Yilin (David) Yang
Yilin (David) Yang
I am a sixth-year Finance Ph.D. student and job market candidate at the Stanford Graduate School of Business.
- Financial intermediation
- Monetary policy implementation
- Central banking
- Interbank payment system
Job Market Paper
What quantity of reserves should the Fed supply in order to support effective monetary policy implementation and an efficient interbank payment system? To answer this question, I construct a model linking interbank intraday payment timing with monetary policy implementation. A low supply of reserves causes banks to delay payments to each other and strategically hoard reserves. This in turn disincentivizes banks from providing liquidity to short-term funding markets, driving up the spreads between overnight risk-free market rates and the central bank deposit rate. As reserve balances get sufficiently low, even small reductions in reserves can have large impacts on these spreads, as in September 2019. My fitted model captures the funding rate spikes of September 16-18, 2019 as an out-of-sample event. (Finalist, BlackRock Applied Research Award.)
The Federal Reserve's "balance-sheet normalization," which reduced aggregate reserves between 2017 and September 2019, increased repo rate distortions, the severity of rate spikes, and intraday payment timing stresses, culminating with a significant disruption in Treasury repo markets in mid-September 2019. We show that repo rates rose above efficient-market levels when the total reserve balances held at the Federal Reserve by the largest repo-active bank holding companies declined and that repo rate spikes are strongly associated with delayed intraday payments of reserves to these large bank holding companies. Intraday payment timing stresses are magnified by early-morning settlement of Treasury security issuances. Substantially higher aggregate levels of reserves than existed in the period leading up to September 2019 would likely have eliminated most or all of these payment timing stresses and repo rate spikes.
We study a trading game with agents who face a high-dimensional estimation problem. We define a new equilibrium concept, the “statisticians’ equilibrium,” in which each agent uses only a ridge regression on her own data to forecast the fundamental’s distribution and does not make inference on price in her demand curve submission. In the presence of a curse of dimensionality, we show that statisticians' equilibrium is an ε-approximation of rational expectations equilibrium and matches survey observations about modern trading processes. In this framework, we analyze price's statistical properties -- from the perspective of the model and an econometrician -- and trading volume. We derive quantitative properties of price's prediction risk and trading volume in these equilibria, introducing the notion of a “regularization externality” in price formation and accounting for trading volume spikes on earnings dates.