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Fixed Income

Paper Session

Sunday, Jan. 5, 2025 1:00 PM - 3:00 PM (PST)

San Francisco Marriott Marquis, Yerba Buena Salon 9
Hosted By: American Finance Association
  • Kerry Siani, Massachusetts Institute of Technology

Asset Heterogeneity, Market Fragmentation, and Quasi-Consolidated Trading

Wei Li
,
CUNY-Baruch College
Zhaogang Song
,
Johns Hopkins University

Abstract

Significant asset heterogeneity characterizes many over-the-counter (OTC) markets. We first introduce a benchmark model that includes only standard asset-specific (AS) trading and demonstrate that asset heterogeneity fragments the market, thereby diminishing the positive network effects on liquidity. Next, we model a parallel-trading market that also allows quasi-consolidated (QC) trading---where assets with varying values trade at a uniform price---and demonstrate that this parallel-trading market results in higher total trading volume and greater social welfare. However, QC trading's uniform pricing creates a ``cheapest-to-deliver'' effect, hurting liquidity for sellers who do not participate in QC trading. Additionally, these sellers, along with some sellers who participate in QC trading, earn lower profits than they would in the AS-only market. Our model provides a foundation for analyzing liquidity and market design in OTC markets with heterogeneous assets.

Whose Asset Sales Matter?

Rhys Bidder
,
King's College London
Jamie Coen
,
Imperial College London
Caterina Lepore
,
International Monetary Fund
Laura Silvestri
,
Bank of England

Abstract

We study the price impact of sales in bond markets. Using novel data on the transactions of financial firms in corporate and government bonds in the UK, we develop a new measure of non-fundamental selling pressure. We instrument for firms' sales of a bond with their sales of bonds other than the bond in question and exploit within issuer-time variation to identify selling pressure that is unrelated to the bond's fundamentals. The price impact of a sale depends critically on who is selling the asset: sales by dealers and hedge funds generate significantly larger impacts than sales of the same size by other investor types. Our results suggest that more attention should be devoted to risks to financial stability stemming from these impactful sellers.

Hidden Duration: Interest Rate Derivatives in Fixed Income Funds

Jaewon Choi
,
Seoul National University
Minsoo Kim
,
University of Melbourne
Oliver Randall
,
University of Melbourne

Abstract

Fixed income funds carry significant duration risk from their use of interest rate derivatives (IRDs). This duration risk is hidden, as funds have typically disclosed portfolio duration weighted by market values instead of notionals, concealing their true risk. We find substantial variation in IRD duration, both across funds and over time. The primary motive behind funds’ use of IRDs is not to hedge interest rate risk or manage flow risk; rather, they are driven by risk-taking, closing the gap to the duration risk of their peers, and the desire for lower transaction costs. The performance of funds’ IRD positions is not associated with manager skill — funds that outperformed due to high IRD duration in early 2020 performed particularly poorly during interest rate hikes in 2022 and 2023.

Data Uncertainty in Corporate Bonds

Gergana Jostova
,
George Washington University
Stanislava Nikolova
,
University of Nebraska-Lincoln
Alexander Philipov
,
George Mason University

Abstract

We study the impact of data uncertainty in corporate bonds on decision making. We provide a taxonomy of data choices a researcher is compelled to make when constructing a sample of monthly corporate bond returns, and investigate the impact of these choices on the researcher's conclusions. Using momentum as a case study, we show that different but reasonable data choices may lead to conflicting findings about a strategy's profitability and thus result in data uncertainty. We propose a Bayesian decision-making framework for a researcher faced with data uncertainty.

Discussant(s)
Patrick Blonien
,
Carnegie Mellon University
Jian Li
,
Columbia University
Ishita Sen
,
Harvard University
Peter Feldhütter
,
Copenhagen Business School
JEL Classifications
  • G1 - General Financial Markets