Dynamics of volatility spillover between stock market and foreign exchange market: evidence from Asian Countries

Background:The purpose of this study is to examine volatility spillover effects between stock market and foreign exchange market in selected Asian countries;Pakistan,India,Sri Lanka,China,Hong Kong and Japan.This study considered daily data from 4th January,1999 to 1st January,2014.Methods:This study opted EGARCH(Exponential Generalized Auto Regressive Conditional Heteroskedasticity)model for the purpose of analyzing asymmetric volatility spillover effects between stock and foreign exchange market.Results:The EGARCH analyses reveal bidirectional asymmetric volatility spillover between stock market and foreign exchange market of Pakistan,China,Hong Kong and Sri Lanka.The results reveal unidirectional transmission of volatility from stock market to foreign exchange market of India.The analysis reveals no evidence of volatility transmission between the two markets in reference to Japan.Conclusions:The result of this study provide valuable insights to economic policy makers for financial stability perspective and to investors regarding decision making in international portfolio and currency risk strategies.

Volatility spillover effect between financial markets:evidence since the reform of the RMB exchange rate mechanism

The volatility spillover effect between the foreign exchange and stock markets has been a major issue in economic and financial studies.In this paper,GC-MSV model was used to study the spillover effect between the foreign exchange market and the stock market after the reform of the RMB exchange rate mechanism.The empirical results show that there is a negative correlation of dynamic price spillovers between the foreign exchange and stock markets.There are asymmetric volatility spillover effects between these two markets for both RMB stages—continued RMB appreciation or constant RMB shock(a significant reduction in appreciation).However,this has been reduced over time.In conclusion,The RMB exchange rate is a key variable that can affect the internal and external equilibrium of the national economy in an open economic environment,and the stock market is capable of quickly reflecting subtle changes in the real economy.In order to keep the stability of the financial markets and the healthy and rapid development of national economy,some suggestions were proposed.

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic:evidence from DCC-GARCH and wavelet analysis

This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns,namely Bitcoin,Ethereum,Stellar,Ripple,Tether,Cardano,Litecoin,and Eos from November 17,2019,to January 25,2021.The study captures the financial behavior of investors during the COVID-19 pandemic as a result of national lockdowns and slowdown of production.Three different methods,namely,EGARCH,DCC-GARCH,and wavelet,are used to understand whether cryp-tocurrency markets have been exposed to extreme volatility.While GARCH family models provide information about asset returns at given time scales,wavelets capture that information across different frequencies without losing inputs from the time horizon.The overall results show that three cryptocurrency markets(i.e.,Bitcoin,Ethereum,and Litecoin)are highly volatile and mutually dependent over the sample period.This result means that any kind of shock in one market leads investors to act in the same direction in the other market and thus indirectly causes volatility spillovers in those markets.The results also imply that the volatility spillover across cryptocurrency markets was more influential in the second lockdown that started at the beginning of November 2020.Finally,to calculate the financial risk,two methods—namely,value-at-risk(VaR)and conditional value-at-risk(CVaR)—are used,along with two additional stock indices(the Shanghai Composite Index and S&P 500).Regardless of the confidence level investigated,the selected crypto assets,with the exception of the USDT were found to have substantially greater downside risk than SSE and S&P 500.

Analysis of Volatility Spillover Effect of Soybean Price between Domestic and International Markets

Sharp fluctuation of soybean prices in international and domestic markets has caused big risks for both domestic soybean producers and processing enterprises in recent years. It also increases the difficulties in implementing price stabilization policy for the government. This paper analyzes the volatility spillovers in soybean prices between international and domestic markets using the multivariate VAR-BEKK-GARCH model based on the data set from December 22,2004 to December 19,2014. The estimate results indicate that there are volatility spillover effects from domestic futures market to spot market and bilateral spillover between international futures market and domestic spot market. In order to prevent market manipulation and to reduce the impacts of price volatility in international soybean market on Chinese market,this paper proposes the following policy measures such as establishing early warning mechanism for soybean price fluctuations,improving soybean futures contract design and strengthening trading risk management mechanism,amplifying information disclosure system,and regularizing speculation activities of big traders.

Volatility Spillover Effect of the International Crude Oil Futures Price on Composite and Sector Indices between the Chinese and Australian Stock Markets

As a type of non-renewable industrial resource,petroleum is of great strategic significance to the development of each nation.Ever since the 19th century,an array of oil crises have incurred certain downturn of the world economy.Pertinent studies have implied that financial crisis is always prone to be accompanied with oil crisis,yet the relevance of crude oil to the stock market,the barometer of the macro-economy,is ambiguous.In order to avoid the risks induced by the volatility of oil price,the oil futures market has appeared,and at the same time,the financial property of crude oil has become far more evident.Owing to lack of mature mining and refining technology,China still imports large amounts of oil from abroad at present.Thus,the economy of China is susceptible to fluctuation in petroleum price.As for Australia,the only net importer among the member countries of the International Energy Agency(IEA),it fails to attain the target of holding 90 days of fuel reserves set by the agency.However,in 2013,Australian Lincoln Energy announced that a gigantic shale oil field with an estimated value of 21 trillion US dollars was found in the South of Australia,and that if that field is mined,Australia has the possibility to turn into a net exporter of crude oil.It can be expected that the Australia’s economic conditions would be closely related to the international oil to a certain extent.Based on the approaches of the first difference and co-integration,this paper delves into the volatility spillover effect of crude oil futures on the Chinese and Australian stock markets.According to the empirical findings,in the short run,the price of crude oil futures has a greater impact on the Australian composite index than on the Chinese composite index.However,crude oil futures are negatively related to the Chinese composite index in the long run.The price of crude oil futures has no significant impact on the Chinese sector indices,but it has a certain impact on the Australian utilities,energy,materials,and industrial sector indices.In the Chinese stock market,the movement of short-run effect to long-run effect of crude oil futures on sector indices is in the reverse direction.Finally,the price of crude oil futures has a significant volatility spillover effect only on the Australian utilities sector index.

Regime specific spillover across cryptocurrencies and the role of COVID‑19

The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes,while considering three pricing factors and the effect of the COVID-19 outbreak.To do so,we apply a Markov regime-switching(MS)vector autoregressive with exogenous variables(VARX)model to a daily dataset from 25-July-2016 to 1-April-2020.The results indicate various patterns of spillover in high and low volatility regimes,especially during the COVID-19 outbreak.The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19,especially in the high volatility regime.Notably,the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak,which is consistent with the notion of contagion during stress periods.

The Spillover Effect between Futures and Spot Price of Agricultural Products:A Case Study of Soybean Products of China

Taking soybean products as an example and using the daily price data of 2007-2015,this paper established the error correction model and BEKK-GARCH model,and made an empirical study on the spillover effect of futures and spot price of agricultural products of China. According to this study,there were mean spillover effect and two-way volatility spillover effect in futures and spot price of soybean,soybean oil,and soybean meal; soybean futures prices significantly guided the spot price; in the price linkage between the types,the price relationship between the soybean meal and soybean was closer than between the soybean oil and soybean.

A Comparison of Spillover Effects before, during and after the 2008 Financial Crisis

This paper applies graphical modelling to the S & P 500, Nikkei 225 and FTSE 100 stock market indices to trace the spillover of returns and volatility between these three major world stock market indices before, during and after the 2008 financial crisis. We find that the depth of market integration changed significantly between the pre-crisis period and the crisis and post-crisis period. Graphical models of both return and volatility spillovers are presented for each period. We conclude that graphical models are a useful tool in the analysis of multivariate time series where tracing the flow of causality is important.

金融系统与装备制造业风险传染研究——基于波动溢出网络视角

当前各类经济风险交叉传染,装备制造业作为我国双循环背景下实现制造业升级的关键战略产业,其与金融系统间的风险溢出效应备受关注。为探究金融系统与装备制造业间的风险溢出效应的时变特征,本文从波动溢出网络的视角,分析金融系统与装备制造业所组成的风险网络内的风险传染机制。首先,用TVP-VAR模型计算装备制造业细分产业与金融子市场间的风险溢出效应值;其次,以细分产业与子市场为节点,以溢出效应值为连边构建有向加权风险溢出网络,分析网络整体特征变化;最后,分析网络的节点特征变化,探究内在机制。本研究发现,风险系统当中,主导的溢出效应来自装备制造业,向金融系统的股票市场与基金市场风险溢出,且这种溢出效应与经济运行状况成反比。本文的研究结论有助于更好地理解金融系统与装备制造业间的风险传染效应,对监管机构加强审慎监管、企业规避风险具有借鉴意义。

跨境资本与人民币汇率的非对称波动耦合效应

构建向量误差修正‒广义自回归条件异方差‒非对称BEKK(VECM-GARCH-ABEKK)模型,从产业资本和金融资本两个维度,研究跨境资本与人民币汇率波动的非对称耦合效应。研究发现,产业资本和金融资本与人民币汇率具有显著的持续性、集聚性波动特征,且两类跨境资本与人民币汇率之间的波动溢出存在差异化的非对称耦合效应。研究提出优先针对产业资本外流风险出台相关政策,构建“宏观审慎+微观监管”监管框架,降低外汇市场超调风险,利用人民币离岸交易构筑资本跨境流动缓冲区等对策建议。

产业链视角下油料产品价格溢出效应的对比分析——基于三元VAR-BEKK-GARCH(1,1)模型

为了更好地理解油料产品价格的波动规律,提升国内油料产品价格研究水平,以大豆、油菜、花生为油料作物的典型代表,以2011年2月—2019年12月油料产业链上各个环节月度价格数据为基础,采用三元VAR-BEKK-GARCH(1,1)模型,研究油料产业链上、中、下游产品的价格溢出效应。结果表明:大豆产业链各环节产品均存在价格均值溢出效应,油菜产业链上、中游之间没有价格均值溢出效应,花生产业链上、下游之间不存在价格均值溢出效应;在大豆、油菜和花生产业链中,各环节产品间价格双向波动溢出效应明显。政府应通过建立农业产业链价格监测体系、多元化的价格监测体系、稳定的市场调节机制等措施,促进相关农业产业链的协调发展。

中国与国际玉米价格的波动关系分析

本文在政策分析的基础上,建立VAR-BEKK-GARCH模型进行实证分析。首先,国际玉米价格的波动幅度比中国价格波动幅度大,临储政策取消后,中国价格波动幅度变大。其次,临储期间,中国价格的预期波动可以影响国际价格的波动,而国际价格的随机波动可以影响中国价格的波动。在生产者补贴期间,中国价格的预期波动和随机波动均可影响国际价格的波动。再次,中国价格的影响力在中国市场和进口市场占据主导地位,与国产玉米供给本国市场的数量地位相符。最后,中国玉米进口的不断增加将降低对本国市场价格的主导能力,需重视通过“期货+保险”规避价格风险,并分散玉米进口源,以提高市场主导力。

经济不确定性和地缘政治风险对国内外原油价格的溢出性分析

本文采用多元GARCH模型对条件相关性进行建模,研究2018年3月—2022年6月“一带一路”沿线中东国家油价、国内原油价格、经济政策不确定性和地缘政治风险之间的全球动态关系。实证结果表明,动态条件相关模型拟合数据最好,表明国内外油价与经济不确定性以及地缘政治风险之间存在时变的溢出效应,其中迪拜原油价格与国内原油价格之间的动态条件相关性较高。

绿色债券市场和能源行业股票市场的风险溢出效应研究

本文使用四种多元GARCH模型分析中国绿色债券市场与传统能源行业股票市场之间的动态条件相关和波动性溢出效应。实证结果表明,DCC-GARCH模型拟合样本数据最好,并且能够利用该模型构建套期保值比率和最优投资组合权重。此外,在绿色债券和传统能源产业的股市之间,有着显著的动态关系,并且呈现较为明显地双向波动性溢出效应。最后,本文利用DCC模型中的条件波动率来估计套期保值比率,为投资者在绿色债券市场和传统能源股票市场上投资提出套期保值建议,以规避投资风险。

波动溢出网络视角下的不确定性冲击与能源价格波动

本文基于TVP-VAR-DY模型选取国内外8种不确定性指标和3种国际能源大宗商品价格指数进行分析,得到如下结论与建议:第一,全球能源不确定性、全球经济政策不确定性和气候政策不确定性对能源市场存在显著的溢出效应;第二,原油价格波动引发了整个能源市场价格波动,并且还导致其他不确定性的上升;第三,原油价格波动在2007~2009年金融危机期间受到的影响较大,动力煤价格波动在2014~2016年中国地缘政治风险上升和巴黎协定签订期间受到的影响较大,天然气价格波动在2020~022年新冠疫情和俄乌冲突发生期间受到的影响较大;第四,不同时频下波动溢出网络特征有所不同。根据以上实证结论,本文为防范能源价格波动风险提供了有益启示。

国际能源价格、中国通货膨胀与经济增长的周期波动关系——基于小波变换频带分析与多元BEKK-GARCH(1,1)模型分析

近些年来国际能源价格变动频繁,给各国经济的发展造成很大的不便与影响。文章现使用小波变换频带分析方法并结合多元GARCH-BEKK(1,1)模型,考察处在不同时间段内国际能源价格、通货膨胀与经济增长三者之间的溢出关系。实证结果表明:三者之间有波动溢出效应,但在不同的周期,其波动效应不同,短期来看三者之间存在双向传递效应,随周期变长发展为单向溢出与双向溢出,长期则国际能源价格向通货膨胀波动溢出效应更为显著。伴随中国经济迈入一个新时期,能源问题一定会成为中国未来经济发展需要重视的挑战,因此需要相关政府机构加快完善国内能源市场化建设,提高能源使用效率,努力减缓国际能源价格波动对中国经济的负面冲击。

中国、巴西、俄罗斯股票市场间波动溢出效应的实证研究——基于小波多分辨率分析的多元BEKK-GARCH模型

互联互通是世界经济飞速发展的动力源,在促进了各国之间的经济往来的同时,也加深了各国股票市场之间的联系。中国与巴西、俄罗斯作为经济合作程度较高,三个国家的股票市场也有很多相似的特征。因此,研究中国、巴西、俄罗斯股票市场之间的波动溢出效应有很重要的学术意义。本文选取中证500指数、巴西IBOVESPA指数、俄罗斯RTS指数,作为中国内地、巴西、俄罗斯股票市场的代表,对2014年1月1日至2019年12月31日的收益率序列做波动溢出效应的实证研究。格兰杰因果检验的结果表明,仅有俄罗斯股票市场收益率的变动是中国股票市场、巴西股票市场收益率变动的格兰杰原因。基于BEKK-GARCH模型,研究发现中国、巴西、俄罗斯的股票市场都体现出波动的聚集性和持久性溢出效应,并且中国与巴西股票市场、中国与俄罗斯股票市场、巴西与俄罗斯股票市场之间都存在双向的波动溢出效应。本文将小波分析引入研究,与多元BEKK-GARCH(1,1)模型相结合,把三个国家股票市场的收益率序列分解为不同尺度的信号,对应建立不同交易周期下的波动溢出效应模型。结果发现在短期、中期和长期的时间尺度下,三个股票市场相互之间依然显著存在双向溢出效应。文章建议政策制定者关注股票市场间的溢出关系,完善监管体制,并且出台抵御市场剧烈波动的政策。同时,又从投资者的角度出发,提醒投资者不要忽视高关联程度的股票市场的重要信息。

巴西农产品期货市场价格发现功能的实证研究——基于VECM-PT-IS与DCC-MGARCH-t模型

农产品期货市场是金融体系中重要的组成部分,其价格发现和套期保值的基本功能对农产品市场价格的长期稳定和准确反映真实供求关系有重要的意义。中国由于农产品期货市场建立时间不长,行业交易准则一直处于建设期。巴西是世界上最大的农产品生产国之一,在全球农业市场中具有重要地位。同样作为发展中国家以及金砖国家的巴西,其农产品期货市场较中国更为发达。本论文通过巴西和中国的大豆、玉米、食糖三种农产品期现货价格发现功能的对比,分析两市领先与滞后关系、价格贡献程度差异和波动溢出效应。通过模型分析期货市场的价格发现功能相比现货市场的具体影响,并提出有效的、可服务于中国农产品期货市场的政策建议。本文选取巴西农业期货交易所2017年1月1日至2019年12月31日提供的期现货每日平均结算价作为实证分析的对象,使用ADF平稳性检验、Johansen协整检验与Granger因果检验等方法检验数据平稳性及数据间的相关关系外,创新性地引用VECM误差修正模型、公共因子(PT-IS)、DCC-MGARCH-t模型研究两个市场价格相互引导关系、贡献差异程度和波动溢出效应。实证结果表明,期货两市价格存在明显的相互引导关系,期货价格贡献度明显高于现货价格贡献度,体现农产品期货具有价格发现的功能。且农产品期货两市之间的波动溢出效应是双向的,内部波动的影响往往大于外部冲击的影响。

Return and Volatility Spillovers Effects:Study of Asian Emerging Stock Markets

This paper examines the extent of contagion and interdependence across the six Asian emerging countries stock markets(e.g., Bangladesh, China, India, Malaysia, the Philippine, and South Korea) and then try to quantify the extent of the Asian emerging market fluctuations which are described by intra-regional contagion effect. These markets experienced both fast growth and key upheaval during the sample period, and thus, provide potentially rich information on the nature of border market interactions. Using the daily stock market index data from January 2002 to December 2016(breaking the 15 years data set into three sub periods; pre-crisis, crisis, and post crisis periods);particularly make attention to the global financial crisis of 20072008. The return and volatility spillovers are modeled through the GARCH(generalized autoregressive conditional heteroscedasticity),pairwise Granger causality tests, and the forecast error variance decomposition in a generalized VAR(vector auto regression) models. This paper shows that volatility and return spillovers behave very differently over time, during the pre-crisis, crisis, and post crisis periods. Importantly, Asian emerging stock markets interaction is less before the global financial crisis period. The return and volatility spillover indices touch their respective historical peaks during the global financial crisis 20072008,however Bangladeshi market faces this condition in 20092010.

Intraday Volatility Spillover between the Shanghai and Hong Kong Stock Markets—Evidence from A+H Shares after the Launch of the Shanghai-Hong Kong Stock Connect

Using minute data of eligible A+H stocks under the Shanghai-Hong Kong Stock Connect(SHHKSC),we investigate the volatility spillover between the Shanghai and Hong Kong stock markets based on a generalized autoregressive conditional heteroskedasticity-X(GARCH-X)model with four exogenous variables,namely,volatilities of the corresponding stocks on the other market,volatilities of the indexes of both stock markets,and volatilities of the correlated stocks,which are selected using the dynamic conditional correlation model and bootstrap approach.Results show that after the launch of the SHHKSC,volatility spillovers are significant in both directions almost all the time,and the volatility spillover between the two stock markets tends to be larger when bidirectional capital flows under the SHHKSC increase or when important financial events occur.We also analyze the influences of the volatilities of correlated stocks and industries on the volatility spillover and volatilities of A+H stocks.The bidirectional volatility spillovers between Shanghai and Hong Kong stock markets do not change qualitatively after incorporating the volatilities of correlated stocks and industries in the GARCH-X model.Moreover,the average volatilities of the correlated stocks are shown to have significant influences on the volatilities of individual A+H stocks,and the influences increase when the local stock market shows a sharp rise or fall.Compared with the market indexes,the correlated stocks could be regarded as a more important and indispensable factor for individual A+H stocks’volatilities modeling,which may carry more information than the industry.