Abstract: Institutional investors commit trillions of dollars to private funds. These commitments give fund managers discretion to call capital on short notice, effectively making investors their liquidity providers. Using novel data on insurers' $370 billion private fund investments, this paper studies the risk of unexpected capital calls. Specifically, I examine the portfolio implications of capital call shocks and the resulting spillovers to public asset markets. I show that capital calls are difficult to predict and that unexpected calls are substantial. Nevertheless, I find no evidence that insurers build liquidity buffers ex ante. Instead, they adjust their portfolios only ex post, primarily by selling risky corporate bonds. These portfolio decisions are driven by regulatory capital considerations. Moreover, capital-call-induced corporate bond sales cause negative price impacts, especially for bonds with high risk weights. These spillover effects are amplified when capital call shocks are concentrated or coincide with other episodes of market stress. Counterfactual stress tests reveal significant aggregate losses under extreme scenarios. Overall, the findings highlight the liquidity risk embedded in private fund commitments and its implications for financial fragility.

Abstract: We identify a novel exogenous demand shock caused by passive funds in corporate bond markets. Passive fund demand for corporate bonds displays discontinuity around the maturity cutoffs separating long-term, intermediate-term, and short-term bonds. Using a novel identification strategy, we show  that these non-fundamental passive demand shifts i) lead to predictable upward price pressure, and ii)  spill over to primary markets, causing lower issuing yield spreads, and firms engaging in debt market timing by substituting bank debt with bond financing. We show how SEC regulations and provisions affect the execution  of passive strategies and their transmission  to the real economy.

Abstract: We examine whether non-fundamental improvements in secondary market trading conditions lead to renegotiations of syndicated loans. Exploiting a regression discontinuity design around the LSTA 100 index reconstitution, we find that loans included in the index are five times more likely to receive interest-rate-reducing amendments relative to comparable loans just below the threshold. Utilizing the within-loan-package interest rate variation, we confirm that these renegotiations are not driven by borrowers’ fundamentals. The threat of refinancing likely drives this effect, as our findings are more pronounced when institutional credit supply is higher and when loans are not subject to call protections.

Abstract: Leveraging the around-the-clock high-frequency S&P 500 index futures data, we document a significant and robust market-wide spillover from individual large firms' earnings announcements. Such spillover highlights the role of firms' earnings news as a pivotal source of aggregate market information, on par with macroeconomic releases. We further show that the magnitude of earnings news spillover is a persistent firm characteristic associated with the firm's size and industry. To examine the systematic risk explanation for the earnings announcement premium, we extend the model of Savor and Wilson (2016) to allow firms' news to covey heterogeneous aggregate cash flow news. Consistent with the model's prediction, we find that firms with larger expected earnings spillovers earn higher abnormal returns during the announcement weeks. Therefore, our findings provide direct evidence supporting the systematic risk explanation for the earnings announcement premium. 

Abstract: We examine 320 different forecasting models for international monthly stock return volatilities, using high frequency realized variances and the implied option variance as the predictor variables.  We evaluate linear and non-linear models, and logarithmic transformed and weighted least squares estimation approaches. A logarithmically transformed Corsi (2009) model combined with the option implied variance (``lm4_log") is robustly, across countries and time, among the best forecasting models. It also survives tests using panel models and international variables. When alternative models (such as models including negative returns) have better performance, the forecasts they generate are extremely highly correlated with those of the ``lm4_log" model.