Systemic Risk, Policies, And Data Needs

Systemic Risk, Policies, And Data Needs

Systemic Risk, Policies, And Data Needs

Agostino Capponi
Columbia University

July 6, 2016

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INFORMS Tutorials in Operations Research, Forthcoming


The study of financial system stability is of fundamental importance in modern economies. The failure or distress experienced by systemically important financial institutions can have contagious effects on the rest of the financial system. This may in turn result in deteriorating macroeconomic conditions and price instability, with negative consequences and spillover effects to other sectors of the real economy. This tutorial surveys the different approaches to systemic risk modeling put forward by the academic and practitioner literature. We review the methodologies, with a focus on the relevant economic forces in play and the mechanisms leading to systemic instabilities. We discuss macroprudential, monetary and resolution policies targeting financial stability. We report the supervisory authorities of the different financial institutions, as well as the current barriers to data sharing.

Systemic Risk, Policies, And Data Needs – Introduction

The global interconnectedness of today’s financial systems, and the numerous channels along which distress can propagate and affect other economic sectors, has been subject of considerable investigation. Early studies on network resilience and systemic risk were conducted before the great recession (Allen and Gale (2000), Eisenberg and Noe (2001)), but research efforts have greatly intensified after it. The credit quality deteriorations and default events experienced by investment banks and mono-line insurances starting from the year 2007 have highlighted the fragility of the financial system, as well as the critical role played by the complex network of contractual dependencies.

There are two main forms of linkages arising between financial institutions. The first is via counterparty risk (Battiston et al. (2012), Capponi (2013), Eisenberg and Noe (2001), Glasserman and Young (2015)), and comes from the fact that institutions share risk through derivatives trading and interbank loans, thus incurring losses if their trading counterparties fail or enter into a distressed state. Under certain circumstances, counterparty related losses may lead to the insolvency of creditors who were relying on these payments to fulfill their obligations. A second form of contagion propagation is via common balance sheet holdings. In this case, forced sale of illiquid assets done by institutions who need to meet redemption requests or satisfy regulatory requirements may depress prices if their selling pressure cannot be adequately satisfied by unconstrained buyers. These price drops may in turn cause troubles to institutions holding the same assets on their balance sheets, leading to liquidity spirals and generating fire-sales externalities (Brunnermeier and Pedersen (2009), Shleifer and Vishny (1992, 2011), Capponi and Larsson (2015)).

This tutorial surveys the models proposed in the literature to measure systemic risk. Those include bottom-up approaches whose aim is to model the direct interaction between banks as well as the interconnectdness of their balance sheets, and top down approaches whose goal is to quantify the contribution to the overall system distress caused by each institution, typically achieved via the specification of a risk measure (Adrian and Brunnermeier (2016), Brownlees and Engle (2015), Acharya et al. (2012)). We survey both the underlying models and mathematical techniques that have been used for quantifying negative externalities caused by financial instability, and empirical literature on systemic risk measurements. We discuss preventive and resolution policies, designed to enhance the resilience of the whole financial system and minimize the inefficiencies arising when assets of defaulted institutions need to be liquidated. Those include macroprudential policies targeting financial stability, monetary policies aimed at preserving price stability, structural policies imposing constraints on the network infrastructure, and policies targeting the resolution of bank failures. We also highlight the key elements of policies aimed at categorizing systemically important financial institutions. An impediment to systemic risk analysis is the lack of a comprehensive dataset for the analysis of macro-financial linkages. We dedicate one section to describe the relation between regulatory authorities and supervised financial institutions. This provides guidance on which regulatory autorities are responsible for collecting datasources related to the different financial institutions, and hence facilitates the task of researchers and specialists relying on these data for systemic risk analysis.

The present tutorial complements the existing surveys on the subject. These include the excellent survey by Bisias et al. (2012) whose primary focus is on the collection of systemic risk measures proposed in the literature, including those based on macroeconomic factors, network and illiquidity measures, and stress testing. Another recent survey on the field has been written by Benoit et al. (2015), and is focused primarily on systemic risk measures and their empirical evaluations. We also refer to Staum (2013) for an early survey on systemic risk models but centered primarily on the network of counterparty relationships.

The rest of the chapter is organized as follows. Section 2 discusses the approaches to systemic risk modeling. Section 3 discusses policies. Section 4 discusses the dependence of financial institutions on their supervisory authorities and barriers to systemic risk data. Section 5 concludes.

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