In the face of severe climate change, researchers have looked for assistance from financial instruments. They have examined how to hedge the risks of these instruments created by market fluctuations through various green financial derivatives, including green bonds (i.e., fixed-income financial instruments designed to support an environmental goal). In this study, we designed a green bond index option contract. First, we combined an autoregressive moving-average model (AMRA) with a generalized autoregressive conditional heteroskedasticity model (GARCH) to predict the green bond index. Next, we established a fractional Brownian motion option pricing model with temporally variable volatility. We used this approach to predict the closing price of the China Bond–Green Bond Index from 3 January 2017 to 30 December 2021 as an empirical analysis. The trend of the index predicted by the ARMA–GARCH model was consistent with the actual trend and predictions of actual prices were highly accurate. The modified fractional Brownian motion option pricing model improved the pricing accuracy. Our results provide a policy reference for the development of a green financial derivatives market, and can accelerate the transformation of markets towards a more sustainable economic development model.

We find that the level of short interest in a firm's stock significantly predicts future seasoned equity offers (SEOs). The probability of an SEO announcement increases by 34% (decreases by 49%) for firms in the top (bottom) quintile of short interest. We identify a causal impact of short interest on SEO issuance using a novel instrument for short interest based on future litigation filings in close geographical proximity to hedge fund centers. Our findings suggest that corporate decisions can be triggered by the aggregate trading activity of sophisticated outside investors.

Using subsidiary-level data of 3,863 Chinese nonfinancial listed firms, we find their capital expenditures increase with foreign sales, and the difference arises from the investments of the firms’ foreign subsidiaries. We show that the foreign sales-foreign investment association becomes more sensitive when the economic policy uncertainty (EPU) increases in the domestic market. However, foreign EPU does not play such a significant role. We provide one possible explanation that due to global diversification, MNEs can hedge foreign EPU using their international subsidiary network, resulting in the overall investments unchanged. However, given China’s tight regulatory capital controls, the MNEs may be less able to hedge the domestic EPU, so that they reallocate investments from the domestic markets to the foreign markets, consistent with the transaction cost assumption underlying the real options theory. Robust tests show that access to foreign capital, profitability and institutional factors have little explanatory power over the MNEs’ foreign investment.

We present a novel methodology for constructing portfolios to hedge economic and financial risks arising from climate change. We utilize ChatGPT-4 to identify climate-related conversations during earnings conference calls and connect these time-stamped transcripts with high-frequency stock price data pinpointed to the conversation level. This approach allows us to assess a company’s dynamic exposure to climate change risks by analyzing real-time stock price responses to discussions about climate issues between managers and analysts. Our proposed portfolio, constructed by taking long (short) positions in stocks with positive (negative) market responses to climate conversations, appreciates in value during future periods with negative aggregate climate news shocks. Compared to portfolios constructed using alternative methods, our real-time market response-based portfolios demonstrate superior out-of-sample hedge performance. A key advantage of our approach is its ability to capture time-series and cross-sectional variations in stocks’ rapidly-evolving exposures to climate risk, relying on the timing of when climate-related issues become salient topics that warrant conference call discussions and real-time market responses to such conversations. Additionally, we showcase the versatility of our approach in hedging other types of dynamic risks: namely political risk and pandemic risk.

This paper examines the effects of superstitious psychology on investors’ decision making in the context of Mercury retrograde, a special astronomical phenomenon meaning “everything going wrong”. Using natural experiments in the Chinese stock market, we find a significant decline in stock prices, approximately -3.14% in the vicinity of Mercury retrogrades, with a subsequent reversal following these periods. The Mercury effect is robust after considering seasonality, the calendar effect, and well-known firm-level characteristics. Our mechanism tests are consistent with model-implied conjectures that stocks covered by higher investor attention are more influenced by superstitious psychology in the extensive and intensive channels. A superstitious hedge strategy motivated by our findings can generate an average annualized market-adjusted return of 8.73%.

Interest rate liberalization in China intensifies the exposure of commercial banks' interest rate risks and further increases the difficulty for commercial banks to effectively control interest rate risks, thus putting forward higher requirements for the normal operation and management of commercial banks. With the development of China's financial derivatives market, banking institutions begin to use basic interest rate derivatives to hedge interest rate risks. It is very important to give full play to the maturity transformation Function of commercial banks to enhance the ability of financial services to the real economy. Based on the semi annual unbalanced panel data of 37 listed banks in A-share stock markets from 2006 to 2020, this paper empirically tests the impact of the use of off balance sheet interest rate derivatives on the Maturity Transformation Function of banks in the case of interest rate fluctuations. The empirical results show that: (1) the use of interest rate derivatives helps to weaken the negative impact of interest rate fluctuations on the Maturity Transformation Function of banks. (2) The analysis of the mechanism shows that the use of interest rate derivatives improves the stability of the bank's asset side term structure and liability side term structure, so as to support the effective play of the bank's financial intermediary role. (3) Further analysis shows that the of interest rate derivatives significantly reduces the volatility of bank earnings. This study makes it clear that the use of interest rate derivatives has a positive impact on the commercial banks, which provides evidence for the further development of interest rate derivatives market in China.

This paper examines the effects of superstitious psychology on investors’ decision making in the context of Mercury retrograde, a special astronomical phenomenon meaning “everything going wrong”. Using natural experiments in the Chinese stock market, we find a significant decline in stock prices, approximately -3.14% in the vicinity of Mercury retrogrades, with a subsequent reversal following these periods. The Mercury effect is robust after considering seasonality, the calendar effect, and well-known firm-level characteristics. Our mechanism tests are consistent with model-implied conjectures that stocks covered by higher investor attention are more influenced by superstitious psychology in the extensive and intensive channels. A superstitious hedge strategy motivated by our findings can generate an average annualized market-adjusted return of 8.73%.

The study reveals that the COVID-19 crisis has had a strong but one-off negative impact on the hedge fund industry. It also shows that during the new coronavirus pandemic, the main components of the hedge fund industry achieved only partially their main investment goal, i.e. they as a whole provided a hedge of the investment risk but did not produce higher than the market return in the conditions of a growing capital market. In this situation, due to the relatively stable М&A market, the Event-Driven Risk Arbitrage strategy was undoubtedly most successful, followed by the Emerging Markets, the Global Macro and the Long/Short Equity strategies. The worst performance was reported for the Fixed Income Arbitrage strategy due to the currently overvalued bond markets and to the expectations for higher inflation rates in the countries with developed capital markets.

The aim of the study is to investigate the behaviour of the Chinese government bond yield curve before and during the COVID-19 pandemic. Its methodology comprises the techniques of time series analysis, correlation analysis and dimensionality reduction. The main empirical results show that in the pandemic period, the behaviour of the Chinese government bond yield curve differs significantly from that before the outbreak of COVID-19. This is evidenced by the weaker correlations among the analysed yields, the presence of anomalies, heterogeneous behaviour and probable arbitrage opportunities at the long-term end of the studied yield curve, as well as the significant changes in the main factors of its dynamics. The research also reveals that prior to the COVID-19 pandemic, portfolios composed of Chinese government bonds could be well protected against interest rate risk even by using traditional parallel shift immunization techniques. However, after the outbreak of the COVID-19 pandemic the use of such techniques would be relatively effective for portfolios of Chinese government bonds with maturities between 1 and 5 years, while portfolios that include Chinese government bonds with maturities greater than 7 years should be either hedged against all the three factors of the yield curve dynamics or be used only for arbitrage strategies.

We examine empirically and theoretically the effects of industrial robot adoption on corporate financing. Empirically, using firm-level panel data on robot deployment in China, staggered across both provinces and industries, we find that robot adoption reduces the cost of debt and increases leverage. We hypothesize that the underlying reason is that being a substitute for labor, robots provide a hedge against fluctuations in labor costs. A model based on this hedging argument delivers additional testable predictions concerning determinants of the relation between robot adoption and corporate financing, which are borne out in the data, providing support for the proposed mechanism. Our evidence is inconsistent with alternative channels behind the observed relations.