ABSTRACT Cross‐asset order flow provides an incremental and novel nonlinear price discovery channel. Structural vector autoregressions of synchronized intraday message data reveal distinct patterns in the comovement of order flow and its influence on returns and volatility. While cross‐market order flow usually reconciles prices through small‐stakes arbitrage in periods of low volatility and comovement during medium volatility associated with information arrival, it can exacerbate price dislocation from fundamental values during extraordinary volatility. While applying market‐wide circuit breakers (MWCB) mitigates the extreme negative spillovers by jointly halting markets, we identify room for further harmonization during the MWCB market reopening process.

Repurchase agreement (repo) markets represent one of the largest sources of funding and risk transformation in the U.S. financial system. Despite the large volume, repo rates can be quite volatile, and in the extreme, they have exhibited intraday spikes that are 5-10 times the rate on a typical day. This paper uses a unique combination of intraday timing data from the repo market to examine the potential causes of the dramatic spike in repo rates in mid-September 2019. We conclude that the spike resulted from a confluence of factors that, when taken individually, would not have been nearly as disruptive. Our work highlights how a lack of information transmission across repo segments and internal frictions within banks most likely exacerbated the spike. These findings are instructive in the context of repo market liquidity, demonstrating how the segmented structure of the market can contribute to its fragility.

Central counterparty (CCP) default waterfalls act as the last lines of defense in over-the-counter markets by managing and allocating resources to cover payment defaults. This article examines the impact of variations in waterfall design on financial system losses in the presence of payment network dependencies and frictions in the cleared and noncleared portion of the system. Through the development of a structural model, we draw several theoretical conclusions about the effectiveness of CCP default waterfalls under severe payment stress. These findings are empirically quantified by testing the model using supervisory data for the U.S. credit default swap market.

We propose a general framework for empirically assessing a central counterparty's capacity to cope with severe financial stress. Using public disclosure data for global central counterparties (CCPs), we show how to estimate the probability that a CCP could cover any specified fraction of payment defaults by its members. This framework supplements conventional standards of assessing risk protection across CCPs that is not predicated on a specific number of member defaults. We apply the approach to a wide range of CCPs in different geographical jurisdictions and asset classes and find that there are substantial differences in protection coverage. In particular, large European CCPs appear to be significantly safer than their counterparts in Asia-Pacific and North America. These differences are also reflected in supervisory data that provide CCP members' risk assessments of the CCPs to which they belong.

Market participants have often noted that general collateral (GC) repo trades happen very early in the morning, with most activity being completed soon after markets open at 7 a.m. Data on intraday repo volumes timing are not publicly available however, obscuring those dynamics to outside observers. In this post, we use confidential data collected by the Office of Financial Research (OFR) to describe the intraday timing dynamics of GC repo in the interdealer market. We demonstrate that a significant majority of interdealer overnight Treasury repo is completed prior to 8:30 a.m. (all times Eastern time), and explore the various factors that are driving repo traders to secure funding in the early morning.

The overnight segment of the triparty repurchase agreement (repo) market plays a pivotal role in the normal functioning of the U.S. financial system by acting as an important source of secured short-term funding and supporting the liquidity of key fixed income markets, including U.S. Treasury and agency securities. This over-the-counter market accounts for over $1 trillion in daily transactions and provides a unique venue in which a diverse set of market participants invest their cash as well as obtain short-term funding.

We propose a general framework for estimating the vulnerability to default by a central counterparty (CCP) in the credit default swaps market. Unlike conventional stress testing approaches, which estimate the ability of a CCP to withstand nonpayment by its two largest counterparties, we study the direct and indirect effects of nonpayment by members and/or their clients through the full network of exposures. We illustrate the approach for the U.S. credit default swaps market under shocks that are similar in magnitude to the Federal Reserve's stress tests. The analysis indicates that conventional stress testing approaches may underestimate the potential vulnerability of the main CCP for this market.

A major credit shock can induce large intraday variation margin payments between counterparties in derivatives markets, which may force some participants to default on their payments. These payment shortfalls become amplified as they cascade through the network of exposures. Using detailed Depository Trust & Clearing Corporation data, we model the full network of exposures, shock-induced payments, initial margin collected, and liquidity buffers for about 900 firms operating in the U.S. credit default swaps market. We estimate the total amount of contagion, the marginal contribution of each firm to contagion, and the number of defaulting firms for a systemic shock to credit spreads. A novel feature of the model is that it allows for a range of behavioral responses to balance sheet stress, including delayed or partial payments. The model provides a framework for analyzing the relative effectiveness of different policy options, such as increasing margin requirements or mandating greater liquidity reserves. This paper was accepted by Karl Diether, finance.

The potential impact of interconnected financial institutions on interbank financial systems is a financial stability concern for central banks and regulators. A number of algorithms/methods have been developed to extrapolate latent interbank risk exposures. However, most use highly stylized network models and reconstruction methods with global optimality lending allocation approaches such as maximizing entropy or minimizing costs. This paper argues that U.S. bank lending and borrowing decisions are largely suboptimal and performance-driven. We present an agent-based model to endogenously reconstruct interbank networks based on 6,600 banks' decision rules and behaviors reflected in quarterly balance sheets. The model formulation reproduces dynamics similar to those of the 2007-09 financial crisis and shows how bank losses and failures arise from network contagion and lending market illiquidity. When calibrated to post-crisis data from 2011-14, the model shows the banking system has reduced its likelihood of bank failures through network contagion and illiquidity, given a similar stress scenario.

The National Banking Acts (NBAs) of 1863–1864 established rules governing the amounts and locations of interbank deposits, thereby reshaping the bank networks. Using unique data on bank balance sheets and detailed interbank deposits in 1862 and 1867 in Pennsylvania, we study how the NBAs changed the network structure and quantify the effect on financial stability in an interbank network model. We find that the NBAs induced a concentration of interbank deposits at both the city and bank levels, creating systemically important banks. Although the concentration facilitated diversification, contagion would have become more likely when financial center banks faced large shocks. (JEL E44, G01, G21, G28, L14, N21)

In over-the-counter markets, dealers facilitate trading by becoming market makers. The costs dealers face, including the cost of holding inventory on balance sheet, and the ease, or difficulty, of reducing their positions, determine the degree of liquidity they provide. We provide a stylized model to examine the implications of these costs on dealer behavior and market liquidity. We use the model to guide an empirical study of the single-name credit default swap (CDS) market between 2010-2016. We find that transaction prices between dealers and clients have progressively become more dependent on the inventories of individual dealers rather than on the aggregate inventory across all dealers. We also find that the volume between clients and dealers decreases across all clients, with larger declines for clients that are depository institutions. At the same time, the volume of interdealer trades decreases, dealer inventories decline, and dealers with large inventories are more likely to trade with clients. Our results are consistent with the view that regulatory reforms implemented following the 2007-09 financial crisis increased the cost of holding inventory for dealers, and the cost of interdealer trading.

Cross-asset market activity can be a channel through which illiquidity risks originating in one market can propagate to others. This paper examines the complex intra-day linkages between the U.S. equity securities market and the equity derivatives market using high-frequency data on S&P 500 index exchange-traded funds and E-mini futures contracts. The paper finds a positive, but short-lived, relationship between the two markets' order flow activities, which relates to the supply, demand, and withdrawal of liquidity between the two markets. The paper also finds that cross-asset market order flow is a key component of liquidity and price discovery, particularly during periods of market volatility.

In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending market. We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 U.S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literature. A key finding of this study is that risk preferences at the individual bank level can lead to unique interbank market structures that are suggestive of the capacity with which the market responds to surprising shocks.

This study addresses a critical regulatory shortfall by developing a platform to extend stress testing from a microprudential approach to a dynamic, macroprudential approach. This paper describes the ensuing agent-based model for analyzing the vulnerability of the financial system to asset- and funding-based fire sales. The model captures the dynamic interactions of agents in the financial system extending from the suppliers of funding through the intermediation and transformation functions of the bank/dealers to the financial institutions that use the funds to trade in the asset markets. The model replicates the key finding that it is the reaction to initial losses, rather than the losses themselves, that determine the extent of a crisis. By building on a detailed mapping of the transformations and dynamics of the financial system, the agent-based model provides an avenue toward risk management that can illuminate the pathways for the propagation of key crisis dynamics such as fire sales and funding runs.

In this study, we propose a multi-agent model to examine bank lending and borrowing risk behaviors and their implications to interbank market dynamics. Using data from 2001 to 2014 that covers around U.S. 6600 banks, we model individual bank decisions using the temporal difference reinforcement learning algorithm based on banks’ lending preferences and environment, and we then generate the interbank market dynamics from the empirical data. This dynamic model allows us to construct interbank networks as they change with bank risk preferences, and thus facilitates the analysis of the banking systems stability. The model successfully replicates the key characteristics of interbank lending and borrowing relationships that have been documented in the recent literature. A key finding of this study is that the risk aversion choice of individual bank leads to unique interbank market structures that suggest the macro risk preference of the market. Combined with the use of balance sheet data, this modeling framework helps central banks and regulators building more functional models for examining the interbank market stability problems.

Using an agent-based model of the limit order book, we explore how the levels of information available to participants, exchanges, and regulators can be used to improve our understanding of the stability and resiliency of a market. Ultimately, we want to know if electronic market data contains previously undetected information that could allow us to better assess market stability. Using data produced in the controlled environment of an agent-based model’s limit order book, we examine various resiliency indicators to determine their predictive capabilities. Most of the types of data created have traditionally been available either publicly or on a restricted basis to regulators and exchanges, but other types have never been collected. We confirmed our findings using actual order flow data with user identifications included from the CME (Chicago Mercantile Exchange) and New York Mercantile Exchange. Our findings strongly suggest that high-fidelity microstructure data in combination with price data can be used to define stability indicators capable of reliably signaling a high likelihood for an imminent flash crash event about one minute before it occurs.

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Electronic markets and automated trading have resulted in a drastic increase in the quantity and complexity of regulatory data. Reconstructing the limit order book and analyzing order flow is an emerging challenge for financial regulators. New order types, intra-market behavior, and other exchange functionality further complicate the task of understanding market behavior at multiple levels. Data visualizations have proven to be a fundamental tool for building intuition and enabling exploratory data analysis in many fields. In this paper, we propose the incorporation of visualizations in the workflow of multiple financial regulatory roles, including market surveillance, enforcement and supporting academic research.

U.S. supervisory stress tests to date have focused on the resilience of large banks to withstand the direct effects of credit and trading shocks. Using data from Depository Trust & Clearing Corporation (DTCC), we apply the Federal Reserve's Comprehensive Capital Analysis and Review (CCAR) supervisory scenarios to evaluate the default of a bank's largest counterparty. We find that indirect effects of this default, through the bank's other counterparties, may be larger than the direct impact on the bank. Further, when taken as a whole, the core banking system has a higher exposure concentration to a single counterparty than does any individual bank holding company. We find that the U.S. banking system's counterparty exposure concentration has risen over the 2013–2015 period. Under the 2015 CCAR this corresponds to a market diversity with just over three counterparties under stress. Our results are the first to evaluate the U.S. credit derivatives market under stress and underscore the importance of a macroprudential perspective on stress testing.