Research
My research interests are: Finance, Banking, Macroeconomics, Economic History, Computational Economics and Econometrics.
My research statement: link to latest version (August 2023)
Government Fiscal Capacity and Financial History
"Costs of Financing US Federal Debt Under a Gold Standard: 1791-1933"
with George Hall, Thomas J. Sargent and Bálint Szőke. (Accepted at the Quarterly Journal of Economics.)
Link to latest version (January 2024).
Link to extended older version (July 2022).
Abstract: From a new data set, we infer time series of term structures of yields on US federal bonds during the gold standard era from 1791-1933 and use our estimates to reassess historical narratives about how the US expanded its fiscal capacity. We show that US debt carried a default risk premium until the end of the nineteenth century when it started being priced as an alternative safe-asset to UK debt. During the Civil War, investors expected the US to return to a gold standard so the federal government was able to borrow without facing denomination risk. After the introduction of the National Banking System, the slope of the yield curve switched from down to up and the premium on US debt with maturity less than one year disappeared.
Supplementary materials:
Github repository with bond data. This respository contains the data on prices, quantities, and descriptions of bonds and notes issued by the United States Federal government from 1776 to 1960.
Data description (February 2018). This document describes Pandas DataFrames and the spreadsheets underlying them that contain prices, quantities, and descriptions of bonds and notes issued by the United States Federal government from 1776 to 1960. It contains directions to a public github repository at which DataFrames and other files can be downloaded.
"US Monetary, Financial, and Fiscal Priorities"
with George Hall, Thomas J. Sargent and Bálint Szőke.
Link to latest version (January 2024)
Abstract: US federal governments have confronted trade-offs among lowering borrow- ing costs, maintaining price stability, and maintaining financial stability. During the gold standard era, successive administrations prioritized decreasing government borrowing costs and keeping trend inflation low. Starting with FDR, the government prioritized financial and business cycle stability and was willing to use inflation taxes to lower its debt obligations and redistribute wealth between nominal creditors and debtors. Towards the end of the twentieth century, the government embraced financial deregulation and aggressive inflation targeting. We use our estimates for historical yields and inflation processes to indicate how those changing policy priorities affected or coincided with key macroeconomic correlations. The slope of both the US federal debt yield curve and the “Phillips curve” has changed signs as government priorities have changed.
"Estimating Historical Yield Curves With Sparse Data"
with George Hall, Thomas J. Sargent and Bálint Szőke.
Link to latest version (August 2023).
Abstract: Estimating 19th century US federal bond yield curves involves challenges because few bonds were traded, bonds had peculiar features, government policies changed often, and there were wars. This paper compares statistical approaches for confronting these difficulties and shows that a dynamic Nelson-Siegel model with stochastic volatility and bond-specific pricing errors does a good job for historical US bond prices. This model is flexible enough to interpolate data across periods in a time-varying way without over-fitting. We exploit new computational techniques to deploy our model and estimate yield curves for US federal debt from 1790-1933.
"Convenience Yields and Financial Repression."
with Bálint Szőke.
New draft coming soon.
Abstract: US federal debt plays a special role in the US economy and so gives the US government a funding advantage, often summarized by the "convenience yield" on US debt. Why? One reason is that government regulation (and/or repression) of the financial sector influences asset pricing and helps makes long term US federal debt a "safe-asset". We study the macroeconomic consequences on government borrowing capacity, financial stability, and investment. We then test our theory using new historical data on US convenience yields going back to 1860.
Financial Sector Regulation, Macro-Finance, and FinTech
"Strategic Money and Credit Ledgers"
with Markus Brunnermeier.
Link to latest version (March 2024)
This paper studies strategic decision making by a private currency ledger controller facing competition from public money and/or other ledgers. It chooses a markup fee for making payments using its ledger and the settlement asset for credit contracts. A monopoly ledger controller can incentivize contract enforcement across the financial sector by threatening exclusion but can also extract rents through its pricing power. The emergent market structure bundles the provision of ledger and trading technologies. Regulation should balance better contract enforcement against higher ledger rent extraction.
"Asset Transfers and Self-Fulfilling Runs",
with Joshua Weiss.
Link to latest version (April 2024).
Abstract: We introduce a new mechanism that eliminates self-fulfilling runs on a Diamond Dybvig intermediary without requiring deposit insurance. During a run, a depositor can take unliquidated intermediary assets in exchange for closing their account. We show our mechanism would have been useful in the most recent banking crisis. We also show our mechanism improves upon suspension if depositors repeatedly interact with the intermediary, there is aggregate return risk, and/or policy makers have limited commitment.
"FinTech Lending, Banking, and Information Portability"
with Markus Brunnermeier.
Link to latest version (April 2024).
Link to older longer version (August 2023).
Technological change has led to increased, but segmented, information collection. Tech platforms record the information about trading histories required for making of uncollateralized loans, whereas banks specialize in making the assessment of collateral quality required for collateralized lending. Current regulation hinders tech platforms from offering financial services while strategic behavior between tech platforms and banks impedes cooperation on information sharing and contract enforcement. We study how the government should design - and how lobbying efforts impact - information portability, which ultimately affects financial market segmentation and financial inclusion.
"The Disruption of Long Term Bank Credit".
Link to latest version (December 20 2020).
Abstract: This paper studies the disruption of bank business credit during a financial crisis in a model with optimal long term contracting under agency frictions and a directed search market for bank funding. Banks commit to long term contracts with entrepreneurs but then face heterogeneous shocks to their cost of funds during a crisis. The optimal contract can be implemented using standard debt securities and a ``covenant'' that allows bankers with high funding costs to adjust debt terms once the entrepreneur has accumulated sufficiently many losses. This is consistent with empirical evidence from the recent financial crisis. In general equilibrium, the contracting frictions amplify the crisis by increasing the termination rate of projects and decreasing the financing rate. The model is extended to incorporate project heterogeneity and working capital. The frictions then skew the economy towards lower volatility projects and sub-optimally reduce project size.
Distinguished CESifo Affiliate Award (2019)
CICM Best Junior Scholar Paper Award (2019)
Deep Learning, Financial Frictions, and Inequality
Talks and Summaries:
BFI Workshop on deep learning methods: Link to BFI talk (April 2024).
"Deep Learning for Search and Matching Models."
with Adam Rebei and Yucheng Yang.
Link to latest version (April 2024).
We develop a new method for characterizing global solutions to search and matching models with aggregate shocks and heterogeneous agents. We formulate general equilibrium as a high dimensional partial differential equation (PDE) with the distribution as a state variable. Solving this problem has previously been intractable because the distribution impacts agent decisions through the matching mechanism rather than through aggregate prices. We overcome these challenges by developing a new deep learning algorithm with efficient sampling in a high dimensional state space. This allows us to study search markets that are not “block recursive”. In applications to labor search models, we show that while block recursivity may approximately hold under symmetric shocks, it fails to capture the dynamics when shocks have an asymmetric impact. Business cycles have a “cleansing” effect by amplifying positive assortative matching in recessions, and the magnitude of the counter cyclicality depends on the bargaining process between workers and firms.
"Deep Learning Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models."
with Zhouzhou Gu, Mathieu Lauriere and Sebastian Merkel.
Link to latest version (April 2024).
Link to supplementary appendix on the continuous time Krusell & Smith model.
We propose and compare new global solution algorithms for continuous time heterogeneous agent economies with aggregate shocks. First, we approximate the state space so that equilibrium in the economy can be characterized by one high, but finite, dimensional partial differential equation. We consider different approximations: discretizing the number of agents, discretizing the state variables, and projecting the distribution onto a set of basis functions. Second, we approximate the value function using neural networks and solve the differential equation using deep learning tools. We refer to the solution as an Economic Model Informed Neural Network (EMINN). The main advantage of this technique is that it allows us to find global solutions to high dimensional, non-linear problems. We demonstrate our algorithm by solving canonical models in the macroeconomics literature.
"Asset Pricing, Participation Constraints, and Inequality."
with Goutham Gopalakrishna and Zhouzhou Gu.
New version coming soon.
How do asset returns interact with wealth inequality? Empirical evidence shows that portfolio choices and financial constraints lead to unequal risk exposure across households and financial intermediaries. To understand the dynamic general equilibrium implications, we build a macroeconomic model with heterogeneous households, a financial sector, asset market participation constraints, and endogenous asset price volatility. We develop a new deep learning methodology for characterizing global solutions to this class of macro-finance models. We show that wealth inequality, financial sector recovery, and asset price dynamics depends on which households are able to purchase assets during crisis. This means the government faces a trade-off between tighter leverage constraints and a more equal recovery. In our calibrated model, asset returns and participation constraints account for a large fraction of the change in wealth inequality over the past half-century.
"Housing Policy and Inequality."
with Zhouzhou Gu.
New draft coming soon.
Should the government encourage home ownership? To answer this question, we build a dynamic general equilibrium model with heterogeneous agents, aggregate shocks, and housing stock. We solve the model using a new global solution technique that applies deep learning tools to train an Economic Model Informed Neural Network (EMINN). This allows us to analytically and numerically investigate the relationship between inequality, financial frictions, asset pricing, and housing policy. We show that subsidies for home purchases only help poorer households that are able to hold onto their homes during recession.
Other Research in Progress
"Green Financing with Political Frictions."
with Yinshan Shang.
New draft coming soon.
We examine the role of green finance in an economy with both political and financial frictions. Investors with heterogeneous preferences for carbon reduction (``green'' and ``brown'' investors) voluntarily contribute to environmentally-focused financial intermediaries (``green'' funds) and also participate in political bargaining over environmental policy. Firms with heterogeneous production technologies choose carbon emission and output levels to maximize their market value. Green funds increase the transparency of firm emission levels, improve coordination amongst investors, and ultimately segment investor portfolios along their environmental preferences. This can increase demand for green stocks and so may incentive firms to reduce carbon emissions. However, it also potentially reduces political cooperation because green investors have a better outside option and brown investors are more exposed to environmental regulation. Risk in the political bargaining process leads to complex hedging behaviour by investors in the green finance market.
Other Published Papers:
"Posterior Distribution of Nondifferentiable Functions"
with Toru Kitagawa, José Luis Montiel Olea, and Amilcar Velez. Journal of Econometrics. (2020). Vol. 217, Iss. 1, pp. 161-175.
Abstract: This paper examines the asymptotic behavior of the posterior distribution of a possibly nondifferentiable function g(θ), where θ is a finite-dimensional pa- rameter of either a parametric or semiparametric model. The main assumption is that the distribution of a suitable estimator θn, its bootstrap approximation, and the Bayesian posterior for θ all agree asymptotically. It is shown that whenever g is Lipschitz, though not necessarily differentiable, the posterior distribution of g(θ) and the bootstrap distribution of g(θn) coincide asymptotically. One implication is that Bayesians can interpret bootstrap inference for g(θ) as approximately valid posterior inference in a large sample. Another implication—built on known results about bootstrap inconsistency—is that credible sets for a nondifferentiable parameter g(θ) cannot be presumed to be approximately valid confidence sets (even when this relation holds true for θ).
"Economic Policy and the Great Depression in a Small Open Economy"
with Lawrence Uren. Journal of Money, Credit, and Banking. (2014). Vol. 46, Iss. 2-3, pp. 347-370.
Abstract: We use a standard New Keynesian model of a small open economy, extended to include a government sector, to investigate the Great Depression in Australia. A calibrated model with a fixed exchange rate regime, similar to the gold standard, does well in replicating the dynamics of output during the interwar period. We then ask to what extent shocks to the economy would have been moderated by adopting modern-day policies. We find that if policymakers had adopted a flexible exchange rate with a Taylor rule policy that output fluctuations during the Great Depression would have been moderated by up to 25%. Changes in government fiscal policy would also have moderated output fluctuations, but by a slightly smaller amount. Overall, we find that improved policy could have reduced output fluctuations by almost 50%.
Other Articles
CHAPTER 9. "Central bank digital currency, FinTech and private money creation"
with Markus Brunnermeier. In Niepelt, Dirk (24 November 2021). Central Bank Digital Currency: Considerations, Projects, Outlook. VoxEU.
Data Sets:
US Federal Debt 1776 - 1960: Quantities and Prices
with George Hall, Thomas J. Sargent, and Bálint Szőke.
Link to latest version (February 2018).
Abstract: This document describes Pandas DataFrames and the spreadsheets underlying them that contain prices, quantities, and descriptions of bonds and notes issued by the United States Federal government from 1776 to 1960. It contains directions to a public github repository at which DataFrames and other files can be downloaded.