In the aftermath of the recent financial crisis, the federal government has pursued significant regulatory reforms, including proposals to measure and monitor systemic risk. However, there is much debate about how this might be accomplished quantitatively and objectively—or whether this is even possible. A key issue is determining the appropriate trade-offs between risk and reward from a policy and social welfare perspective given the potential negative impact of crises. One of the first books to address the challenges of measuring statistical risk from a system-wide persepective, Quantifying Systemic Risk looks at the means of measuring systemic risk and explores alternative approaches. Among the topics discussed are the challenges of tying regulations to specific quantitative measures, the effects of learning and adaptation on the evolution of the market, and the distinction between the shocks that start a crisis and the mechanisms that enable it to grow.

In the aftermath of the recent financial crisis, the federal government has pursued significant regulatory reforms, including proposals to measure and monitor systemic risk. However, there is much debate about how this might be accomplished quantitatively and objectively—or whether this is even possible. A key issue is determining the appropriate trade-offs between risk and reward from a policy and social welfare perspective given the potential negative impact of crises. One of the first books to address the challenges of measuring statistical risk from a system-wide persepective, Quantifying Systemic Risk looks at the means of measuring systemic risk and explores alternative approaches. Among the topics discussed are the challenges of tying regulations to specific quantitative measures, the effects of learning and adaptation on the evolution of the market, and the distinction between the shocks that start a crisis and the mechanisms that enable it to grow.

The recent financial crisis and the difficulty of using mainstream macroeconomic models to accurately monitor and assess systemic risk have stimulated new analyses of how we measure economic activity and the development of more sophisticated models in which the financial sector plays a greater role. Markus Brunnermeier and Arvind Krishnamurthy have assembled contributions from leading academic researchers, central bankers, and other financial-market experts to explore the possibilities for advancing macroeconomic modeling in order to achieve more accurate economic measurement. Essays in this volume focus on the development of models capable of highlighting the vulnerabilities that leave the economy susceptible to adverse feedback loops and liquidity spirals. While these types of vulnerabilities have often been identified, they have not been consistently measured. In a financial world of increasing complexity and uncertainty, this volume is an invaluable resource for policymakers working to improve current measurement systems and for academics concerned with conceptualizing effective measurement.

The Handbook on Systemic Risk, written by experts in the field, provides researchers with an introduction to the multifaceted aspects of systemic risks facing the global financial markets. The Handbook explores the multidisciplinary approaches to analyzing this risk, the data requirements for further research, and the recommendations being made to avert financial crisis. The Handbook is designed to encourage new researchers to investigate a topic with immense societal implications as well as to provide, for those already actively involved within their own academic discipline, an introduction to the research being undertaken in other disciplines. Each chapter in the Handbook will provide researchers with a superior introduction to the field and with references to more advanced research articles. It is the hope of the editors that this Handbook will stimulate greater interdisciplinary academic research on the critically important topic of systemic risk in the global financial markets.

Little progress has been made so far in addressing—in a comprehensive way—the externalities caused by impact of the interconnectedness within institutions and markets on funding and market liquidity risk within financial systems. The Systemic Risk-adjusted Liquidity (SRL) model combines option pricing with market information and balance sheet data to generate a probabilistic measure of the frequency and severity of multiple entities experiencing a joint liquidity event. It links a firm’s maturity mismatch between assets and liabilities impacting the stability of its funding with those characteristics of other firms, subject to individual changes in risk profiles and common changes in market conditions. This approach can then be used (i) to quantify an individual institution’s time-varying contribution to system-wide liquidity shortfalls and (ii) to price liquidity risk within a macroprudential framework that, if used to motivate a capital charge or insurance premia, provides incentives for liquidity managers to internalize the systemic risk of their decisions. The model can also accommodate a stress testing approach for institution-specific and/or general funding shocks that generate estimates of systemic liquidity risk (and associated charges) under adverse scenarios.

This book is about how extreme and systemic risk can be analyzed in an integrated way. Risk analysis is understood to include measurement, assessment as well as management aspects. Integration is understood as being able to perform risk analysis for extreme and systemic events simultaneously. The presented approach is based on Sklar's theorem, which states that a multivariate distribution can be separated into two parts – one describing the marginal distributions and the other describing the dependency between the distributions using a so-called copula. It is suggested to reinterpret Sklar's theorem from a system or network perspective, treating copulas as a network property and individual, including extreme, risk as elements within the network. In that way, extreme and systemic risk can be analyzed independently as well as jointly across several scales. The book is intended for a large audience, and all techniques presented are guided with examples and applications with a special focus on natural disaster events. Furthermore, an extensive literature and discussion of it are given in each chapter for the interested reader.

We propose a framework to link empirical models of systemic risk to theoretical network/ general equilibrium models used to understand the channels of transmission of systemic risk. The theoretical model allows for systemic risk due to interbank counterparty risk, common asset exposures/fire sales, and a “Minsky" cycle of optimism. The empirical model uses stock market and CDS spreads data to estimate a multivariate density of equity returns and to compute the expected equity return for each bank, conditional on a bad macro-outcome. Theses “cross-sectional" moments are used to re-calibrate the theoretical model and estimate the importance of the Minsky cycle of optimism in driving systemic risk.