Abstract: We identify a novel and common exogenous demand shock caused by passive funds in the corporate bond market. Specifically, passive fund demand for corporate bonds displays discontinuity around the maturity cutoffs separating long-term, intermediate-term, and short-term bonds. Passive funds' demand increases significantly upon a bond's crossing of 10-, 5-, and 3-year time-to-maturity cutoffs. We develop a novel identification strategy to study the impact of passive fund demand in the corporate bond market. First, we find that these non-fundamental demand shifts lead to a significant and lasting decrease in yield spreads, as well as persistent liquidity improvements. Second, passive fund demand shocks spill over to the primary market, causing lower issuing yield spreads, active new bond issuance, and higher total bond outstanding. Furthermore, firms reduce bank debt after the positive passive fund demand shock, suggesting that borrowers substitute cheap bond financing for bank debt in response to the elevated passive fund demand in the bond market.

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: An important innovation in the private lending market is the increasing participation of nonbank institutional lenders. Compared to traditional banks, nonbank lenders have a higher demand for secondary market liquidity due to their fragile funding source. An important question is how the demand for liquidity by nonbank lenders affects borrowers' cost of capital. In this paper, we exploit a novel setting of Morningstar LSTA US Leverage Loan 100 Index weekly rebalances as an exogenous liquidity shock to examine how secondary market liquidity affects private lending. Consistent with nonbanks having a higher demand for liquidity, we show a significant and persistent increase in secondary market price and CLO trading volume following the exogenous liquidity improvement. Importantly, we observe a notable surge in interest rate-reducing loan renegotiations closely aligned with the timing of index inclusion, with an average reduction of 21 bps. The effect of interest rate reduction is more pronounced when the aggregate credit supply by nonbanks is higher. Our findings highlight the substantial and timely passthrough of nonbank demand to the financing cost for private loan borrowers through renegotiation. Our exploration into the mechanism suggests that borrowers with greater bargaining power tend to receive more borrowing costs reduction. Moreover, renegotiation frictions negatively impact the efficacy of borrowing cost reduction. Overall, our paper provides novel causal evidence that nonbank lenders' demand for liquidity is a salient non-fundamental determinant of loan renegotiation and financing cost.

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.