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.

Intelligent design:stablecoins(in)stability and collateral during market turbulence

How does stablecoin design affect market behavior during turbulent periods?Stable-coins attempt to maintain a“stable”peg to the US dollar,but do so with widely varying structural designs.The spectacular collapse of the TerraUSD(UST)stablecoin and the linked Terra(LUNA)token in May 2022 precipitated a series of reactions across major stablecoins,with some experiencing a fall in value and others gaining value.Using a Baba,Engle,Kraft and Kroner(1990)(BEKK)model,we examine the reaction to this exogenous shock and find significant contagion effects from the UST collapse,likely partially due to herding behavior among traders.We test the varying reactions among stablecoins and find that stablecoin design differences affect the direction,magnitude,and duration of the response to shocks.We discuss the implications for stablecoin developers,exchanges,traders,and regulators.

Discovering interlinkages between major cryptocurrencies using high‑frequency data: new evidence from COVID‑19 pandemic

Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies(Bitcoin,Ethereum,and Litecoin),this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period.We also estimate the optimal weights,hedge ratios,and hedging effectiveness during both sample periods.We find that the return spillovers vary across the two periods for the Bitcoin–Ethereum,Bitcoin–Litecoin,and Ethereum–Litecoin pairs.However,the volatility transmissions are found to be different during the two sample periods for the Bitcoin–Ethereum and Bitcoin–Litecoin pairs.The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period.Based on optimal weights,investors are advised to decrease their investments(a)in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and(b)in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period.All hedge ratios are found to be higher during the COVID-19 period,implying a higher hedging cost compared to the pre-COVID-19 period.Last,the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period.Overall,these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification,hedging,forecasting,and risk management.

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.

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.

Examining volatility spillover between Asian stock markets

This study examined the volatility spillover effects between Asian stock markets,i.e.,Pakistan,India,Sri Lanka,China Mainland,Japan and China Hong Kong.The daily data was considered from the period 4^(th) January,1999 to 1^(st) January,2014,consisting 5 trading days from Monday to Friday.The volatility spillover between stock markets was captured by using GARCH(generalized auto regressive conditional heteroskedasticity)model.The empirical analyses show evidence of significant bidirectional spillover of return and volatility between China Mainland and Japan.The results also show significant bidirectional volatility transmission between the following equity markets;China Hong Kong and Sri Lanka,China Mainland and Sri Lanka.The significant unidirectional transmissions of stock market volatility are found to be flowing from;India to China Mainland,Sri Lanka to Japan,Pakistan to Sri Lanka,China Hong Kong to India and Japan.These results are important for economic policy makers in order to safeguard the financial sector from international financial shocks.The investors can use this information for making efficient portfolio which will reduce their risk and enhance their returns.

Coherence,Connectedness,Dynamic Linkages Among Oil and China's Sectoral Commodities with Portfolio Implications

This paper investigates the time-frequency dependence,return and volatility connectedness,dynamic linkages,and portfolio diversification gains among oil and China’s sectoral commodities,namely,Petrochemicals(CIFI),Grains(CRFI),Energy(ENFI),Non-ferrous metals(NFFI),Oil&Fats(OOFI),and Softs(SOFI),utilizing a proposed research framework that contains the wavelet coherence,novel TVP-VAR based connectedness,and the cDCC-,DECO-FIAPARCH(1,d,1)model.The empirical results demonstrate that global oil market exhibits a relatively higher(lower)coherence with ENFI,NFFI,and OOFI(CRFI)on the long-term time horizon and the oil market leads China’s sectoral commodities during most sample periods.The crude oil market transmits significant connectedness to China’s sectoral commodities,especially the energy commodity sector(ENFI).The dynamic return and volatility total spillovers tend to intensify and exhibit significant fluctuations during the GFC and the oil price collapse.Further,the time-varying linkages among oil and China’s sectoral commodities are positive and fluctuant,mainly at a relatively low level.The dynamic return and volatility connectedness,multi-view linkages,optimal portfolio weights,and hedging ratios display significant time-varying features.The oil-commodity nexus offers diversification benefits and the optimal-weighted portfolio presents the best variance and downside risk reduction performance.Furthermore,risk management effectiveness is market-condition-dependent and heterogeneous across different commodity sectors and sub-samples.This paper can not only help investors and market regulators to capture the complex interconnectedness and risk transmission trajectory among oil and China’s sectoral commodities but also benefits for investors and portfolio managers to construct optimal portfolios and hedging strategies.

Information Linkages between Chinese and World Copper Futures Markets

In this paper, we examine the price discovery process and volatility spillover effects in informationally linked futures markets. Using synchronous trading information from the Shanghai Futures Exchange （SHFE）, the New York Mercantile Exchange （NYMEX）, and the London Metal Exchange （LME） for copper futures from 2000 to 2012, we show that the cointegration relationships of these futures markets changed during 2006-2008. The results indicate that there is a bidirectional relationship in terms of price and volatility spillovers between the LME and NYMEX and the SHFE, with a stronger effect from the LME and NYMEX to the SHFE （versus the effect from the SHFE to the LME and NYMEX） prior to 2006. Our results also highlight the increasingly prominent role of the SHFE in the price formation process and cross-volatility spillover effects since 2008. Finally, we show that volatility spillover has important implications for constructing optimized portfolios for copper investors.

Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network

This paper focuses on volatility spillover effects and considers the issue of how to measure the connectedness of networks among financial firms.To assess the network connectedness of firms from different industries,we proposed a novel procedure and applied it to 20 leading financial institutions from four industries in China’s stock markets.The results show that the total connectedness of the Chinese financial system was much higher during the stock market crisis between June 2015 and February 2016 than during stable periods of economic development.This analysis can be used to determine which firms play a dominant role in risk transmission throughout the entire system.It is suggested that the government should provide targeted regulatory policies to particular types of firms.

The impact of reporting frequency on the information quality of share price： evidence from Chinese state-owned enterprises

As a major global exchange, the Stock Exchange of Hong Kong （SEHK） only requires semi-annual reporting whereas other major exchanges including the ones in Chinese mainland require quarterly reporting. We argue against the traditional view that higher reporting frequency is necessarily more beneficial. The decision on reporting frequency depends on how the information is being processed by the recipient traders and the results are not obvious. Using a sample of Chinese companies dual- listed in both China A share market and SEHK （AH shares） as the experimental group and mainland＇s companies listed on SEHK （H shares） only as the control group, we apply the difference-in-difference （DID） method to investigate the impacts of reporting frequency on stock information quality. The results suggest that after China A share market require quarterly financial reporting for all listed companies in 2002, the information asymmetry of the H tranche of AH stocks increases. Different from prior studies, the results suggest a negative association between stock information quality and financial reporting frequency. We argue that the increased information asymmetry in the H tranche is caused by the noise spilled over from the A tranche. We conduct multivariable GARCH tests and find evidence supporting this conjecture.