Knowledge that Transforms

Artificial intelligence (AI) is rapidly changing how the financial system is operated, taking over core functions for both cost savings and operational efficiency reasons. AI will assist both risk managers and the financial authorities. However, it can destabilize the financial system, creating new tail risks and amplifying existing ones due to procyclicality, unknown-unknowns, the need for trust, and optimization against the system.

We study how investors in the US municipal bond market price the state lockdowns announced during the coronavirus (COVID) pandemic. To begin with, we examine the extent to which state-level COVID developments influence yield spreads of municipal bonds. We find that macro-level factors are the primary determinants of municipal bond spreads during the pandemic, but state-level COVID developments also matter at the margin. For instance, a doubling of new COVID cases in a state is associated with a 2% (1.4 basis points) increase in yield spreads of municipal bonds issued in that state. Accordingly, lockdowns may decrease municipal bond spreads by reducing COVID cases, but lockdowns may also increase them by reducing local economic activities. Overall, we find that yield spreads in both primary and secondary municipal bond markets increase by about 15% following lockdown announcements, suggesting that lockdown announcements increase the risk premiums investors require for holding municipal bonds.

Millions of investors place their trust in financial advisors who may have incentives to give them bad advice. This may indicate that advisors behave more fairly than economic theory predicts. In this paper, we present results from a large-scale experiment studying advice-giving under conflicting interests. We use a binary dictator game as a baseline and transform it into a situation where the dictator gives advice that may or may not be followed. Our results show that people are averse to giving bad advice. When subjects are given the role of advisor, they behave less selfishly, even when the economic incentives and considerations remain the same as in the baseline dictator game.

This paper aims to provide a comprehensive analysis of the potential conflicts between macroprudential and monetary policies within a DGSE model with financial frictions. The identification of conflicts is conditional on different types of shocks, policy instruments, and policy objectives. We first find that conflicts are not systematic but are fairly frequent, especially in the case of supply-side and widespread shocks such as investment efficiency and bank capital shocks. Second, monetary policy and countercyclical capital requirements generate conflicts in many circumstances. By affecting interest rates, they both “get in all the cracks”, albeit with their respective targets generally moving in opposite directions. Nonetheless, monetary policy could reduce its adverse financial side effects by responding strongly to the output gap. Third, countercyclical loan-to-value caps, as sector-specific instruments, cause fewer conflicts. Thus, they can be more easily implemented without concerns about generating spillovers, whereas smooth coordination is required between state-contingent capital requirements and monetary policy.

We examine how market power in the run-up to the 2007–2009 crisis affected banks’ systemic risk during the crisis, and whether this effect was influenced by two key factors: securitization and bank capital. Using a sample of the largest listed banks from 15 countries, we find that more market power prior to the crisis is connected to larger levels of realized systemic risk during the crisis. The use of securitization exacerbated the effect of market power on systemic risk, while capitalization partially mitigated it.

We study the cross-sectional relation between stock and corporate bond markets. By correcting credit spreads of corporate bonds for expected default losses and by using equity-bond elasticities, we obtain a firm’s expected bond-implied stock return, which we then compare to its realized stock return. We find, surprisingly, a strong negative cross-sectional relation between these expected and realized stock returns. We show that this effect is not simply a restatement of the distress risk puzzle or other well-known anomalies in stock and corporate bond markets. This negative cross-sectional relation is strongest for high-risk firms and for liquid stocks.

We find that aggregate profitability and asset investment exhibit robust joint predictive power for aggregate excess stock returns, consistent with the investment model of Hou, Xue, and Zhang (henceforth HXZ, 2015). These results provide out-of-sample empirical support for HXZ, as the same mechanisms that HXZ use to explain firm-specific variation in stock returns can also be used to explain variation that is market-wide in nature. Also consistent with the HXZ investment model, we find that the growth rate of short-term (long-term) assets exhibits a stronger predictive power for one-year-ahead (two-year-ahead) stock returns.

In the financial crisis and recession induced by the Covid-19 pandemic, many investment-grade firms became unable to borrow from securities markets. In response, the Fed not only reopened its commercial paper funding facility but also announced it would purchase newly issued and seasoned corporate bonds rated as investment grade before the Covid pandemic. We assess the effectiveness of this program using long sample periods, spanning the Great Depression through the Great and Covid Recessions. Findings indicate that the announcement of corporate bond backstop facilities helped stop risk premia from rising further than they had by late-March 2020. In doing so, these backstop facilities limited the role of external finance premia in amplifying the macroeconomic impact of the Covid pandemic. Nevertheless, the corporate bond programs blend the roles of the Federal Reserve in conducting monetary policy via its balance sheet, acting as a lender of last resort, and pursuing credit policies.

The Sovereign Debt Management Office of the Kingdom of Denmark decided in 2017 to pay direct compensation to certain financial intermediaries for providing liquidity in its sovereign bond market. We analyze the extent to which market liquidity and liquidity premia in the Danish sovereign bond market changed in response to this new market design and, in particular, the compensation offered to liquidity providers. We also investigate whether the new compensation scheme for market makers is cost-efficient for the Kingdom of Denmark through lower bond liquidity premia, bond yields and debt service costs. We find that improvements in market liquidity conditions lead to potential savings, net of costs, for the Kingdom of Denmark of DKK 23.12 million over the lifetime of the bonds issued every year as a direct benefit, and to a decrease in interest expenses for the real economy of about 0.07% of nominal GDP as one indirect benefit. From these findings, we derive policy recommendations for the Kingdom of Denmark, as well as other European countries that currently generally employ an indirect rather than a direct compensation scheme.

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.