Pengxiang Zhai

This study reviews how climate change could be considered an additional source of financial market risk using a bibliometric methodology. We find that the primary impetus for academics’ research into these concerns has come from significant international climate change events, e.g., the adoption of the Paris Climate Agreement. Ecological Economics, Energy Policy, and Energy Economics emerge as the major journals of the existing research output. The bibliographic coupling analysis of the corpus further reveals the existence of five major themes. The first theme evaluates the climate risk and explores mechanisms by which can financial risks be impacted by climate change. The second theme talks about the losses brought on by climate change. The third theme talks about how climate finance products, such as green bonds, green securities, and green insurance, are created and issued, as well as how they help to address and mitigate climate risks. The fourth theme examines how the current financial policy frameworks and instruments can be optimized and adapted in light of the considerable degree of uncertainty that surrounds climate change. The fifth theme discusses the climate-related financial risk modeling. We provide a summary of the development of these themes as well as the future direction to be explored.

In this paper, we propose a novel imaging method to forecast the daily price data of West Texas Intermediate (WTI) crude oil futures. We use convolutional neural networks (CNNs) for future price trend prediction and obtain higher prediction accuracy than other benchmark forecasting methods. The results show that images can contain more nonlinear information, which is beneficial for energy price forecasting. Nonlinear factors also have a strong influence during drastic fluctuations in crude oil prices. In the robustness tests, we find that the image‐based CNN is the most stable approach and can be applied in various futures forecasting scenarios. In the prediction of low‐frequency models for high‐frequency data, the CNN method still retains considerable predictive power, indicating the possibility of transfer learning of our novel approach. By unleashing the power of the picture, we open up a whole new perspective for forecasting future energy trends.

Using unbalanced panel data on 3326 Chinese listed companies from 2014 to 2021, this study investigates the impact of corporate environmental performance on China’s excess stock returns. The results show that stocks of companies with better environmental performance earn significantly higher excess returns, indicating the existence of green returns in the Chinese stock market. We further reveal that heightened climate-change concerns can boost the stock market’s green returns, while tightened climate policies decrease green returns by increasing long-term carbon risk. Our findings are robust to endogeneity problems and hold great implications for both investors and policymakers.

This study uses a high‐dimensional time‐frequency volatility spillover model to examine risk interactions across 32 global stock and credit default swap (CDS) markets during the coronavirus (COVID‐19) pandemic. Our empirical results mainly show that cross‐market risk spillovers between these two types of markets are rare over the whole crisis period. Adding CDS assets to the stock portfolio would significantly decrease the overall risk spillovers in the mixed portfolio. Then, the time‐frequency spillover and rolling window analyses confirm this finding and provide further evidence of frequency heterogeneity of risk spillovers during the pandemic. The outbreak of the COVID pandemic only sharply increases short‐term risk spillovers between the stock and sovereign CDS market but exerts insignificant impacts on the medium and long‐term cross‐market risk spillovers. Moreover, our results show that developed countries are net transmitters in the stock markets. In contrast, the emerging markets account for the most risk spillovers in sovereign CDS markets over the sample period, emphasizing the heterogenous adverse impacts of the pandemic across various countries and markets. Finally, we find that bailouts implemented by the US and EU central banks, though with heterogeneous impacts across frequencies, gradually enhance the confidence and resilience of investors in these markets.

This paper examines the relationship between environmental violations and the cost of corporate bonds, and further analyzes whether refinancing constraints, the lending restrictions on penalized firms, are an important channel through which environmental violations affect the cost of corporate debt financing in China. Using the manually collected environmental violation events from 2008 to 2020, we find that the bond cost of firms violating environmental regulations increases significantly after being penalized. The finding is robust to potential sample selection bias and endogeneity problems. Furthermore, we find that the effect of environmental violations on the cost of corporate bonds is more profound in short‐term debt‐dependent firms and can be alleviated by the internal financing dependence. These results confirm that the refinancing risk arising from China's green loan policy on pollution‐intensive firms plays a key role in the relation between environmental violations and the cost of capital in the firms. In the context of the growing concerns of global society about climate change, our findings have great implications for pollution‐intensive firms, bond investors, and regulatory authorities in emerging economies to manage the increasing environmental risk.