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.

Properties of Time-Varying Causality Tests in the Presence of Multivariate Stochastic Volatility

This paper compares the statistical properties of time-varying causality tests when errors of variables have multivariate stochastic volatility (SV). The time-varying causal-ity tests in this paper are based on a logistic smooth transition autoregressive model. The compared time-varying causality tests include asymptotic tests, heteroskedasticity-robust tests, and tests using wild bootstrap. Our simulation results show that asymptotic tests and heteroskedasticity-robust counterparts have size distortions under multivariate SV, whereas tests using wild bootstrap have better size properties regardless of type of error. In particular, the time-varying causality test with first-order Taylor approximation using wild bootstrap has better statistical properties.

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.

Volatility forecasting in Chinese nonferrous metals futures market

This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data.The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility,which are captured by long memory HAR-GARCH model.Besides,the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility.Finally,in-sample and out-of-sample forecasts are investigated,and the results show that the LHAR-CJ-G model,considering time-varyingvolatility of realized volatility and leverage effects,effectively improves the explanatory power as well as out-of sample predictive performance.

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.

基于时频波动溢出网络的中国金融机构关联性

金融机构关联性是系统性风险的主要来源,厘清时频域下金融机构关联性特征有助于防范与化解系统性风险.本文基于30家A股上市金融机构在2011年~2020年期间的高频交易数据,构建时频波动溢出网络研究中国金融机构关联性.通过引入网络密度与全局效率两个系统层面指标和入度、出度与相对影响值三个机构层面指标,以测度时频域下静态与动态关联性.研究发现:中国金融机构间的关联性主要为20天~60天的中长期关联性;在极端事件期间,往往表现为长期关联性增强和短期关联性减弱;银行部门倾向于接收偏短期的波动关联性并发出偏短期波动关联性,而证券和保险部门倾向于接收偏长期的波动关联性并发出偏短期的波动关联性.

房地产股价波动对高技术板块的溢出效应研究——基于DGC-t-MSV模型

过度的金融资产投资可能导致房地产股价泡沫,并且通过风险溢出渠道给高技术板块带来价格冲击。研究聚焦于房地产行业和高技术产业的股票价格指数,结合MODWT分解和DGC-t-MSV模型进行实证分析,结果表明:其一,“房地产-高技术”的股票价格动态相关,房地产行业价格波动的传导作用更加强烈,对高技术板块具有明显的溢出效应;其二,实施限制性政策后房地产行业的挤出效应降低,高技术及其分类产业的创新投入受到的限制逐渐减弱;其三,短、中期频繁交易是导致房地产溢价的主要原因,需要警惕高技术板块面临的溢出风险。积极引导高技术板块的价值投资,合理配置技术要素,能够有效促进高技术产业的高质量、高水平发展。

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。

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。

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。

基于时频视角的中美豆类期货特质性波动溢出效应研究

在国际粮食安全问题日益突出的背景下,识别和监管中国大豆市场面临的外部风险具有重要的现实意义。基于极差波动率,运用TVP-FAVAR-DY模型,研究了中美豆类期货市场特质性波动溢出效应,并揭示了时频视角下豆类期货波动溢出效应的异质性。研究发现,相对于收益率波动率,极差波动率能更好地捕捉极端波动风险;特质性波动成分对原序列的解释力强于公共波动成分;美国大豆期货在时域和高频波动溢出网络中处于主导地位,而中国豆油期货在中频和低频波动溢出网络中处于主导地位;时域波动溢出效应与高频结果基本保持一致,对中频和低频波动溢出效应的刻画能力有限;除了美国大豆期货,美国豆粕期货也成为中短期重要的波动溢出传递者。相关短线交易者、长期投资者和风险监管部门,均有必要增加对极差波动率、特质性波动成分和频域视角的关注。

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。

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。