Biographical Summary

Krista Schwarz joined the Federal Reserve Board in 2020 from the Finance Department of the Wharton School. As a Principal Economist in Monetary Affairs, Krista's research is inspired by outstanding policy questions. Her expertise is in empirical asset pricing, particularly in relation to market structure and liquidity. She presents in academic seminars and conferences globally, and has published in top finance journals such as the Review of Financial Studies and the Journal of Financial and Quantitative Analysis.

From 2009 to 2020, Krista was faculty at the University of Pennsylvania's Wharton School, where her teaching and research drew on her market experience; trading repo, fed funds, fixed income and foreign exchange while working on the Open Market Desk of the Federal Reserve Bank of New York. She has also worked at the World Bank, the European Commission in Luxembourg, and the Economics Bureau of the U.S. Embassy in Berlin. She has a Ph.D. and an M.Phil. in Finance and Economics from Columbia University in the City of New York, and an MA in International Policy and Economics from the Johns Hopkins University (SAIS).

Research Summary

My research is in empirical asset pricing and centers on identifying and interpreting the drivers of asset price movements, distinguishing between risk pricing and non-risk-based frictions. I have a particular interest in price relationships that are unexplained in a classic no-arbitrage setting, which I characterize broadly as liquidity. My approach uses large datasets on quantities and prices to pin down and trace the effects of changes in market structure and investor constraints.

Post-financial crisis policy has differed by market segment, regulatory authority, and timing. There has been no shortage of market structure shifts. Understanding the implications is important for the effectiveness of monetary policy which relies on efficient transmission through asset prices. It is important for regulators to understand the tradeoffs in imposing constraints. It also matters to the perceived risk-reward assessment of investment strategies. My aim is to give policymakers and investors more precise, complete and forward-looking information with which to ground decisions.

Newest Research

Predictable End-of-Month Treasury Returns (Hartley and Schwarz, 2021, Working Paper.)
We find an intriguing new anomaly in the U.S. Treasury market; the unconditional end-of-month excess return on U.S. Treasury coupon securities accounts for the entirety of the term premium (e.g. excess returns of around 20 basis points per month at the 10-year maturity). A trading strategy comprised a long Treasury position over just the last few days of the month, gives an annualized Sharpe Ratio of around 1. This is comparable to the risk-adjusted return for other leading anomalies in the literature (e.g. the foreign currency carry trade).

Relating flows to prices, by investor type, we find that index rebalancing helps to explain the anomalous timing of excess returns. We isolate the demand shock associated with the security's changing role in the Barclay's Aggregate Treasury Government Index. Investors that benchmark performance more closely show relatively greater net purchases of the new index additions.

The effect is not sustained in Treasury security prices, in contrast to the liquidity risk-pricing feedback loop detailed in Musto, Nini, Schwarz (2018). Index demand shocks do not appear to be incorporated into the fundamental value of the relative asset prices. Such shocks may grow as a larger share of investments shifts to passive funds that mechanically track index changes.

Select Publications and Illustrated Results

My work contributes to the literature on asset pricing amomalies by empirically identifying the drivers of anomalies, and investigating their transmission through market structure to pin down what friction or risk allows the effects to be amplified and sustained.
Notes on Bonds: Illiquidity Feedback During the Financial Crisis (Musto, Nini, and Schwarz, 2018, Review of Financial Studies, 31(8): 2983-3018.)
For instance, levered investors' demand for liquid securities contributes to the limits to arbitrage in fixed income markets. When leverage constraints are tightened, the frequency of trading in already liquid securities increases, widening the risk wedge between more- and less-liquid securities, thus allowing a relative price differential to arise and persist.


  • Animation: U.S. Treasury yields split into two separate curves (liquid versus illiquid securities) in 2008-2009.
Treasury Market Liquidity Differential
Data updated through: August 2019



The dynamics of this feedback loop are driven by the interaction between the liquidity risk pricing of investors with heterogeneous security preferences (relating holding horizon to asset liquidity) and intermediaries facing inventory risk. Liquidity feedback between risk pricing and security characteristics gives a possible mechanism for multiple liquidity equilibria.

  • Animation: visualize limits to arbitrage over various holding periods; the precise timing of entering into an arbitrage matters more (less) for an investor with a relatively short (long) horizon.

  • Animation: the relative transaction costs of Treasury securities diverge by market liquidity profile.

Mind the Gap: Disentangling Credit and Liquidity in Risk Spreads (Schwarz, 2019, Review of Finance, 23(3): 557-597.)
In the Eurozone sovereign debt markets, I find that the role of liquidity risk in asset prices can be large, and it varies over time and in the cross section. This argues in favor of including a liquidity factor in asset pricing models.

K-Spread Measure of Market Liquidity
Data updated through: August 2019




In the Great Financial Crisis, liquidity risk explained more than one third of the average trough-to-peak sovereign bond yield spread widening. During the Sovereign Debt Crisis, the balance of risks shifted to credit. Interestingly, bond market liquidity is the largest contributor to interbank rates over both sample periods. This supports interpreting the market liquidity premia of a security as being closely linked to its funding value as collateral.
My work also contributes methodologically to risk premium estimation. The method of estimating risk premia can importantly determine whether a candidate asset pricing model is rejected or not.
Using Stocks or Portfolios in Tests of Factor Models (Ang, Liu, and Schwarz, 2020, Journal of Financial and Quantitative Analysis, 55(3): 709-750.)
In the standard two-stage Fama MacBeth framework there is a tradeoff in using individual test assets versus portfolio groupings of assets to estimate factor risk premia.
  • Assets are typically grouped into portfolios. The motivation for this is to increase the precision of the factor loading estimates. Since factor loadings are estimated with error, it makes sense that idiosyncratic error should be increasingly diversified away as more estimates are grouped into each portfolio.


  • On the other hand, individual test assets give the full cross section of information in the risk premium estimation, increasing efficiency. Also, there is no risk of mis-assigning asssets into portfolios, which could actually worsen bias.
In simulations, the mean square error (MSE) of risk premium estimates shows that using a small number of portfolios gives little benefit to bias and sacrifices much in terms of efficiency. Using a larger number of portfolios (at least 250) achieves the minimum MSE in some data settings, but the tradeoff shifts to favor using individual assets as the likelihood of incorrect portfolio assignment increases (e.g. due to sorting on estimated betas).