Knowledge that Transforms

Repo markets are an important first stage of monetary policy transmission. In the European repo market, the majority of participants, including non-dealer banks and non-banks, do not have access to centralized trading platforms. Rather, they rely on OTC intermediation by a small number of dealers that exert significant market power. Dealer market power causes the passthrough of the ECB's policy rate to be inefficient and unequal. Allowing market participants access to centralized trading platforms, or a secured deposit facility with the central bank, could improve the transmission efficiency of monetary policy while reducing the dispersion in repo rates across customers.

We investigate how high-frequency trading (HFT) in equity markets affects options market liquidity. We find that increased aggressive HFT activity in the stock market leads to wider bid–ask spreads in the options market through two main channels. First, options market makers’ quotes are exposed to sniping risk from HFTs exploiting put–call parity violations. Second, informed trading in the options market further amplifies the impact of HFT in equity markets on the liquidity of options by simultaneously increasing the options bid–ask spread and intensifying aggressive HFT activity in the underlying market.

Consistent with contemporaneous research, we document that minority-owned firms were more likely than observationally similar white-owned firms to receive PPP loans from nonbank lenders than from banks. However, we show that this substitution to nonbanks was only partial, resulting in significantly lower PPP take-up by minority-owned firms, particularly Black-owned ones. Location and firm characteristics explain about two-thirds of the 25 percentage point disparity in PPP take-up by Black-owned firms. While there was greater substitution to nonbanks in more racially biased locations, overall take-up was still lower in those locations. Access to professional help with applications facilitated use of nonbanks and mitigated disparities.

We investigate individual investors' decisions under time-varying ambiguity (VVIX) using plausibly exogenous forced mutual fund liquidations at a German brokerage. Investors reinvest 87% of forced liquidations when the refund occurs on a day of low ambiguity and 0% when it occurs on a day of high ambiguity. Instead of reinvesting, investors become inert and keep the refund in their cash holdings. The effect reverses approximately six months after the liquidation. If investors reinvest, they decrease their risk-taking under ambiguity. Our results are not driven by risk, rebalancing decisions, experiencing losses, or attention and are robust to alternative measures of ambiguity.

Using available return data, many multi-factor asset pricing models present impressive in-sample Sharpe ratios, significantly surpassing that of the market portfolio. Such a performance, however, contradicts the conventional wisdom in finance. Investors cannot realistically attain the in-sample Sharpe ratios. They obtain the out-of-sample Sharpe ratios, which are significantly lower. Estimation risk is one reason for this performance deterioration. We theoretically study the effect of estimation risk by obtaining the exact distributions of in-sample and out-of-sample Sharpe ratios, and argue that such effect needs to be considered in model comparisons.

We explore how mutual fund managers and investors react when the tradeoff between a fund's sustainability and performance becomes salient. Following the introduction of Morningstar's sustainability ratings (the “globe” ratings), mutual funds increased their holdings of sustainable stocks to attract flows. Such sustainability-driven trades, however, underperformed, impairing the funds’ overall performance. Consequently, a tradeoff between sustainability and performance emerged. In the new equilibrium, the globe ratings do not affect investor flows and funds no longer trade to improve their globe ratings.

We quantify the exposure of major financial markets to news shocks about global contagion risk while accounting for local epidemic conditions. For a wide cross section of countries, we construct a novel dataset comprising (i) announcements related to COVID19 and (ii) high-frequency data on epidemic news diffused through Twitter (Hassan et al., 2019’s methodology). We provide novel empirical evidence about financial dynamics both around epidemic announcements and at daily/intra-daily frequencies. Analysis of contagion data and social media activity about COVID19 suggest that the market price of contagion risk is significant.

Privately issued money often bears default risk, which creates transaction frictions when used as a medium of exchange. The late 19th century US provides a unique context to evaluate the real effects of supplying a new type of money that is safe from default. We measure the local change in “monetary” transaction frictions with a market access approach derived from general equilibrium trade theory. Consistent with theories hypothesizing that lowering transaction frictions benefits the traded and inputs-intensive sectors, we find an increase in traded goods production, in the share of manufacturing output and employment, and in innovation.

We study whether regulators should reveal stress test results that contain imperfect information about banks' financial health. Although disclosure restores market confidence in banks, it misclassifies some healthy banks as risky. This encourages banks to choose portfolios deemed safe by regulators, leading to model monoculture and making the financial system less diversified. Under the ex-ante optimal disclosure policy, the regulator addresses this tradeoff by fully revealing stress test results when adverse selection is very severe or very mild, but never disclosing the results otherwise.

Popular bid–ask spread estimators are downward biased when trading is infrequent. Moreover, they consider only a subset of open, high, low, and close prices and neglect potentially useful information to improve the spread estimate. By accounting for discretely observed prices, this paper derives asymptotically unbiased estimators of the effective bid–ask spread. Moreover, we combine them optimally to minimize the estimation variance and obtain an efficient estimator. Through theoretical analyses, numerical simulations, and empirical evaluations, we show that our efficient estimator dominates other estimators from transaction prices, yields novel insights for measuring bid–ask spreads, and has broad applicability in empirical finance.