I construct a systemic liquidity risk index (SLRI) from data on violations of arbitrage relationships across several asset classes between 2004 and 2010. Then I test whether the equity returns of 53 global banks were exposed to this liquidity risk factor. Results show that the level of bank returns is not directly affected by the SLRI, but their volatility increases when liquidity conditions deteriorate. I do not find a strong association between bank size and exposure to the SLRI - measured as the sensitivity of volatility to the index. Surprisingly, exposure to systemic liquidity risk is positively associated with the Net Stable Funding Ratio (NSFR). The link between equity volatility and the SLRI allows me to calculate the cost that would be borne by public authorities for providing liquidity support to the financial sector. I use this information to estimate a liquidity insurance premium that could be paid by individual banks in order to cover for that social cost.

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.

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.

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 global financial crisis has forced a re-examination of risk transmission in the financial sector and how it affects financial stability. Current macroprudential policy and surveillance (MPS) efforts are aimed establishing a regulatory framework that helps mitigate the risk from systemic linkages with a view towards enhancing the resilience of the financial sector. This paper presents a forward-looking framework ("Systemic CCA") to measure systemic solvency risk based on market-implied expected losses of financial institutions with practical applications for the financial sector risk management and the system-wide capital assessment in top-down stress testing. The suggested approach uses advanced contingent claims analysis (CCA) to generate aggregate estimates of the joint default risk of multiple institutions as a conditional tail expectation using multivariate extreme value theory (EVT). In addition, the framework also helps quantify the individual contributions to systemic risk and contingent liabilities of the financial sector during times of stress.

This paper provides an overview of indicators that can be used to illustrate and analyze liquidity developments in financial markets. The measures include bid-ask spreads, turnover ratios, and price impact measures. They gauge different aspects of market liquidity, namely tightness (costs), immediacy, depth, breadth, and resiliency. These measures are applied in selected foreign exchange, money, and capital markets to illustrate their operational usefulness. A number of measures must be considered because there is no single theoretically correct and universally accepted measure to determine a market's degree of liquidity and because market-specific factors and peculiarities must be considered.

Until about twenty years ago, the consensus view on the cause of financial-system distress was fairly simple: a run on one bank could easily turn to a panic involving runs on all banks, destroying some and disrupting the financial system. Since then, however, a series of events—such as emerging-market debt crises, bond-market meltdowns, and the Long-Term Capital Management episode—has forced a rethinking of the risks facing financial institutions and the tools available to measure and manage these risks. The Risks of Financial Institutions examines the various risks affecting financial institutions and explores a variety of methods to help institutions and regulators more accurately measure and forecast risk. The contributors--from academic institutions, regulatory organizations, and banking--bring a wide range of perspectives and experience to the issue. The result is a volume that points a way forward to greater financial stability and better risk management of financial institutions.

This paper explores insurance as a source of financial system vulnerability. It provides a brief overview of the insurance industry and reviews the risks it faces, as well as several recent failures of insurance companies that had systemic implications. Assimilation of banking-type activities by life insurers appears to be the key systemic vulnerability. Building on this experience and the experience gained under the FSAP, the paper proposes key indicators that should be compiled and used for surveillance of financial soundness of insurance companies and the insurance sector as a whole.

As part of Basel III reforms, the NSFR is a new prudential liquidity rule aimed at limiting excess maturity transformation risk in the banking sector and promoting funding stability. The revised package has been issued for public consultation with a plan of making the rule binding in 2018. This paper complements earlier quantitative impact studies by discussing the potential impact of introducing the NSFR based on empirical analysis of end-2012 financial data for over 2000 banks covering 128 countries. The calculations show that a sizeable percentage of the banks in most countries would meet the minimum NSFR prudential requirement at end-2012, and, further, that larger banks tend to be more vulnerable to the introduction of the NSFR. Additionally, by comparing the NSFR to other structural funding mismatch indicators, we find that the NSFR is a relatively consistent regulatory measure for capturing banks’ funding risk. Finally, the paper discusses key policy issues for consideration in implementing the NSFR.