Abstract The need to integrate data from many sources is likely to be a key bottleneck in the analysis of systemic risk. Oversimplified approaches result in large amounts of code being written and maintained by hand, and many software engineers having access to sensitive data. We describe the separate dimensions of the problem – many “silver bullets” only address one or two. We explain why it is neither feasible nor desirable to use a single standard for all data sharing for financial research, and then explore the pros and cons of standards at different levels of detail. We then explore some of the tools needed to implement and evolve a set of data exchanges. Finally, we discuss how data difficulties raise important research issues for both financial analysis experts and computer scientists.

The systemic risk data integration challenge

Systemic risk analysis in the financial sector requires massive data integration. Information must be collected from hundreds (and ultimately, perhaps, thousands) of financial firms, corresponding entities must be matched, and all the data must be transformed to meet the needs of systemic risk analysis models. The Dodd–Frank act established an Office of Financial Research (OFR) and gave it the mission of collecting, integrating, and analyzing diverse data in order to better track and analyze financial systemic risk. The data integration challenge is daunting and, if handled poorly, will be a significant impediment to the OFR being able to do systemic analyses.

Abstract We present a quantitative methodology for analyzing the potential for contagion and systemic risk in a network of interlinked financial institutions, using a metric for the systemic importance of institutions: the Contagion Index.

We apply this methodology to a data set of mutual exposures and capital levels of financial institutions in Brazil in 2007 and 2008, and analyze the role of balance sheet size and network structure in each institution's contribution to systemic risk. Our results emphasize the contribution of heterogeneity in network structure and concentration of counterparty exposures to a given institution in explaining its systemic importance. These observations plead for capital requirements which depend on exposures, rather than aggregate balance sheet size, and which target systemically important institutions.

Keywords Default risk, domino effects, balance sheet contagion, scale-free network, default contagion, systemic risk, macro-prudential regulation, random graph.

Introduction

The recent financial crisis has emphasized the importance of systemic risk, defined as macro-level risk which can impair the stability of the entire financial system. Bank failures have led in recent years to a disruption of the financial system and a significant spillover of financial distress to the larger economy (Hellwig, 2009). Regulators have had great difficulties anticipating the impact of defaults partly due to a lack of visibility on the structure of the financial system as well as a lack of a methodology for monitoring systemic risk. The complexity of the contemporary financial systems makes it a challenge to define adequate indicators of systemic risk that could help in an objective assessment of the systemic importance of financial institutions and an objective framework for assessing the efficiency of macro-prudential policies.

Abstract We illustrate how a network-based analysis can be useful to the evaluation of systemic risk, highlighting the abilities of a network model in terms of identification and measurement of the system-wide effects. Beginning with the methodological framework used in the social interactions literature, we discuss the use of behavior-based models in the financial markets context and relate our approach to that used in the epidemiological literature. Using these ideas, we define a new measure of systemic risk. Our measure differs from existing approaches in that it depends on the specific network architecture and will be a function of the strategic behavior of agents in the system. The measure is a quantification of the average impact of a shock that emerges as the result of the strategic reaction of market participants. We provide an application of this approach discussing the role of correlated trading strategies in fully electronic exchanges. While such markets offer no ability for traders to choose their transaction partners, the realized pattern of trades resembles a highly organized network. Importantly, these network patterns are closely related to profitability in the market; certain positions in the network are more valuable than others. As well, the observed structure of the network implies a very large impact of shocks to the system. We conclude with some policy implications and suggestions for future research.

Keywords Financial networks, systemic risk, interconnections. JEL Classification G10, C21.

Abstract Over recent years a number of network models of interbank markets have been developed and applied to the analysis of insolvency contagion and systemic risk. In this chapter we survey the concepts used in these models and discuss their main findings as well as their applications in systemic risk analysis. Network models are designed to address potential domino effects resulting from the failure of financial institutions. Specifically they attempt to answer the question of whether the failure of one institution will result in the subsequent failure of others. Since in a banking crisis authorities usually intervene to stabilize the banking system, failures and contagious failures by domino effects are very rarely observed in practice. Empirical analysis is thus difficult and as a consequence most studies of insolvency contagion are built on simulation models. In this chapter we describe in some detail how such simulations are designed and discuss the main insights that have so far been obtained by applications to the complex network of real world exposure data of banking systems.

Keywords Contagion, Interbank Market, Systemic Risk, Financial Stability. JEL-Classification Numbers: G21, C15, C81, E44

Introduction

Will the failure of a financial institution be a threat to the stability of the banking system? This is a key question for authorities in the management of a financial crisis. At the height of a crisis the general level of uncertainty and the panic among market participants usually lead to stabilization policies and interventions of the public sector.

Abstract The recent financial crisis has brought to the fore issues of quantifying and reducing systemic risk. This focus has precipitated the exploration of various methods for measuring systemic risks and for attributing systemic risk contributions to systemically important financial institutions. Concomitant with this stream of research are efforts to collect, standardize and store data useful to these modeling efforts. While discussions of modeling approaches are pervasive in the literature on systemic risk, issues of data requirements and suitability are often relegated to the status of implementation details. This short chapter is an attempt to deepen this discussion. We provide a 2 × 2 mapping of modeling strategies to key data characteristics and constraints that can help modelers determine which models are feasible given the available data; conversely, the mapping can provide guidance for data collection efforts in cases where specific analytic properties are desired. The framework may also be useful for evaluating, at a conceptual level, the trade-offs for incremental data collection. To provide background for this mapping, we review the analytic benefits and limitations of using aggregate vs. micro-level data, provide background on the role of data linking and discuss some of the practical aspects of data pooling including concerns about confidentiality. Throughout the chapter, we include examples from various domains to make the points we outline concrete.

Introduction

Computational Issues and Requirements: Introduction

While the computational and security considerations to properly address systemic risk in the financial sector are still emerging, it is already very evident that they will be extremely challenging in multiple dimensions. This Part addresses this problem from three different perspectives.

In the first chapter, Enabling Data Analysis for Addressing Systemic Risk, Eric Hughes et al. start with an overview of the current trends in computational choices for big data analysis. They begin with traditional relational databases which have become the industry's norm, but require well-structured data that can be difficult to enforce across a diverse community. Next, massively parallel processing architectures are introduced as one strategy to manage the scalability of analytical solutions for very large data sets. NoSQL databases for both government and industry are discussed because they relax the disciplined data requirements needed by traditional databases in order to improve rapid assimilation and analytics of heterogeneous data sets. Looking out further, semantic databases promise the allure of automated logical reasoning, but this remains a research topic. Analytic cloud computing approaches take advantage of several of the previous topics to provide very simple, massively scalable, analytics using popular techniques like Google's MapReduce. Finally, of importance to the systemic risk management community is the development of complex event processing which focuses on real time analysis to reduce the latency issues often present in the batch techniques typically employed. The second major topic in the Hughes et al. chapter is a discussion of some of the biggest analysis challenges that need to be hosted on these computational architectures.

Abstract Recently, the US experienced an economic crisis that shook confidence in key aspects of the financial system, and led to some calls for changes in the way the government tracks economic information that might warn of such a crisis. Among those changes was the creation of the Office of Financial Research (OFR), intended to collect and provide information to “anticipate emerging threats to financial stability or assess how shocks to one financial firm could impact the system as a whole” (OFR 2010). These functions have been termed systemic risk: the risk that a threat to a large, single component of the financial system poses to the system as a whole, due to the inter-connectedness of the system and potential lack of consumer confidence in the system that might be caused if one component failed.

This chapter considers the computational approaches that may be needed to provide information about systemic risk, and possible mitigations of that risk. We acknowledge that there are many schools of thought for why the recent crisis occurred, the degree of systemic risk it posed, and possible government actions to mitigate the risk. Our position is that an agency such as the OFR with responsibility for monitoring systemic risk must be prepared to analyze diverse, uncertain information about the financial system and threats to it. Such an agency must be prepared to evaluate this information from multiple perspectives, and assess possible future outcomes given a variety of assumptions and regulatory responses.

Abstract This chapter discusses the information technology requirements of systemic risk management, from the point of view of a hypothetical regulator of an “originate-to-distribute” (O-D) financial supply chain. We take the view that, even though the mortgage sector remains seriously disabled following the World Financial Crisis of 2008, the information technology requirements for the collection and transmission of data, as well as the performance of various analytical operations, at each step of the O-D process are in fact generic to the development of scale efficiencies in funding consumer and small commercial loans. This chapter identifies requirements for the construction and use of scalable, data and compute intensive analytical solutions capable of meeting the challenge of decision support for institutions concerned with broad scope risk. Such considerations apply not just in the financial system, of course. But our discussion is particularly motivated by requirements for public regulators, financial services entities and other business entities with significant liquidity and financial management needs.

Introduction

The world financial crisis of 2008 was triggered by developments in the “originateto- distribute” (O-D) mortgage supply chain in the “shadow banking” system, which by 2006 had substantially replaced the role of regulated banks and government entities in originating and servicing mortgages in the United States. The O-D supply chain emerged as a more competitive solution, because it was able to partition the various roles into separately capitalized and larger-scale processing entities.

Abstract In this chapter, we review recent work on the regularity of dynamical market impact models and their associated optimal order execution strategies. In particular, we address the question of the stability and existence of optimal strategies, showing that in a large class of models, there is price manipulation and no well-behaved optimal order execution strategy. We also address issues arising from the use of dark pools and predatory trading.

Introduction

Market impact refers to the fact that the execution of a large order influences the price of the underlying asset. Usually, this influence results in an adverse effect creating additional execution costs for the investor who is executing the trade. In some cases, however, generating market impact can also be the primary goal, e.g., when certain central banks buy government bonds in an attempt to lower the corresponding interest rates.

Understanding market impact and optimizing trading strategies to minimize market impact has long been an important goal for large investors. There is typically insufficient liquidity to permit immediate execution of large orders without eating into the limit order book. Thus, to minimize the cost of trading, large trades are split into a sequence of smaller trades, which are then spread out over a certain time interval.

Abstract We use a multi-agent-based model to investigate and analyze financial crises where agents are large aggregates of the economic system under consideration. We analyze financial crises as the breakage of a dynamic financial equilibrium. We suggest that when the equilibrium is stable, a small perturbation is absorbed by the market. On the other hand, when the market becomes unstable, perturbations propagate and amplify through the system, and contagion and systemic risk occur, resulting in a global financial crisis.

The market instability indicator is the spectral radius of the Jacobian matrix of a dynamical system driving the evolution of the economy. Entries of this Jacobian matrix can be computed by estimating the elasticities of flows of funds between aggregate agents. The higher the elasticities, the larger the entries of the Jacobian matrix and the more unstable the economy. High leverage and borrowing capacity constraints increase elasticities and make the market unstable as soon as the market instability indicator is above the critical value 1.

In order to avoid deflation and economic collapse in 2008, the US government had a very strong reaction with Quantitative Easing and TARP. This put the market under rapid oscillations of very high amplitude and chaotic behavior, making long term forecasts inefficient. By correctly monitoring and utilizing elasticities, governments facing a major crisis may be able to optimize the efficiency of monetary and fiscal policies and accurately allocate their support to the various sectors of the economy, as opposed to being part of the origination of market chaos by inappropriate allocation of government resources.

Abstract The financial crisis has demonstrated the need to rethink the conceptual approach of risk and data collection within the financial sector, by taking a holistic, economic and financial system wide perspective. As part of maintaining financial stability in Europe, three European-wide supervisory authorities, a new supervisory task for credit institutions for the European Central Bank, and one macroprudential body have been established, which are supplemented by a scheme of inter-governmental financial assistance. This chapter provides an overview of the new challenges to manage systemic risks in the European financial system, focusing on the required macro- and micro level statistics and the new institutional and conceptual framework for identifying systemic risk and calls for further research to understand the behavioral aspects of decision making and herding effects in financial markets and to look beyond traditional economic theory, which seems to have failed to predict the size, magnitude and the contagion effects of the recent financial crises. Despite the fact that it is too early yet to judge the performance of the new financial stability framework, the set-up is in our view likely to have a major positive impact on the European endeavor to safeguard financial stability, bringing back the needed trust and confidence in financial markets.

Keywords Keywords: Financial stability, financial markets, financial statistics, banking union, systemic risk.

Abstract Historical accounts of financial crises suggest that fear and greed are the common denominators of these disruptive events: periods of unchecked greed eventually lead to excessive leverage and unsustainable asset-price levels, and the inevitable collapse results in unbridled fear, which must subside before any recovery is possible. The cognitive neurosciences may provide some new insights into this boom/bust pattern through a deeper understanding of the dynamics of emotion and human behavior. In this chapter, I describe some recent research from the neurosciences literature on fear and reward learning, mirror neurons, theory of mind, and the link between emotion and rational behavior. By exploring the neuroscientific basis of cognition and behavior, we may be able to identify more fundamental drivers of financial crises, and improve our models and methods for dealing with them.

Introduction

In March 1933, unemployment in the United States was at an all-time high. Over 4,000 banks had failed during the previous two months. Bread lines stretched around entire blocks in the largest cities. The country was in the grip of the Great Depression. This was the context in which Franklin Delano Roosevelt delivered his first inaugural address to the American people as the 32nd president of the United States. He began his address not by discussing economic conditions, nor by laying out his proposal for the “New Deal”, but with a powerful observation that still resonates today: “So, first of all, let me assert my firm belief that the only thing we have to fear is fear itself – nameless, unreasoning, unjustified terror which paralyzes needed efforts to convert retreat into advance”.

Systemic risk and the financial crisis of 2007 to 2009

In the fall and winter of 2008 to 2009, the worldwide economy and financial markets fell off a cliff. The stock market fell 42 percent in the United States and, on a dollar-adjusted basis, the market dropped 46 percent in the United Kingdom, 49 percent in Europe at large, 35 percent in Japan, and around 50 percent in the larger Latin American countries. Likewise, global gross domestic product (GDP) fell by 0.8 percent (the first contraction in decades), with the decline in advanced economies a sharp 3.2 percent. Furthermore, international trade fell a whopping 12 percent.

When economists bandy about the term systemic risk, this is what they mean. Financial firms play a critical role in the economy, acting as inter-mediaries between parties that need to borrow and parties willing to lend or invest. Without such intermediation, it is difficult for companies to get credit and conduct business, and for people to get student loans and automobile loans, to save, and to perform a range of other financial transactions. Systemic risk emerges when the financial sector as a whole has too little capital to cover its liabilities. This leads to the widespread failure of financial institutions and/or the freezing of capital markets, which greatly impairs financial intermediation, both in terms of the payments system and in terms of lending to corporations and households.

Networks: Introduction

Events leading to the recent financial crisis have underlined the importance of financial contagion: scenarios in which the failure of a financial institution lead to subsequent losses or default of other financial institutions, leading eventually to a large-scale failure of the financial system. Standard economic models of banking which have traditionally focused on a single representative bank, interacting with borrowers or a central bank, do not have much to say about such contagion phenomena, whose modeling requires a representation of the interlinkages between financial institutions and market participants. Such interlinkages have naturally motivated the use of network models in the analysis of systemic risk.

Early theoretical work on the stability of interbank networks – for instance the pioneering studies by Allen and Gale (2000) and Rochet and Tirole (1996) – have underlined the importance of interbank liabilities for understanding systemic risk in the framework of stylized network structures. Empirical studies by central banks on the structure of interbank payment systems and balance sheet interlinkages have subsequently revealed that interbank networks have a complex, heterogeneous structure and that care must be taken when applying insights derived from simple, homogeneous network models. An important challenge is to understand the mechanisms behind the emergence of these networks, the link between their structure and their stability properties, and the implications of network structure for the monitoring and regulation of systemic risk.

Regulation: Introduction

Policymakers have long been concerned with the sources and effects of systemic risk. Unfortunately, however, relatively few resources have been put to identifying and measuring this risk. There should be no doubt that the response to the financial crisis of 2007-09 requires good measures of systemic risk. And, indeed, the Dodd–Frank Act and international efforts sponsored by the Basel Committee and the Financial Stability Board mandate more stringent supervision of financial institutions that pose appreciable systemic risk.

These efforts provide a general sense of factors seen to contribute to systemic risk, including size, interconnectedness, and substitutability. The three chapters in this part of the Handbook generally take matters several steps further. The first, authored by Carsten Detken and Per Nymand-Andersen, focuses on the European approach to systemic risk, including a description of changes to the supervisory architecture for financial institutions devised in the wake of the financial crisis. It discusses the data and analytics necessary to identify systemic risk and describes the vulnerabilities that may precipitate a systemic crisis. The chapter suggests a framework for assessing both the potential impact of the risks identified as well as the resilience of the financial sector. Finally, the chapter suggests a policy response process and also briefly discusses the supervisory response to systemic risk underway in the United States.

Introduction

Counterparty Risk and Systemic Risk

Over-the-counter (OTC) derivatives are contracts that are privately negotiated and traded directly between two parties. Securities such as swaps, forward rate agreements, and exotic options are typically traded over the counter. The OTC derivative market is the largest market for derivatives, and is largely unregulated. Participants in this market are banks and other relatively sophisticated financial institutions, such as insurance firms, investment companies, and hedge funds. OTC investors tend to be unaware of recent transactions prices and prices that may be currently available in the market. Thus, OTC markets are relatively intransparent.

The outstanding volume of OTC derivatives has grown exponentially over the past two decades, with only a relatively modest slump during the crisis of 2007–09. According to the Bank for International Settlements, the total outstanding notional amount is US$ 601 trillion (as of December 2010). Of this amount, 77% are interest rate contracts, 10% are foreign exchange contracts, 5% are credit default swaps, 1% are equity contracts, 0.5% are commodity contracts, and 6.5% are other.

Derivatives facilitate the sharing of risk among market participants. They also create connections between market participants. One of these connections is counterparty risk. This is the distribution of loss faced by one OTC derivative party due to the other's failure to perform on its contractual obligations. Unlike many other financial risks, counterparty risk is bilateral: both parties to a contract may be exposed, depending on the value of the positions they hold against each other.

Abstract We introduce a simple framework where banks emerge as a response to a natural need in a society of individuals with heterogeneous liquidity preferences. We examine bank failures and the conditions for an interbank market to be established.

We start with an economy consisting of a group of individuals arranged in a 2- dimensional cellular automaton and two types of assets available for investment. Because of uncertainty, individuals might change their investing preferences and accordingly seek their surroundings neighbors as trading partners to satisfy their new preferences. We demonstrate that the individual uncertainty regarding preference shocks coupled with the possibility of not finding suitable trading partners when needed give rise to banks as liquidity providers. Using a simple learning process, individuals decide whether or not to join the banks, and through a feedback mechanism we illustrate how banks get established in society. We then show how the same uncertainty in individual investing preferences that gave rise to banks also causes bank failures. In the second level of our analysis, in a similar fashion, banks are treated as agents and use their own learning process to avoid failures and create an interbank market.

In addition to providing a bottom up model for the formation of banks and interbank markets, our model allows us to address under what conditions bank oligopolies and frequent banks failures are to be observed, and when an interbank market leads to a more stable system with fewer failures and less concentrated market players.

Statistics and Systemic Risk

Statistical methods have long played a critical role in quantifying the risks inherent in various activities and they will undoubtedly continue to play an important role going forward. The challenge with regards to using statistical methods to quantify system-wide risks, inherent in a financial system, is that the use of historical, transaction data only allows for the calibration of reduced form models. These models assume that the system is stable and that structural changes (dramatic changes in resources or incentives of the participants are stable over time). As we know, the underlying structure of the financial system can change dramatically, especially during times of stress.

To illustrate this difference, consider the challenge of modeling foot traffic at an indoor shopping mall, where individuals have to pay to enter and exit one of the many mall doors. To model the movement of shoppers, we could build a purely statistical (reduced form) model of the door traffic, and for most situations this would be sufficient. However, in extreme cases (e.g. if there was an explosion in a store) the system would dramatically change. Shoppers would rush for the nearest doors and ticket sellers would get overwhelmed and close their doors; creating the equivalent of a fire-sale or a liquidity crisis. Then shoppers would rush to the next set of doors. In these cases a statistical model would get it horribly wrong.

Abstract FpML is the industry-standard protocol for complex financial products. It is based on XML (eXtensible Markup Language), the standard meta-language for describing data shared between applications. This chapter describes FpML and shows how it can be used as a language for expressing financial contracts. If financial contracts are expressed in text, they are very difficult to reason with computationally. Yet, computer-driven analysis of complex networks of contracts, and long chains of dependencies in financial events, is likely to be important for systemic risk assessment. This chapter suggests how FpML can be helpful in this regard.

Introduction

The ‘Financial products Markup Language’ (FpML) is an open standard based on XML and XML Schema technology which has been developed by the financial industry to help streamline the processing of ‘Over the Counter’ (OTC) derivative transactions. These transactions are a significant component of today's capital markets and a major source of information technology investment for the firms involved since they carry high monetary value and risk.

Derivatives products

A derivative is a contract which takes a property of another financial instrument, for example an interest rate, a foreign exchange rate or a stock price, and uses it to define a series of actual or potential payouts that will occur over the life of the contract.