bralver, Riccardo Colacito, Robert H. Edelstein, Robert F. Engle, Charles A. E. Goodhart, Clive W. J. Granger, Paul R. Kleindorfer, Donald L. Kohn, Howard Kunreuther, Andrew Kuritzkes, Robert H. Litzenberger, Benoit B. Mandelbrot, David M. Modest, Alex Muermann, Mark V. Pauly, Til Schuermann, Kenneth E. Scott, Nassim Nicholas Taleb, and Richard J. Zeckhauser. Introduces a new risk-management paradigm Features contributions by leaders in finance and economics Demonstrates how "killer risks" are often more economic than statistical, and crucially linked to incentives Shows how to invest and design policies amid financial uncertainty