The paper uses a graph model to examine the effects of financial market regulations on systemic risk. Focusing on central clearing, we model the financial system as a multigraph of trade and risk relations among banks. We then study the impact of central clearing by a priori estimates in the model, stylized case studies, and a simulation case study. These case studies identify the drivers of regulatory policies on risk reduction at the firm and systemic levels. The analysis shows that the effect of central clearing on systemic risk is ambiguous, with potential positive and negative outcomes, depending on the credit quality of the clearing house, netting benefits and losses, and concentration risks. These computational findings align with empirical studies, yet do not require intensive collection of proprietary data. In addition, our approach enables us to disentangle various competing effects. The approach thus provides policymakers and market practitioners with tools to study the impact of a regulation at each level, enabling decision-makers to anticipate and evaluate the potential impact of regulatory interventions in various scenarios before their implementation.

We link partisan and racial gerrymandering to electoral and legislative outcomes. Candidates compete in primaries and general elections, offering ideological and distributive benefits to heterogeneous voters. Redistricting generates systematic trade-offs: concentrated minority districts increase descriptive representation but reduce distributive leverage, while dispersed minorities gain substantive representation as pivotal swing groups but risk losing preferred candidates. Crossover voting by nonminority voters introduces sharp non-linearities; it enhances minority electoral success while increasing majority influence over policy. We document a U-shaped relationship between minority influence and minority concentration. Our framework offers a unified foundation for the paradox of packing, cracking, and minority representation.

We apply an artificial intelligence approach to simulate the impact of financial market regulations on systemic risk-a topic vigorously discussed since the financial crash of 2007-09. Experts often disagree on the efficacy of these regulations to avert another market collapse, such as the collateralization of interbank (counterparty) derivatives trades to mitigate systemic risk. A limiting factor is the availability of proprietary bank trading data. Even if this hurdle could be overcome, however, analyses would still be hampered by segmented financial markets where banks trade under different regulatory systems. We therefore adapt a simulation technology, combining advances in graph theoretic models and machine learning to randomly generate entire financial systems derived from realistic distributions of bank trading data. We then compute counterparty credit risk under various scenarios to evaluate and predict the impact of financial regulations at all levels-from a single trade to individual banks to systemic risk. We find that under various stress testing scenarios collateralization reduces the costs of resolving a financial system, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations. Moreover, the concentration of credit risk does not necessarily correlate monotonically with systemic risk. While the analysis focuses on counterparty credit risk, the method generalizes to other risks and metrics in a straightforward manner.

The G20's push towards central clearing changed the shape of the world's financial system: all standardized derivative contracts must now be cleared through central counterparties (CCPs). Despite considerable debate, the impact of central clearing nonetheless remains ambiguous and hard to measure as clearing regulations have been implemented alongside many other changes. In the present paper, we isolate the impact of CCPs by first representing all trade and risk relations of a financial system in a graph model. We then formalize clearing as an operator on those graphs and obtain sharp a priori bounds of its effect on total risk levels. Using numerical simulation, we then show how clearing alters the credit risk exposures of each bank depending on the netting structure of its trades. Further, we demonstrate how CCPs only reduce the total levels of risk in the system if their credit quality is substantially higher than that of the banks. We show, paradoxically, how the CCPs expose the system to substantial concentration risk and thereby undermine their initial purpose.

This paper analyzes the design of financial regulatory structure in the European Union. We develop a two-pronged approach to track changes in decision-making authority in EU financial market regulations and directives enacted from 1964 to the present. Traditional observational data collection methods manually code laws to identify the amount of discretionary authority delegated to regulatory bodies that oversee segments of financial markets. The lack of robustness and scalability of this approach, however, may limit the generalizability of observational studies. To remedy these potential shortcomings, we match observational methods with data science techniques, in particular natural language processing, to visualize complex patterns in the text of laws and temporal movements. The combination of both observational and computational approaches provides more detailed insights of the various elements of financial regulatory structure and the temporal allocation of decision-making authority among the European Commission, regulatory agencies and the Members States. Our analysis indicates that both the scope and location of decision-making authority shifted over time, moving from Member States to EU regulatory agencies. The amount of discretionary authority delegated to EU agencies to implement regulations, on the other hand, has remained largely unchanged.Issue editors: Françoise Roure and Serge Catoire

In this chapter, we simulate and analyze the impact of financial regulations concerning the collateralization of derivative trades on systemic risk—a topic that has been vigorously discussed since the financial crisis in 2007/08. Experts often disagree on the efficacy of these regulations. Compounding this problem, banks regard their trade data required for a full analysis as proprietary. We adapt a simulation technology combining advances in graph theory to randomly generate entire financial systems sampled from realistic distributions with a novel open-source risk engine to compute risks in financial systems under different regulations. This allows us to consistently evaluate, predict, and optimize the impact of financial regulations on all levels—from a single trade to systemic risk—before it is implemented. The resulting data set is accessible to contemporary data science techniques like data mining, anomaly detection, and visualization. We find that collateralization reduces the costs of resolving a financial system in crisis, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations.

We explore the determinants of market regulation with an analysis of the policy-making process in which the legislature delegates authority to an executive agency and special interests can lobby the executive agency. We discuss how the mere threat of administrative lobbying by the industry may be sufficient to induce the agency to set policies preferred by the industry. Our analysis also shows that policy conflict, the difference between the legislature’s preferred policy and the agency’s implemented policy, is increasing in the agency’s vulnerability to lobbying but decreasing in the interest group’s lobbying cost when the legislature prefers more extreme policies. Administrative lobbying either amplifies or mitigates the conflict between the legislature and the agency. Relatedly, our analysis shows that the “ally principle” does not hold and the legislature prefers an agency that is slightly more biased against the industry. The legislature delegates greater discretion to the agency when policy uncertainty is higher, when policy conflict between the legislature and the agency is a lower, and when administrative lobbying mitigates the policy conflict between legislature and agency.

The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate policy processes at a scale unimaginable even a few years ago. We apply these scalable NLP and ML techniques to analyze the United States Government's regulation of the banking and financial services sector. First, we employ NLP techniques to convert the text of financial regulation laws into feature vectors and infer representative "topics" across all the laws. Second, we apply ML algorithms to the feature vectors to predict various attributes of each law, focusing on the amount of authority delegated to regulators. Lastly, we compare the power of alternative models in predicting regulators' discretion to oversee financial markets. These methods allow us to efficiently process large amounts of documents and represent the text of the laws in feature vectors, taking into account words, phrases, syntax, and semantics. The vectors can be paired with predefined policy features, thereby enabling us to build better predictive measures of financial sector regulation. The analysis offers policymakers and the business community alike a tool to automatically score policy features of financial regulation laws to and measure their impact on market performance.

In this paper, we simulate and analyze the impact of financial regulations concerning the collateralization of derivative trades on systemic risk. We represent a financial system using a weighted directed graph model. We enhance a novel open source risk engine to automatically classify a financial regulation for its impact on systemic risk. The analysis finds that introducing collateralization does reduce the costs of resolving a financial system in crisis. It does not, however, change the distribution of risk in the system. The analysis also highlights the importance of scenario based testing using hands on metrics to quantify the notion of system risk.

Banking & Financial Services Policy Report •

Over the past three decades, antitrust laws have proliferated across the globe. International institutions and governments have promoted antitrust policy as an important regulatory tool to enhance economic performance. At the same time, we have scant empirical evidence on whether these policies actually work. Do they foster market competition, with the ensuing benefits of greater efficiency, economic prosperity and innovation? In other words, is the adoption of these antitrust laws good public policy and an efficient use of scarce public resources? Our research seeks to provide a theoretical and empirical foundation to these questions. We develop a novel dataset on antitrust laws and enforcement across time and jurisdictions that accounts for political and institutional nuances, and link these variables to patent data. This enables us to construct robust measures quantifying antitrust regimes and, for the first time, systematically test alternative hypotheses on the impact of antitrust policy on innovation. While our research focuses on antitrust policy, this project provides an analytical foundation for thinking about the determinants of regulatory policies and their relative ability to contribute to more innovative and competitive markets and, ultimately, to greater social welfare.

The development of computational data science techniques in natural language processing and machine learning algorithms to analyze large and complex textual information opens new avenues for studying the interaction between economics and politics. We apply these techniques to analyze the design of financial regulatory structure in the United States since 1950. The analysis focuses on the delegation of discretionary authority to regulatory agencies in promulgating, implementing, and enforcing financial sector laws and overseeing compliance with them. Combining traditional studies with the new machine learning approaches enables us to go beyond the limitations of both methods and offer a more precise interpretation of the determinants of financial regulatory structure.

The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate processes, such as government regulation of financial markets, at a scale unimaginable even a few years ago. This paper develops scalable NLP and ML algorithms (classification, clustering and ranking methods) that automatically classify laws into various codes/labels, rank feature sets based on use case, and induce best structured representation of sentences for various types of computational analysis. The results provide standardized coding labels of policies to assist regulators to better understand how key policy features impact financial markets.

We analyze the institutional determinants of U.S. financial market regulation with a general model of the policy-making process in which legislators delegate authority to regulate financial risk at both the firm and systemic levels. The model explains changes in U.S. financial regulation leading up to the financial crisis. We test the predictions of the general model with a novel, comprehensive data set of financial regulatory laws enacted specifically between 1950 and 2009. The theoretical and empirical analysis finds that economic and political factors impact Congress' decision to delegate regulatory authority to executive agencies, which in turn impacts the stringency of financial market regulation, and our estimation results indicate that political factors may have been stronger and resulted in inefficiencies.

We analyze the process of democratization in a polity with groups that are divided along ethnic as well as economic lines. We show that: (i) the presence of ethnic minorities, in general, makes peaceful democratic transitions less likely; (ii) minorities suffer from discriminatory policies less in democracies with intermediate levels of income inequality; and (iii) in new democracies with low levels of income inequality, politics is divided along ethnic lines, and at greater levels of inequality economic cleavages predominate.