Jeremy Bejarano

Research

Working Papers

Negative Treasury Haircuts (2025)

with Lina Lu (Federal Reserve Bank of Boston) and Jonathan Wallen (Harvard Business School)
HBS Working Paper 26-034

We study the supply of leverage in the Treasury market by large dealer banks. On average, leverage is greater than 100%. Negative haircuts are inconsistent with canonical theories of collateralized lending. For dealer banks, bundling counterparty credit risk with Treasury repo is an attractive regulatory arbitrage. We show that the balance sheet capacity of dealer banks is an important driver of their supply of leverage. Their ability to warehouse Treasuries on balance sheet enables them to avoid fire sales and lend at higher leverage. This supply of leverage is cyclical and increases the fragility of the Treasury market.

An Open Benchmark for Evaluating Time Series Forecasting Methods across Financial Markets (2026)

with Viren Desai, Kausthub Keshava, Arsh Kumar, Zixiao Wang, Vincent Hanyang Xu, and Yangge Xu
Working Paper

Financial regulators and researchers have emphasized forward-looking risk monitoring to address systemic vulnerabilities. This paper benchmarks state-of-the-art global time series forecasting methods, which have proven superior in the time series literature, on a wide-ranging suite of financial datasets. Benchmarks drive progress, and our systematic evaluation of over a dozen forecasting methods ranging from classical models to modern deep learning architectures reveals which approaches best capture early warning signals across different market segments. We evaluate these methods on critical financial stability metrics including arbitrage basis spreads that signal funding market stress, banking indicators that reveal institutional vulnerabilities, and asset returns across multiple markets. To enable reproducible research and continuous improvement in financial forecasting, we develop an open-source financial time series forecasting repository that standardizes these datasets according to canonical academic methodologies. Our results provide financial stability authorities with evidence-based guidance on which forecasting approaches most reliably detect emerging risks in specific market segments, directly enhancing the toolkit for macroprudential surveillance and systemic risk monitoring. Consistent with decades of empirical finance, returns remain extremely difficult to forecast, yet machine-learning-based global models yield meaningful accuracy gains for basis spreads, liquidity metrics, and other supervisory indicators where classical baselines fall short.

The Transition to Alternative Reference Rates in the OFR Financial Stress Index (2023)

OFR Working Paper 23-07

The OFR Financial Stress Index (OFR FSI) is a daily market-based snapshot of stress in global financial markets that is constructed from 33 financial market variables. As of the time of writing, seven of these variables rely on obsolete reference rates. Since its inception, the OFR FSI was intended to allow for the periodic replacement of obsolete variables as the need arises. In this paper, I introduce replacements for these seven obsolete variables, and I make explicit the procedure with which the OFR FSI incorporates these new variables. Furthermore, I demonstrate generally that this replacement procedure produces an index with the following desirable properties: (1) the index is a weighted sum of the presently included variables; (2) removed variables no longer directly affect the index, and newly included variables do not modify historical values of the index; (3) the index uses all available historical data on the newly included variables to train the model; and (4) the volatility of the index is roughly comparable before and after the replacement.

Dissertation Chapters

Characterizing the Role of Dividend Dynamics in the Term Structure of Equity Risk Premia

(Dissertation Chapter 1)

I characterize the relationship between dividend dynamics and the term structure of equity risk premia. Within a class of log-linear asset pricing models, I show that the risk exposure associated with dividend futures is equal to the impulse response function of dividends and that the average slope of the term structure depends on the relationship between the permanent and transitory components of dividends. Going beyond the class of log-linear models, I then explore the consequences of adding a transitory, mean-reverting component to dividend dynamics within several classic asset pricing models, such as the extended consumption capital asset pricing model and an external habits model. Recent empirical evidence suggests that the term structure of equity may be downward sloping on average, which is at odds with the traditional specification of many common asset pricing models. I show that this potential discrepancy can be reconciled by adjusting cash flow growth dynamics in the proposed way.

Sectoral Shifts, Production Networks, and the Term Structure of Equity

(Dissertation Chapter 2)

I argue that the term structure of equity as characterized by expected holding period returns on dividend strips can be used as a diagnostic to evaluate the quantity dynamics that arise in a macroeconomic model. For instance, as shown in the first chapter, the risk exposures associated with dividend futures are equal to the impulse responses of aggregate consumption with respect to the underlying shocks. As an application, I derive the asset pricing implications of a multi-sector production network model and use this to shed light on relative importance of idiosyncratic and aggregate total factor productivity (TFP) shocks. Though aggregate TFP in the U.S. over the last 60 years has grown approximately 1.4 percent annually, these gains have been dispersed across individual sectors, with some sectors even seeing substantial declines. This dispersion is either the result of idiosyncratic sectoral shocks or aggregate shocks that shift the composition of the economy without necessarily affecting long-run aggregate output. Decomposing the contribution of each shock to this term structure of equity, I show that the shift shocks contribute to a downward sloping term structure of equity while others contribute to an upward sloping term structure. Thus, imposing a downward sloping term structure in this model amounts to putting a lower bound on the contribution of aggregate shifts relative to other shocks.

Teaching

Course Material

Here I include material that I developed for courses that I have taught in the past.

FINM 32900: Full-Stack Quantitative Finance

University of Chicago, Financial Mathematics Program (Winter 2024, 2025, 2026)
[Syllabus] [Online Textbook] [Course Evaluations]

"Full Stack Quantitative Finance" is a hands-on course centered on a core set of fundamental tools common across financial computing and data science. This course examines elements of the analytical pipeline, from data extraction and cleaning to exploratory analysis, visualization, and modeling, and finally, publication and deployment. It does so with the aim of teaching the tools and principles behind creating reproducible and scalable workflows, including build automation, dependency management, unit testing, the command-line environment, shell scripting, Git for version control, and GitHub for team collaboration. These skills are taught through case studies, each of which will additionally give students practical experience with key financial data sets and sources such as CRSP and Compustat for pricing and financials, macroeconomic data from FRED and the BEA, bond transactions from FINRA TRACE, Treasury auction data from TreasuryDirect, textual data from EDGAR, and high-frequency trade and quote data from NYSE.

ECON 21410: Computational Methods in Economics

University of Chicago (Spring 2018, 2019)
[Course Evaluations]

This course introduces the basic programming and computational techniques necessary for solving and estimating economic models. The course covers topics in numerical methods, such as optimization, function approximation, and Monte Carlo techniques, as well as topics in data exploration.

Parallel Programming with Python and MPI

BYU Computational Economics Bootcamp (2013)
[Online Text]

This is the material, including a short text I wrote and video tutorials, for a workshop on parallel programming with MPI and Python that I taught as part of a summer bootcamp on computational economics in 2013.

Miscellaneous

Interactive Plot and Widget Demo. Here I present examples of the kinds of interactive plots and widgets that can be easily embedded into a website using tools that work well with or are based in the Python/R ecosystem.
Jupyter Notebook: Fixed and Random Effects Models in Python, R, and Stata . This should be updated, but some have found this useful. It's a Jupyter notebook in which I replicate some examples from Wooldridge's panel data book. It provides side-by-side code showing how to implement fixed and random effects models in Python (using the statsmodels and linearmodels packages), R, and Stata.