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This paper presents a three-period model featuring a short-term investor in the over-the-counter bond market. A short-term investor stores cash because of a need to pay cash at some future date. If a short-term investor buys bonds, then a deadline for retrieving cash lowers the resale price of bonds for the investor through bilateral bargaining in the bond market. Ex-ante, this holdup problem explains the use of a repo by a short-term investor, the existence of a haircut, and the vulnerability of a repo market to counterparty risk. This result holds without any uncertainty about bond returns or asymmetric information.

We empirically investigate the short- and long-run dynamic relationship between security broker–dealer leverage and stock prices. To explore various potential adjustment dynamics, we use the error correction term to construct several threshold indicators that distinguish the deviation from long-run equilibrium. Using these indicators, we estimate different types of threshold cointegration and error correction models and analyze the results in the context of boom and bust cycles in financial markets. Our results show that, in the long run, a 1% increase in the stock price leads to approximately a 0.43% increase in leverage. We show that a momentum autoregressive model with an endogenous threshold does a better job of fitting the financial data. The asymmetric error correction structure shows that changes in leverage experience significant corrective measures when the stock prices plunge during recessions.

Purpose This paper aims to measure the trade price impact of a recent regulatory disclosure intervention in municipal securities secondary markets, which required broker-dealers to disclose securities trading information on a near-real-time and continuing basis. Design/methodology/approach The author analyzes trade price outcomes in the preintervention and postintervention regimes using a suite of time series estimations that give heteroskedasticity-robust standard errors (Prais–Winsten and Cochrain–Orcutt), accommodate higher-order lag structure in the error term (autoregressive integrated moving average) and account for volatility clustering in the time series (generalized autoregressive conditional heteroskedasticity). Findings Results show that regulatory disclosure intervention significantly improved trade price efficiency in municipal securities secondary markets as daily trade price differential and volatility both declined market-wide after the disclosure intervention. Research limitations/implications The sample consists of trades in State of California general obligation bonds; therefore, empirical findings may not be generalizable to other states, local governments and different types of bonds. Practical implications The findings highlight voluntary information disclosure as a practical and effective mechanism in disclosure regulation of municipal securities secondary markets. Originality/value Only a small body of work exists that examines information disclosure regulation in municipal securities secondary markets; therefore, this paper expands knowledge on the topic and should provide renewed impetus for regulatory efforts aimed at improving the efficiency of municipal capital markets.

Municipal bonds trade in opaque, decentralized broker-dealer markets in which price information is costly to gather. We analyze a database of trades between broker-dealers and customers in municipal bonds. These data were only released to the public with a lag; the market was opaque. Dealers earn lower average markups on larger trades, even though dealers bear a higher risk of losses with larger trades. We estimate a bargaining model and compute measures of dealer's bargaining power. Dealers exercise substantial market power. Our measures of market power decrease in trade size and increase in the complexity of the trade for the dealer.

This paper employs linear and nonlinear causality tests to examine (for the first time) the dynamic relation between broker-dealer leverage and the stock market in the United States, using quarterly data since 1967. We find significant linear causality from the stock market to broker-dealer leverage and a nonlinear feedback from broker-dealer leverage to the stock market, supporting the view that the macro economy is highly nonlinear. This bidirectional causality shows that a stock market crash might happen long before a fall in fundamental asset values.

Dealer banks--that is, large banks that deal in securities and derivatives, such as J. P. Morgan and Goldman Sachs--are of a size and complexity that sharply distinguish them from typical commercial banks. When they fail, as we saw in the global financial crisis, they pose significant risks to our financial system and the world economy.How Big Banks Fail and What to Do about Itexamines how these banks collapse and how we can prevent the need to bail them out. In sharp, clinical detail, Darrell Duffie walks readers step-by-step through the mechanics of large-bank failures. He identifies where the cracks first appear when a dealer bank is weakened by severe trading losses, and demonstrates how the bank's relationships with its customers and business partners abruptly change when its solvency is threatened. As others seek to reduce their exposure to the dealer bank, the bank is forced to signal its strength by using up its slim stock of remaining liquid capital. Duffie shows how the key mechanisms in a dealer bank's collapse--such as Lehman Brothers' failure in 2008--derive from special institutional frameworks and regulations that influence the flight of short-term secured creditors, hedge-fund clients, derivatives counterparties, and most devastatingly, the loss of clearing and settlement services. How Big Banks Fail and What to Do about Itreveals why today's regulatory and institutional frameworks for mitigating large-bank failures don't address the special risks to our financial system that are posed by dealer banks, and outlines the improvements in regulations and market institutions that are needed to address these systemic risks.

For the average investor trying to save for retirement or a child's college fund, the world of investing has become increasingly complex. These retail investors must turn more frequently to financial intermediaries, such as broker-dealers and investment advisers, to get sound investment advice. Such intermediaries perform different duties for their clients, however. The investment adviser owes his client a fiduciary duty of care and therefore must provide financial advice that is in the client's best interests, while the broker-dealer must merely provide advice that is suitable to the client's interests—a lower standard than the fiduciary duty of care. And yet these divergent standards are not necessarily evident to the average investor. As a result, investors run the risk of being placed into suboptimal investments by broker-dealers. Two competing solutions to this issue have been proposed: a higher fiduciary standard for both broker-dealers and investment advisers and a less burdensome disclosure standard in which broker-dealers would inform their clients that they are not fiduciaries. This Note analyzes these potential reforms using a traditional economics analysis as well as new behavioral-economics research. It concludes that a fiduciary standard more effectively protects investors and that a disclosure standard would actually have the perverse effect of making investors more susceptible to behavioral biases that can impair their ability to invest properly.

The efficiency, safety, and soundness of financial markets depend on the operation of core infrastructure--exchanges, central counter-parties, and central securities depositories. How these institutions are governed critically affects their performance. Yet, despite their importance, there is little certainty, still less a global consensus, about their governance.Running the World's Marketsexamines how markets are, and should be, run. Utilizing a wide variety of arguments and examples from throughout the world, Ruben Lee identifies and evaluates the similarities and differences between exchanges, central counter-parties, and central securities depositories. Drawing on knowledge and experience from various disciplines, including business, economics, finance, law, politics, and regulation, Lee employs a range of methodologies to tackle different goals. Conceptual analysis is used to examine theoretical issues, survey evidence to describe key aspects of how market infrastructure institutions are governed and regulated globally, and case studies to detail the particular situations and decisions at specific institutions. The combination of these approaches provides a unique and rich foundation for evaluating the complex issues raised. Lee analyzes efficient forms of governance, how regulatory powers should be allocated, and whether regulatory intervention in governance is desirable. He presents guidelines for identifying the optimal governance model for any market infrastructure institution within the context of its specific environment. Running the World's Marketsprovides a definitive and peerless reference for how to govern and regulate financial markets

This chapter explores the structure of exchange‐traded products (ETPs), including exchange‐traded notes (ETNs) and, especially, exchange‐traded funds (ETFs). ETFs are regulated by multiple divisions of the Securities and Exchange Commission (SEC); and the 1940 Act, 1934 Act, and 1933 Act (all as defined below) play a role in their operation. Because ETFs operate in a manner not contemplated by these acts, ETFs need exemptive relief to operate. They also must comply with exchange listing rules and unique tax treatment; international and fixed‐income ETFs have additional requirements. Finally, the chapter briefly discusses credit limits at broker‐dealers and market closures.

This chapter discusses mitigation of risks associated with market impact (MI) that can be used by both HFTs and other market participants. It also illustrates algorithmic execution, also known as algo execution, which refers to a set of programmatic computer methodologies used to determine the optimal way to parcel and execute an order. An ideal execution algo would consistently execute the customer's buy order at the lowest price available. Given the difficulties of precisely pinpointing the price lows and the highs within a period of time, a good algo produces a certain price improvement according to prespecified optimality conditions. The optimality conditions may be based on the trader's risk aversion, concurrent market state, the benchmark chosen by the trader. Additionally, execution algorithms have become essential for all investors, as it help traders accumulate or liquidate large positions by breaking up orders into pieces, and reducing market impact and visibility of orders.