Modeling dynamic conditional correlations with leverage effects and volatility spillover effects: Evidence from the Chinese and US stock markets affected by the recent trade friction

In this article, we investigate the dynamic conditional correlations (DCCs) with leverage effects and volatility spillover effects that consider time difference and long memory of returns, between the Chinese and US stock markets, in the Sino-US trade friction and previous stable periods. The widespread belief that the developed markets dominate the emerging markets in stock market interactions is challenged by our findings that both the mean and volatility spillovers are bidirectional. We do find that most of the shocks to these DCCs between the two stock markets are symmetric, and all the symmetric shocks to these DCCs are highly persistent between Shanghai’s trading return and S&P 500′s trading or overnight return, however all the shocks to these DCCs are short-lived between S&P 500′s trading return and Shanghai’s trading or overnight return. We also find clear evidence that the DCC between Shanghai’s trading return and S&P 500′s overnight return has a downward trend with a structural break, perhaps due to the “America First” policy, after which it rebounds and fluctuates sharply in the middle and later periods of trade friction. These findings have important implications for investors to pursue profits.     

成交量、持仓量对我国大豆类期货价差波动影响的实证分析

采用GARCH(1,1)模型就成交量、持仓量对大豆类期货价差波动率的影响进行实证分析,结果显示：当期成交量、持仓量对大豆期货价差波动的整体影响是显著的;滞后成交量、持仓量对大豆期货价差波动的整体影响也是显著的;当成交量、持仓量同时进入条件方差方程时,它们对大豆类期货价差波动的影响整体上也是显著的。这一结论揭示了我国大豆期货市场信息传递过程,验证了我国大豆期货市场的信息非有效性,对期货市场投资者以及期货市场监管者具有一定的借鉴意义。     

President’s Tweets,US-China economic conflict and stock market Volatility: Evidence from China and G5 countries

This study provides empirical evidence that the tweets from US President Donald J. Trump influence the trading decisions of investors worldwide. We examine the effects of Trump’s tweets related to China on stock market volatility in China and the G5 countries. Our results show that Trump’s original tweets related to the US-China economic conflict expand volatility in stock markets worldwide, and the US-China trade friction intensifies this effect. Furthermore, Trump’s tweets with different sentiments have different impacts on the returns of global stock markets. Our findings confirm that international investors may make their investment decisions based on information conveyed in these tweets.     

Analysis of the impact of Sino-US trade friction on China’s stock market based on complex networks

This paper analyzes the impact of the Sino-US trade friction incident in 2018 on China's stock market by using the complex network methods. Firstly, we divide the Sino-US trade friction incident in 2018 into four research periods. Based on the GARCH-BEKK model and the Planar Maximum Filter Graph (PMFG) algorithm, the volatility spillover network between China's stock market sectors and the stock price correlation network of China's stock market corresponding to the above four research periods are constructed. Next, from the perspective of sectors in stock market, we use various network centrality indicators to build a systematic importance comprehensive evaluation index of industry sectors in the stock market through the principal component analysis method, to explore the impact of the Sino-US trade friction incident on the risk spillover effects of sectors in China's stock market. From the perspective of the overall stock market, we analyze the impact of Sino-US trade friction incident on the overall stability of the stock market through calculating the network topology indicators and conducting simulation experiments. Finally, the main factors affecting the stability mechanism of China's stock market are studied through the probit model. The results show that: (1) The risk spillover effect of various sectors in China's stock market changes significantly in different periods of Sino-US trade friction, and there are obvious cyclical rotation effects among various sectors (2) When some weighted stocks in the stock market abnormally fluctuate or suffer targeted shocks, the China's stock market's ability to maintain stability is weak, and the Sino-US trade friction will reduce the stability of China's stock market, and the higher the intensity of trade friction incident is, the more obvious the impact of the incident is. (3) The important factors that affect the abnormal fluctuations in China's stock market include four types of indicators: the stock market network structure, the fluctuation of important international stock indexes, the fluctuation of commodity prices in the international market, and the domestic macroeconomic indicators. This study provides a reference for China's financial regulatory authorities to conduct macro-prudential management, control systemic risks, and maintain the stability of financial market.     

Cross-region risk spillover between the stock and stock index futures markets under exogenous shocks

By integrating the stock and futures markets of mainland China and Hong Kong into the same financial system, we explore the cross-region risk spillovers between the stock market and stock index futures market under the impact of exogenous events. We find evidence of significant risk spillovers between the two stock markets, and confirm that exogenous shocks, including the adjustments of regulatory policies of mainland China and 2019 Hong Kong Protest, can significantly affect the volatility spillover across assets and markets. Our findings can potentially help regulators and investors understand the cross-region risk conduction and assess portfolio risk after exogenous event.     

The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods

Based on the new perspective of high-dimensional and time-varying methods, this paper analyzes the contagion effects of US financial market volatility on China’s nine financial sub-markets. The results show evidence of non-linear Granger causality from the US financial volatility (VIX) to the China’s financial markets. Increased US financial volatility has a negative next-day impact on the stock, bond, fund, interest rate, foreign exchange, industrial product and agricultural product markets, and a positive next-day impact on the gold and real estate markets. US financial volatility has the greatest impact on industrial product market, following by stock, agricultural product, fund, real estate, bond, gold, foreign exchange, and interest rates. Major risk events such as the global financial crisis can cause an enhanced contagion effect of US financial volatility to China's financial markets. This paper supports the achievements of China's actions to prevent and resolve major financial risks in the period of the COVID-19 epidemic.     

Testing the stabilization hypothesis in the UK short-term interest rates: Evidence from a GARCH-X model

The objective of this paper is to examine the validity of one of the recurring arguments made against futures markets that they give rise to price instability. The paper concentrates on the impact of futures trading on the spot market volatility of short-term interest rates. The analytical framework employed is based on a new statistical approach aiming to reconcile the traditional models of short-term interest rates and the conditional volatility processes. More specifically, this class of models aims to capture the dynamics of short-term interest rate volatility by allowing volatility to depend on both scale effects and information shocks. Using a GARCH-X and asymmetric GARCH-X model four main conclusions emerge from the present study. First, the empirical results suggest that there is an indisputable change in the nature of volatility with evidence of mean reversion after the onset of futures trading. Second, the information flow into the market has improved as a result of futures trading. Third, a stabilization effect has been detected running from the futures market to the cash market by lowering volatility levels and decreasing the risk in the spot market. Finally, trying to capture the leverage effect the findings suggest that positive shocks have a greater impact on volatility than negative shocks.     

全球视角下美国对华加征关税的福利影响——基于服装业的局部均衡模拟分析

基于2017年全球服装产品生产与贸易的有关数据,利用可计算局部均衡模型从全球视角实证考察美国加征关税在行业层面的福利影响。结果表明：第一,美国对华服装产品加征关税具有比较显著的贸易破坏效应,但由于贸易偏转效应,中国服装对欧盟、日本等第三方市场的出口将有所增加;第二,在全球化背景下,由于贸易转移效应的存在,加征关税对美国服装产业的救济效果非常有限;第三,美国对中国服装产品加征关税导致美国社会净福利损失要远高于中国社会净福利损失。研究结论对如何应对当前中美贸易摩擦具有一定借鉴意义。     

Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China

We develop a skewness-dependent multivariate conditional autoregressive value at risk model (SDMV-CAViaR) to detect the extreme risk transmission channels between the Chinese stock index futures and spot markets. The proposed SDMV-CAViaR model improves the forecast performance of extreme risk by introducing the high-frequency realized skewness. Specifically, the realized skewness has a significant impact on the spillovers, but the realized volatility and realized kurtosis do not, which implies that the jump component plays an important role in extreme risk spillovers. The empirical results indicate there are bidirectional extreme risk spillovers between the stock index futures and spot markets, the decline of one market has direct and indirect channels to exacerbate the extreme risk of the other market. Firstly, the market decline will directly increase the extreme risk of related markets by decreasing market returns. Besides, the decline will indirectly increase the extreme risk by increasing the negative realized skewness and extreme risk spillovers.     

中美贸易中知识产权摩擦及应对策略

近些来,中美知识产权贸易摩擦日渐成为中美贸易摩擦的焦点,已成为中国企业对美出口的最大障碍。本文对中美知识产权贸易摩擦的特点、原因进行了分析,结合我国的实际情况,从政府及企业两个层面提出了应对中美知识产权贸易摩擦的策略。     

中美贸易冲突下中国制造业企业发展问题及对策探讨

近年来,随着中美经济合作程度的日益加深和双方国际贸易逆差的逐渐加大,中美贸易冲突愈演愈烈。以中美贸易冲突为经济背景,以中国制造业企业为研究对象,对中美贸易摩擦的现状进行梳理,总结了企业在核心技术及重要制造元件和产品出口销售方面所面临的问题,并从企业内、外部两方面提出了企业东方管理策略:(1)“盖有非常之功,必待非常之人”,注重人才价值,提升专业素养;(2)“革弊,须从源头理会”,聚焦核心技术,推动自主创新;(3)“上下同欲者胜”,人心归一,竞合共赢;(4)“知己知彼,百战不殆”,关注市场趋势,及时调整战略:(5)“君子藏器于身,待时而动”,顺应“一带一路”,拓展海外市场。     

Index futures trading and stock price volatility: Evidence from Denmark and France

This study uses an EGARCH methodology to investigate the impact of index futures trading on the price volatility of two European stock markets. The results show that index futures trading has changed the distribution of stock returns in Denmark and France, however, it has not increased stock price volatility. There is evidence that futures trading has dampened stock price fluctuations in France. The results further show that stocks in Denmark and France exhibit strong volatility persistence and asymmetry, especially during the post-futures period.     

Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration

Given that the United States is an engine of global stock market while China is the largest emerging market with a cornucopia of anomalies in particular, it is vital to investigate the risk-return relationship in the two markets. This paper brings new insights not only into risk-return tradeoff, but also to the leverage effect, with the application of the fractionally co-integrated vector auto-regression (FCVAR) model capturing the fractional cointegrated relationship and long memory property. Results show that China stock markets own the property of double long memory but the US markets don’t. Most of all, in the US market, a positive risk-return tradeoff exists for the whole sample while after the crisis, even we find the negative relation, it’s not a volatility feedback effect but low risk and high returns. However, there is only a volatility feedback effect in China stock markets. Besides, there is a leverage effect in the US market, while Chinese market exhibits a reverse one, another anomaly, indicating significant difference in the two markets again.     

Is bitcoin a safe haven or a hedging asset? Evidence from China

Based on daily data about Bitcoin and six other major financial assets (stocks, commodity futures (commodities), gold, foreign exchange (FX), monetary assets, and bonds) in China from 2013 to 2017, we use a VAR-GARCH-BEKK model to investigate mean and volatility spillover effects between Bitcoin and other major assets and explore whether Bitcoin can be used either as a hedging asset or a safe haven. Our empirical results show that (i) only the monetary market, i.e., the Shanghai Interbank Offered Rate (SHIIBOR) has a mean spillover effect on Bitcoin and (ii) gold, monetary, and bond markets have volatility spillover effects on Bitcoin, while Bitcoin has a volatility spillover effect only on the gold market. We further find that Bitcoin can be hedged against stocks, bonds and SHIBOR and is a safe haven when extreme price changes occur in the monetary market. Our findings provide useful information for investors and portfolio risk managers who have invested or hedged with Bitcoin.     

Modelling the iron ore price index: A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource, predicting its price is strategically important for risk management at related enterprises and projects. Based on a signal decomposition technology and an artificial neural network, this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China’s iron ore futures and spot markets, and to forecast the price index series of China’s and international iron ore spot markets from the futures market. The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets. However, the forward price in China’s iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market, and was therefore unable to fully disseminate domestic iron ore market information. The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets. The results are valuable references for the early-warning and management of the related enterprise project risks.     

Trade friction and price discovery in the USD–CAD spot and forward markets

This paper examines the impact of trade friction on price discovery in the USD–CAD spot and forward markets. Using the recently developed fractionally cointegrated vector autoregressive (FCVAR) model, we investigate how the foreign exchange spot and forward markets respond to trade friction. We consider two major trade friction events: the United States–Mexico–Canada Agreement and the recent trade friction between Canada and China. Both events show that the forward market plays a dominant role in price discovery, and the influence of the forward market increases as trade tension increases. By comparing the fractional and non-fractional models, we find that the fractional model fits the data better and has superior forecasting performance to the cointegrated vector autoregressive (CVAR) model.     

The role of oil futures intraday information on predicting US stock market volatility

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.     

Time-varying asymmetric volatility spillover between global markets and China’s A,B and H-shares using EGARCH and DCC-EGARCH models

This paper investigates the volatility spillover and dynamic conditional correlation between three types of China’s shares including A, B and H-shares with 12 major emerging and developed markets from 2002 to 2017 using EGARCH and multivariate DCC-EGARCH models. Both models found that Chinese equities are more related with their neighbouring countries such as Singapore, Japan, Australia and ASEAN-5 than with US, Germany and UK. The EGARCH model, with an auxiliary term added to capture the volatility spillover, found no volatility spillover between A-share markets and other advanced and emerging markets during the GFC and extended-crisis periods while this behaviour is not observed for B-share and H-share markets. However, the multivariate DCC model found strong evidence of contagion effect in both return correlations and volatility spillover for all China’s markets. In addition, both models found increased regional and global integration in A-share and B-share markets but not the H-share market. Finally, the results from both models provide clear evidence of distinct behaviours associated with return and volatility spillover in these three share types, suggesting foreign investors should consider the heterogeneity in volatility spillover and return correlations of these Chinese share types when forming investment strategies.     

我国贸易失衡的结构特征、形成机制及政策启示

本文对我国贸易失衡的结构特征和原因进行了研究,并讨论了相应的政策启示.研究发现,近年来我国贸易顺差占GDP比重与其他新兴经济体相比并不大,但对欧美的贸易顺差以及对日、韩的贸易逆差的绝对规模处于上升趋势;从地区层面看,贸易失衡主要由贸易规模最大的几个省份引起;加工贸易占比越高的行业,贸易顺差规模越大.本文的政策启示如下:...     

Research on the time-varying effects among green finance markets in China: A fresh evidence from multi-frequency scale perspective

Green finance is an essential instrument for achieving sustainable development. Objectively addressing correlations among different green finance markets is conducive to the risk management of investors and regulators. This paper presents evidence on the time-varying correlation effects and causality among the green bond market, green stock market, carbon market, and clean energy market in China at multi-frequency scales by combining the methods of Ensemble Empirical Mode Decomposition Method (EEMD), Dynamic Conditional Correlation (DCC) GARCH model, Time-Varying Parameter Vector Autoregression with Stochastic Volatility Model (TVP-VAR-SV), and Time-varying Causality Test. In general, the significant negative time-varying correlations among most green finance markets indicate a prominent benefit of risk hedging and portfolio diversification among green financial assets. In specific, for different time points and lag periods, the green finance market shock has obvious time-varying, positive and negative alternating effects in the short-term scales, while its time delay and persistence are more pronounced in the medium-term and long-term scales. Interestingly, a positive event shock will generate positive connectivity among most green finance markets, whereas a negative event including the China/U.S. trade friction and the COVID-19 pandemic may exacerbate the reverse linkage among green finance markets. Furthermore, the unidirectional causality of “green bond market - carbon market - green stock and clean energy markets” was established during 2018–2019.