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.

This paper proposes the two-component realized EGARCH model with dynamic jump intensity (hereafter REGARCH-C-DJI model) to model and forecast stock market volatility. The key feature of our REGARCH-C-DJI model is its ability to exploit the high-frequency information as well as to capture the long memory volatility and jump dynamics. An empirical application to Shanghai Stock Exchange Composite (SSEC) index data shows the presence of high persistence of volatility and dynamic jumps in China’s stock market. More importantly, the REGARCH-C-DJI model dominates the GARCH, EGARCH, REGARCH and REGARCH-C models in terms of out-of-sample forecast performance. Our findings highlight the importance of accommodating the realized volatility, volatility components and jump dynamics in forecasting stock market volatility.

The objective of this study is to create optimal two-asset portfolios consisting of stocks from Western Europe, the United States, and the BRICS (Brazil, China, India, Russia, and South Africa), as well as sixteen commodity types during the BREXIT period. We utilized dynamic variances and covariances from the GARCH model to derive weights for the two-asset portfolios, with each portfolio consisting of one equity factor and one commodity factor. Subsequently, hedge ratios were calculated for these various assets. Our findings indicate that portfolios consisting of European stocks do not require the inclusion of commodities, whereas the other equities do.

Starting from document research, this paper analyzes the mechanism of the risk spillover effect from developed capital markets to the Chinese capital market. After that, this paper conducts an empirical study on the risk spillover effect of developed capital markets on the Chinese capital market by using the DCC-GARCH model. Then the impact degree of global major stock market fluctuations on the Chinese stock market is measured. The analysis shows that there exists a significant risk spillover effect of developed capital markets on the Chinese capital market, but the effect began to weaken after the financial crisis and the size of the spillover effect can be affected by macro factors such as geographical locations, foreign trade, and foreign investment.

本文使用事件研究法利用一分钟间隔的交易数据，结合 VECM 模型和 BEKKGARCH 模型检验不同类型的中美贸易摩擦事件冲击是否改变了离在岸汇率的联动机制。研究发现：无论是利空事件还是利好事件，事件前后离在岸汇率之间始终存在协整关系，且离岸汇率占据主导地位，当二者出现偏离时多数情况下在岸汇率向离岸汇率靠拢，且离岸汇率始终是在岸汇率的格兰杰原因；事件冲击显著增大了离岸汇率对来自在岸汇率的脉冲响应程度。当中美爆发贸易冲突并对人民币汇率产生冲击时，离在岸汇率的波动率会显著增强，此时在岸汇率向离岸汇率的波动溢出效应变得更加显著，事件冲击增强了在岸汇率向离岸市场汇率的波动溢出效应。此外，利空消息与利好消息产生的上述影响并没有显示出明显差别。

This study examines the price discovery patterns in the three BRICS countries’ stock index futures markets that were launched after 2000 – China, India, and Russia. We detect two structural breaks in these three futures price series and their underlying spot price series, and use them to form subsamples. Employing a Vector Error Correction Model (VECM) and the Hasbrouck (1995) test, we find the price discovery function of stock index futures markets generally improves over time in China and India, but declines in Russia. A closer examination not only confirms the findings of Yang et al. (2012) and Hou and Li (2013) regarding price discovery in China’s stock index markets, but also reveals the inconsistency of futures’ leading role in the price discovery process. Further, we find some evidence of day-of-the-week effects in earlier part of the sample in China, but not in India or Russia. And our GARCH model results show bidirectional volatility spillover between futures and spot in China and India, but only unidirectional in Russia.

In recent years, with the rapid expansion of commercial banks' non-standardized business, the systematic correlation between shadow banking and commercial banks in China has been gradually enhanced, which enables the partial liquidity crisis of shadow banking to spread rapidly to commercial banks, leading to the increased vulnerability of China's financial system. Based on this, we built shadow banking indexes of trusts, securities, private lending and investment, introduced the dynamic correlation coefficient calculated by the dynamic conditional correlation multivariate GARCH model into the improved CoVaR model, and used the DCC-BEKK-MVGARCH-Time-Varying CoVaR Model to measure the risk overflow contribution of shadow banking in China. We find that shadow banking and commercial banks have an inherent relationship. Due to their own risks, different types of shadow banking contribute to the risk spillover to commercial banks in different degrees. The risk correlation between shadow banking and commercial banks fluctuates.

The existing literature mainly documents the relationship between international and domestic fossil energy markets; however, empirical evidence of the dynamic relationships between fossil energy market and new energy market is lacking. This paper combines TGARCH model and copula model to explore the dynamic linkages and spillover effects between international fossil energy (crude oil, coal and natural gas) markets and China's new energy market using daily data from 4 January 2012 to 3 September 2018. The empirical results indicate that fossil energy returns and new energy returns are positive related over time. And the crude oil returns and new energy returns, as well as the coal returns and new energy returns have lower tail dependence, while there is upper tail dependence structure between natural gas returns and new energy returns. Furthermore, the extreme upside and downside risk spillover from international fossil energy markets to China's new energy market is asymmetric. Among the spillover effects, the downward risk spillover of crude oil market exerts the most significant impact on China's new energy market.

运用EGARCH模型首先考察了沪深300指数期货与股票现货市场上10大基金重仓股之间进行套期保值的效果，然后又运用EGARCH模型考察了沪深300指数期货与10只随机选取的深圳证券交易所中小企业板上市的股票之间进行套期保值的效果。发现前者的套期保值效果并不很理想，后者的效果更差。为了给投资者提供更多的、实用的套期保值工具，应该借鉴海外市场的经验，在我国推出中小市值股票指数期货和其它行业股票指数期货。最后对政策构想中的“中小板综合指数期货”的套期保值效应进行了模拟实证检验，证明这种政策构想是值得付诸实践的。

使用DCC-GARCH和分位数回归模型，本文比较分析了金融危机前后人民币与东亚六种货币汇率的相关关系，研究结果表明，第一，危机前后东亚货币与人民币都保持一定程度的同步性，但这种同步性具有明显的时变特征，总体来说，危机后各货币的同步性程度均有所增加，这可能反映了中国对东亚地区经济影响力的提升；第二，危机前后东亚货币与人民币的相关性特点具有明显的阶段性差异，危机前东亚货币表现出明显的国别差异，但危机后表现出更多的一致性，并且在人民币升值幅度较小及贬值时，才表现出与人民币的相关性，而人民币贬值幅度越大，东亚货币的贬值幅度也越大；第三，导致这种差异的主要原因在于东亚在金融危机前后所面临的宏观经济环境变化。