Based on the connectedness time-frequency domain decomposition method are adopted in this paper. With the help of network topology for visualization, the characteristics and transmission path of financial risks in the time-frequency domain under major emergencies are studied. The results show that after the occurrence of MPEs, the level of risk spillover in China's financial market usually decreases in the short term, medium term and long term. When the policy has a long time lag or the market reaction is not timely, the medium term risk spillover will be higher than the short term risk spillover.

Using the Boehmer, Jones, Zhang, and Zhang (2021) algorithm, we identify a broad swath of marketable retail investor orders in the U.S. market during the pandemic. The marketable retail trading volumes rapidly rise from $325 billion in 2019 to $852 billion at mid-2020, and stay high for the next two years. The retail order flows positively predict cross-sectional returns over various horizons, and are associated with wider future effective spreads and higher future volatilities, as well as less market participations by high frequency traders and short-sellers. We find supportive evidence for informed and uninformed retail hypotheses.

This study is the first to examine China’s crude oil options market. Using high-frequency data and three different price discovery measures, we conduct a rigorous analysis and find that after its first 8 months of operation, China’s crude oil options market has already played an important role in price discovery. Factors such as volume, volatility, and speculation can impact its price discovery ability. We also find a unique phenomenon in China’s crude oil derivatives market, namely that speculative activity mainly occurs in the futures market and adds to the price discovery of the futures market rather than to the options

In the era of big data, asset pricing is influenced by various factors, which are extracted from multi-source heterogeneous information, such as high frequency market and sentiment information, low frequency firm characteristic and macroeconomic information. Especially, low frequency policy information plays a significant role in the long-term pricing in China but it is barely investigated due to its textual form. To this end, we first extract policy variables from major national development plans (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using Natural Language Processing (NLP) technique and Dynamic Topic Model (DTM). However, traditional models are inadequate for mixed frequency data modeling and feature extraction. Then, we propose a mixed frequency deep factor asset pricing model (MIDAS-DF) that solves the asset pricing problems under the mixed frequency data environment through mixed data sampling (MIDAS) technique and deep learning architecture. Time-varying latent factors and factor loadings can be modeled from mixed frequency data directly in a nonlinear and data-driven way. Thus, the MIDAS-DF model is able to learn the nonlinear joint-patterns hidden in multi-source heterogeneous information. Our empirical studies of 4939 stocks on the Chinese A-share market from January 2003 to July 2022 demonstrate that low frequency policy information has profound impacts on asset pricing, which anchors the long-term pricing direction, and high frequency market and sentiment information have significant influences on stock prices, which optimize the short-term pricing accuracy, they together enhance the pricing effects. Consequently, pricing effects the MIDAS-DF model outperform the five competing models on individual stocks, various test portfolios, and investment portfolios. Our research about heterogeneous information provides implications to the government and regulators for decision-support in policy-making and our investment portfolio is of great importance for investors’ financial decisions.

This paper investigates the intertemporal relation between expected aggregate stock market returns and conditional variance considering periodic trading breaks. We propose a modified version of Merton’s intertemporal asset pricing model that merges two different processes driving asset prices, (i) a continuous process modeling diffusive risk during the trading day and, (ii) a discontinuous process modeling overnight price changes of random magnitude. Relying on high-frequency data, we estimate distinct premia for diffusive trading volatility and volatility induced by overnight jumps. While diffusive trading volatility plays a minor role in explaining the expected market risk premium, overnight jumps carry a significant risk premium and establish a positive risk-return trade-off. Our study thereby contributes to the ongoing debate on the sign of the intertemporal risk-return relation.

We establish a framework to study the factor structure in stock variance under a high-frequency and high-dimensional setup. We prove the consistency of conducting principal component analysis on realized variances in estimating the factor structure. Moreover, based on strong empirical evidence, we propose a multiplicative volatility factor (MVF) model, where stock variance is represented by a common variance factor and a multiplicative lognormal idiosyncratic component. We further show that our MVF model leads to significantly improved volatility prediction. The favorable performance of the proposed MVF model is seen in both US stocks and global equity indices.

In 2013, the Shanghai Futures Exchange (SHFE) introduced a night session in Chinese metal futures markets. Using high-frequency data of gold, silver, and copper futures, we investigate the impact of night trading on intraday return predictability in Chinese metal futures markets. Firstly, we find the intraday return predictability has changed after introducing night trading: before the launch of night trading, the first half-hour daytime returns show significant predictability, whereas the first half-hour night returns exhibit forecasting power after that. Such changes can be explained by the immediate reactions of domestic investors to international news released in the evening. Secondly, the market timing strategy outperforms the always-long and buy-and-hold benchmark strategies. Thirdly, the predictability of night return is stronger on days with higher volatility and volume. Furthermore, stronger intraday predictability is associated with global news releases and positive news sentiment, suggesting enhanced connectedness of Chinese and international metal futures markets after the launch of night trading.

In recent years, numerous Chinese peer-to-peer (P2P) lending platforms have collapsed, prompting us to investigate the regulation and monitoring of the fintech industry. Using a unique dataset of P2P lending platforms in China, we investigate the effect of the information environment on regulatory monitoring and platform collapse. Using the platforms’ proximity to regulatory offices as a proxy for information asymmetry, we show that an increase in distance reduces regulatory monitoring and increases the likelihood of platform collapse. Specifically, for every 1% increase in the driving distance between the local regulatory office and a P2P lending platform’s office, the platform’s likelihood of collapse increases by 1.011%. To establish causality, we conduct a difference-in-differences analysis that exploits two exogenous shocks: government office relocation and subway station openings. We provide evidence that proximity enhances monitoring quality by facilitating soft information collection, reducing platform failures. We further find two channels of this effect: (1) the information channel through which greater regulatory distance reduces the likelihood and frequency of regulators’ on-site visits and (2) the resource-constraint channel, through which greater regulatory distance significantly increases the local regulatory office’s monitoring costs. Overall, this study highlights the importance of the acquisition of soft information for regulatory monitoring to ensure the viability of fintech firms.

This study investigates the impact of independent directors' participation in the shareholders meeting on corporate governance, and finds that the more frequently the independent directors attend shareholder meetings, the lower the degree of earnings management by the enterprise; the mechanism test shows that more information increases the probability, frequency, and severity of independent directors’ subsequent dissenting opinions; This study identified a new channel for independent directors to independently obtain true information and this is of great significance for regulators, shareholders, company board, and other stakeholders with an interest in how the information influence independent directors governance effects.

Climate change has become mankind’s main challenge. Greenhouse gas (GHG) emissions from shipping are not irresponsible for this, representing 3% of the global total; an amount equal to that of Germany’s emissions. The Fourth Greenhouse Gas Study 2020 of the International Maritime Organization (IMO) predicts that the proportion of GHG emissions from shipping will rise further, as global trade continues to recover and grow, along with the economic development of India, China and Africa. China and the European Union have proposed to include shipping in their carbon emissions trading systems (ETS). As a result, the study of the relationship between the carbon finance market and the shipping industry, attempted here for the first time, is particularly important both for policymakers and shipowners. We use wavelet analysis and the spillover index methods to explore the dynamic dependence and information spillovers between the carbon finance market and shipping. We discover a long-term dependence and information linkages between the two markets, with the carbon finance market being the dominant one. Major events, such as the 2009 global financial crisis; Brexit in 2016; the 2018 China-US trade frictions; and COVID-19 are shown to strengthen the dependence of carbon finance and shipping. We find that the dependence is strongest between the EU carbon finance market and dry bulk shipping, while the link is weaker in the case of tanker shipping. Nonetheless, carbon finance and tanker shipping showed a relatively stronger dependence when OPEC refused to cut production in 2014, and when the China-US trade dispute led to the collapse of oil prices after 2018. We show that information spillovers between carbon finance and shipping are bidirectional and asymmetric. The carbon finance market is the principal transmitter of information. Our results and their interpretation provide guidance to governments on whether (and how) to include shipping in emissions trading schemes, supporting at the same time the environmental sustainability decisions of shipping companies.