监管对冲指的是被监管者在应对监管政策带来的冲击时，为了应对监管的政策影响（冲击）而采取成本转嫁或风险转嫁的策略性行为，结果是部分抵消或削弱了监管政策意图达到的效果，这个问题具有现实普遍性。基于此，我们开展了两项研究，分别是：1.监管对冲：企业跨主体风险转移；2.监管对冲：银企跨期风险转移。这两项研究均以2016年宏观审慎评估（MPA）政策这一准自然实验，第一项研究结果显示：政策落地后，银行对短期贷款依赖度高的企业显著收缩表内信贷，但企业并未被动“去杠杆”，而是通过大幅扩张商业信用、提高经营负债来“补缺口”。现实中，银行信贷紧缩后，原本高度依赖贷款的企业会利用商业信用等方式，把资金压力转移给上游中小供应商...

We introduce an ordinary differential equation (ODE) based deep generative method for learning conditional distributions, named Conditional F\"ollmer Flow. Starting from a standard Gaussian distribution, the proposed flow could approximate the target conditional distribution very well when the time is close to 1. For effective implementation, we discretize the flow with Euler’s method where we estimate the velocity field nonparametrically using a deep neural network. Furthermore, we also establish the convergence result for the Wasserstein-2 distance between the distribution of the learned samples and the target conditional distribution, providing the first comprehensive end-to-end error analysis for conditional distribution learning via ODE flow.

While a substantial body of empirical research has focused on the pricing of green debt, there is no consensus on the existence of a green bond yield’ debt funding costs, this paper develops a dynamic capital structure model that incorporates green debt. Issuing green debt provides convenience for shareholders by lowering interest payments. However, it also lowers the firm’s expected growth rate, as proceeds are committed to green projects. Our model reveals that green debt refinancing causes shareholders to be conservative about debt policy, thus inducing the low target leverage level. The reduction in interest payments alleviate the debt burden, which reduces expected credit risk. As a result, creditors are willing to accept a low credit spread. Finally, we find that the degree of green investment and debt maturity have important effects on the greenium.

We propose a simple indicator function based on the aggregate tug-of-war between overnight and intraday traders, and use it to identify two types of trading days: quiet and noisy days. We analyze these days and document that the security market line is upward sloping on quiet days and downward sloping on noisy days. This result is robust to a number of additional tests. Moreover, the result holds on both (i) important macroeconomic and earnings news days and (ii) other days, challenging some proposed explanations in the literature. We present and test a mechanism based on the over-correction hypothesis to rationalize the finding.