Research

US public state pension deficits are very large, accounting for 18.5% of an average state's GDP and up to 50% in Illinois. In principle, households should respond to this heavy future burden by increasing current savings, particularly in safe assets, since pension deficits are countercyclical. Comparing households residing on opposing sides of a state's border, I document that households in larger-deficit states save more, investing more in safe bank deposits and less in risky stocks. Specifically, households hold 0.70 dollars more in deposits and 0.33 dollars less in stocks for each additional dollar of pension deficit. This effect strengthened further following the implementation of new accounting standards in 2015 that made deficits more salient by requiring states to publicly disclose them. Exploiting staggered state pension reforms, I also find that households respond consistently when states reduce pension deficits; they decrease deposits and increase stock holdings. These reallocations spill over onto local economic activity: as households withdraw deposits following a pension reform, exposed local banks cut lending to local businesses.

While regulators stress the importance of passive investors' fiduciary duty in corporate governance, we argue that when passive funds fulfill their fiduciary duty to increase a firm’s value, they unintentionally create market inefficiencies. In our model, investors acquire information, optimize portfolios, and affect a firm's value through governance. In equilibrium, bad firms are left governed by passive investors, a consequence of market clearing. Therefore, when passive investors increase a firm's value, they increase it more for bad firms, reducing information sensitivity and generating market inefficiencies. We offer unique empirical predictions and explore applications on ESG policies and product market competition.

This paper uncovers a novel phenomenon of flight-to-Bitcoin (FTB) whereby local demand for Bitcoin increases with local economic policy uncertainties. FTB holds across Bitcoin demand proxies including local premiums over the US market, turnover, and web traffic on cryptocurrency exchanges. We find that FTB is mainly driven by a lack of confidence in local authorities. Consistent with this interpretation, FTB is stronger during corruption incidents and Bitcoin ownership shifts from centralized exchanges to decentralized wallets amid turbulence. A comparison with safe-haven assets further differentiates FTB from other forms of flight-to-safety. The evidence collectively illustrates one overlooked motivation of Bitcoin investment: investors hold Bitcoin to mitigate their concerns about local authorities.

This paper develops a structural model to examine high-frequency price dynamics. The key innovation is to allow trades' permanent price impact to be time-varying—dynamic trade informativeness. A distribution-free filtering technique pins the real-world data to the model. The filtered series (i) significantly recover the efficient price innovation through the dynamics of trade informativeness, (ii) improve trades' explanatory power for future returns, (iii) distinguish informativeness from trades' aggressiveness, (iv) gauge informed investors' patience, and (v) capture systematic patterns around scheduled and unscheduled events, as well as general intraday trends. The framework contributes to the better utilization of high-frequency trading data.

The global financial crisis of 2007–2009 revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify systemic risk is at the heart of the process of identifying the so-called systemically important financial institutions for regulatory purposes as well as to investigate key drivers of systemic contagion. The article proposes a method for dynamic forecasting of CoVaR, a popular measure of systemic risk. As a first step, we develop a semi-parametric framework using asymptotic results in the spirit of extreme value theory (EVT) to model the conditional probability distribution of a bivariate random vector given that one of the components takes on a large value, taking into account important features of financial data such as asymmetry and heavy tails. In the second step, we embed the proposed EVT method into a dynamic framework via a bivariate GARCH process. An empirical analysis is conducted to demonstrate and compare the performance of the proposed methodology relative to a very flexible fully parametric alternative.