Knowledge that Transforms

This paper introduces the “financing purposes (FP) channel”, a new channel through which uncertainty affects the effectiveness of monetary policy. Using U.S. bank-firm-loan-level data from 1990 to 2019, we examine how firms adjust FP in response to monetary policy shocks and how this response varies with the level of macroeconomic uncertainty. We find that firms demand more bank loans for investment-related purposes during monetary expansion, but this tendency diminishes notably when uncertainty spikes. A counterfactual analysis suggests that heightened uncertainty explains almost half of the decline in the share of productive loans during the Great Recession. Our results are not driven by banks’ credit supply and are more pronounced for more financially constrained firms and those with a higher degree of investment irreversibility, aligning with the real options theory and financial frictions channel. We also show that FP positively predicts real activities such as investment and employment growth, indicating that high uncertainty weakens the monetary policy transmission via the financing purposes channel.

This paper investigates how the regulation of bank wealth management products (WMPs) affects corporate innovation. We build a concise model linking firms’ assets allocation decisions to regulatory variables affecting banks. The model suggests that current WMP regulation can enhance formal lending and stimulate innovation among state-owned enterprises (SOEs), while having no impact on innovation in private enterprises (PEs). Empirically, we identify firms’ exposure to bank regulation by examining the number of WMPs issued by banks in 2017 and the distance between banks and firms. Our results indicate that innovation output significantly increased for highly exposed SOEs, while there was no significant change for PEs. Using city-level data, we further observe that regulation fosters innovation in regions with higher exposure. Our findings suggest that regulation exerts heterogeneous effects on the real economy by banks’ credit allocation and firms’ investment strategies.

This study explores the intertemporal relationship between the gold-to-platinum price ratio (logGP) across economic conditions and international equity risk premiums. We find that logGP provides distinct predictive signals based on economic states, with logGP during U.S. contractions works as a strong and robust predictor of global stock market returns both in-sample and out-of-sample. This predictability stems not from U.S. market spillovers but rather reflects investors’ tail risk concerns and forecasts of economic activity. Our results indicate that U.S. recessions act as wake-up calls for international investors. Concerns about a U.S. recession propagating to other economies prompts investors to reassess local risks, thereby influencing local stock market dynamics.

This study examines the impact of European Central Bank (ECB) monetary policy surprises on economic uncertainty and investor risk aversion. We identify four factors using a high-frequency event-study approach. These factors measure surprises regarding the current setting of policy rates (Target), the bank’s future policy path (Forward Guidance), and quantitative easing (QE). The fourth factor reflects unexpected news about future macroeconomic conditions. Our main finding is that quantitative tightening surprises, proxied by positive QE surprises, increase economic uncertainty and investor risk aversion. Additionally, we document the significant response of key macroeconomic variables to these surprises.

This research proposes a novel firm-based model for pricing cyber insurance. Our model considers two types of cyber risk: virus attacks and data breaches. Virus attacks deliver adverse shocks to the firm’s productivity, while data breaches cause premium customer departures that worsen the prospect of the firm’s product demand. We derive the endogenous structural form of cyber losses in firms and utilize it to solve the formula for cyber insurance premiums. Our quantitative results show that the consensus prediction about a strictly positive premium-risk nexus is no longer valid. Asymmetries in the sub-premium’s sensitivity to cyber risks from different sources and the premium customer loss rates jointly shape the complexity of the relation between cyber insurance premiums and cyber risks. Improvements in the product demand conditions enhance firms’ incentives to hedge cyber losses and push premiums higher. Lastly, we discuss the influence of product price competition on premiums.

We reassess the predictive power of risk-neutral excess-of-market stock variance (Martin and Wagner, 2019) for stock returns. After correcting two look-ahead biases that influence evidence supporting an average predictive coefficient of 0.5 reported in prior works, we find the data are too noisy to reject the null hypothesis of an average coefficient of zero. However, this insignificant average predictive coefficient conceals the predictability’s strong covariance with market volatility, as well as its large variation across characteristics-sorted subsamples. Out-of-sample analysis confirms that while the MW model does not significantly outperform benchmark models on average, it significantly outperforms during high-volatility periods.

This paper presents an assessment of macroprudential borrower-based measures (BBMs). Despite such measures being in place, several countries saw renewed increases in house prices and indebtedness when rates were low after the Global Financial Crisis. Against this background, we develop a novel modeling framework that allows to explore the link between multiple BBM limits and lifetime credit risk parameters. While our analysis is based on Lithuania’s household loan data, we draw three general lessons that have broader implications. First, we find that BBMs significantly reduce individual mortgage credit risk, thereby providing aggregate resilience. Second, our model indicates that the stance of an income-based measure, specifically the DSTI limit, may have been loose during the low-rate era, highlighting a potential weakness of the tool. Third, we provide empirical evidence supporting more stringent regulation of investor loans and propose a calibration approach.

We investigate the changes in risk sensitivities of interest rates following a shift in Prosper.com's pricing mechanism from auctions to posted prices. We find that the shift leads to reduced sensitivities of loan pricing to credit risk. Furthermore, this change results in higher profits for the platform while investors receive less compensation for the credit risk they undertake. Additionally, borrowers encounter less credit rationing. Analyses of repeat borrowers under both pricing regimes and tests with data under Prosper's upgraded posted prices confirm these findings. We argue that less risk-based pricing results from the incentives of the lending platform.

We study the impact of macroeconomic and financial uncertainties on cross-sectional returns in the Chinese stock market. We find that stocks with a lower macroeconomic uncertainty beta generate higher excess returns, implying that macroeconomic uncertainty commands a negative risk premium. Meanwhile, the exposure to financial uncertainty is not priced in stock returns. Unlike financial uncertainty, macroeconomic uncertainty is a state variable that predicts a deterioration in economic activity, suggesting that investors require a premium for holding stocks that correlate negatively with it.

Using a time series asset pricing model, I empirically show that underpricing of U.S. Treasury securities is explained by risk premia that compensate dealers for bearing price risk. This finding suggests that the Treasury could reduce underpricing by reducing the post-auction price risk (volatility) to auction participants, which can be achieved mathematically by reducing the time from auction to settlement. I calculate that underpricing cost the Treasury $46.3 billion from January 2000 through June 2016. I estimate that standardizing the settlement period to 1-day could have saved the Treasury $15.6 billion over the same period. In addition, I use the estimated model to forecast expected risk-adjusted returns (that result from underpricing) for each auction, and find that these forecasts predict Treasury auction demand. This finding suggests that auction demand depends on underpricing, albeit on an expected risk-adjusted basis. Further, this expected underpricing may actually help the Treasury to sell debt and avoid auction failures.