Curbing environmental pollution has become a key priority in China, as reflected in the adoption of policies such as “New Normal” and the takeover of regulatory environmental enforcement by the top leadership of the central government in 2015. In this paper, we use a dataset of publicly-traded firms in the Shanghai and Shenzhen stock exchanges and the event study methodology to gauge the reaction of the investor class to the new environmental enforcement regime. Our results indicate that, together, the announcement and implementation of the new enforcement regime spurred a significant decline of over $29 billion in shareholder value of polluting companies, suggesting that capital market participants expect increased regulatory costs for targeted companies. We also find that neither political connections nor firm size mitigated the severity of the market losses. Instead, larger firms and state-owned enterprises with excess capacity experienced bigger declines in market value.

In this paper, we propose a nonparametric estimator of the short rate diffusion process using observations of a panel of yields. The proposed estimator can greatly reduce the bias of the nonparametric estimator proposed in Stanton (1997) that uses a single time series of short rate observations. Simulations confirm that the new method significantly attenuates the spurious nonlinearity of the drift function as documented in Chapman and Pearson (2000). We apply the method to estimate the U.S. short rate process using a panel of six Treasury yields. With 42 years’ daily observations of the panel of yields, the proposed drift function estimator achieves the same efficiency as the Stanton (1997) estimator based on 145 years of daily short rate observations. Finally, we show that the proposed estimator also has significant economic implications on the pricing of bonds and interest rate derivatives.