This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dyn...This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dynamic Conditional Correlation–Conditional Value-at-Risk(ADCC-CoVaR)model and a bootstrapped Kolmogorov–Smirnov(KS)test,we analyze the period from December 30,2005,to February 28,2024,examining correlations,downside and upside risk spillovers,and highlighting the effects of major events such as the global financial crisis of 2008,the COVID-19 pandemic,and the Russia-Ukraine war.The results show heightened correlations during crises and significant risk spillovers across market pairs,with downside risks often outweighing upside risks.Gold displays minimal risk spillover,highlighting its unique role as a haven asset.We find that spillovers between gold,global commodities,and stocks increased during the pandemic and the Russia-Ukraine conflict,while those involving crude oil remained stable.These findings provide valuable guidance for portfolio managers in navigating volatile markets.展开更多
Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional ...Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional data degrade performance,contrary to common expectations.While more data can still be beneficial,it may introduce systemic concept drift due to the complex nonstationarities of stock price index time series,thereby exacerbating overfitting.One such drift is memory inconsistency:locally measured long memories fluctuate over time,alternately approaching and deviating from the random walk condition.We address this problem by typifying memory inconsistencies into two simplified forms:long-term dependentto-independent(D2I)and long-term independent-to-dependent(I2D)inconsistencies.The first experiment,which uses U.S.stock price indices,suggests that additional training examples may lead to performance deterioration of long short-term memory(LSTM)networks,especially when memory inconsistencies are prominent.Since stock markets are influenced by numerous unknown dynamics,the second experiment,which uses simulated mean-reverting time series derived from the fractional Ornstein–Uhlenbeck(fOU)process,is conducted to focus solely on challenges arising from memory inconsistencies.The experimental results demonstrate that memory inconsistencies disrupt the performance of LSTM networks.Theoretically,additional errors from D2I and I2D inconsistencies increase as the time lag increases.Since LSTM networks are inherently recurrent,causing information from distant steps to attenuate,they fail to effectively capture memory inconsistencies in practical offline learning schemes.Nonetheless,transplanting pretrained memory-consistent gate parameters into the LSTM model partially mitigates the performance deterioration caused by memory inconsistencies,suggesting that memory augmentation strategies have the potential to overcome this problem.As such a memory augmentation method,we propose the Gate-of-Gates(GoG)model,which extends the capacity of LSTM gates and demonstrates that it can mitigate additional errors arising from memory inconsistencies.展开更多
This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,t...This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,the US,and the UK)by implementing the time-varying VAR(TVP-VAR)model for daily data over the period spanning from 01/01/2015 to 05/18/2020.Results showed that stock markets were highly connected during the entire period,but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020.Moreover,we found that the European stock markets(except Italy)transmitted more spillovers to all other stock markets than they received,primarily during the COVID-19 outbreak.Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns.Also,findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset,indicating that information spillovers from a given market may signal either good or bad news for other markets,depending on the prevailing economic situation.These results have important implications for individual investors,portfolio managers,policymakers,investment banks,and central banks.展开更多
Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary traje...Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.展开更多
This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The margin...This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing precrisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre- and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching time- varying copula model performs better than the time-varying copula model.展开更多
The purpose of this study is to investigate the financial integration of the stock markets of the ASEAN 5 ＋ 3 countries. These countries include Indonesia, Malaysia, Philippines, Singapore, Thailand, China, Japan, an...The purpose of this study is to investigate the financial integration of the stock markets of the ASEAN 5 ＋ 3 countries. These countries include Indonesia, Malaysia, Philippines, Singapore, Thailand, China, Japan, and South Korea. The research determined the stock return volatility for each country＇s index during the first decade of the new millennium. The findings showed that there is the presence of integration and co-integration with Philippine index＇s return with the index＇s returns of the following countries： Indonesia, Singapore, and Thailand. Furthermore, there is evidence of volatility clustering in these stock markets. The study concluded with the policy implications of greater integration in light of the planned cross trading among four ASEAN bourses, namely, Philippines, Singapore, Thailand, and Malaysia by 2012.展开更多
The accuracy and time scale invariance of value-at-risk （VaR） measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory （EVT） is applied to model the extre...The accuracy and time scale invariance of value-at-risk （VaR） measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory （EVT） is applied to model the extreme tail of standardized residual series of daily/weekly indices losses, and parametric and nonparametric methods are used to estimate parameters of the general Pareto distribution （GPD）, and dynamic VaR for indices of three stock markets in China. The accuracy and time scale invariance of risk measurement methods through back-testing approach are also examined. Results show that not all the indices accept time scale invariance; there are some differences in accuracy between different indices at various confidence levels. The most powerful dynamic VaR estimation methods are EVT-GJR-Hill at 97.5% level for weekly loss to Shanghai stock market, and EVT-GARCH-MLE （Hill） at 99.0% level for weekly loss to Taiwan and Hong Kong stock markets, respectively.展开更多
The emphasis of this study is on the practice of the Pooled Mean Group （PMG） estimators to investigate the magnitude of macroeconomic performances： Real Gross Domestic Product （RGDP）, Foreign Exchange Rate （EX）...The emphasis of this study is on the practice of the Pooled Mean Group （PMG） estimators to investigate the magnitude of macroeconomic performances： Real Gross Domestic Product （RGDP）, Foreign Exchange Rate （EX）, and Deposit Interest Rate （DINT） affecting on the rate of financial sector returns in Southeast Asian Stock Markets including Stock Exchange Of Thailand （SET） index （Thailand）, the Kuala Lumpur Composite Index （KLSE） index （Malaysia）, Financial Times Share Index （FTSI） （Singapore）, Philippine Stock Exchange （PSE）, and the Jakarta Composite Index （JKSE） （Indonesia）. The Panel Autoregressive Distributed Lag （Panel ARDL） is applied to model the relations. The study applies the Levin, Lin, and Chu （LLC） test （2002） and Im, Pesaran, and Shin （IPS） test （2003） to investigates a set of time series data to examine whether the determinants and the rate of financial sector returns contain a unit root, the next step is investigated the cointegration and causality relationship of the determinants of financial sector influencing on long-run rate of returns of financial sector in Southeast Asian Stock Markets.展开更多
The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets,particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic.This s...The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets,particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic.This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets,focusing on gold and six major stock markets(Japan,USA,UK,China,Germany,and France)using daily data from January 2,2011,to June 2,2022,with 2958 daily observations.We employ DCC-GARCH,wavelet coherence,and cascade-correlation network models to analyze the relationship between Bitcoin and those markets.Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty.We also find evidence of long-term contagion between Bitcoin and the six stock markets,with short-term contagion observed in Chinese and Japanese markets during COVID-19.These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets.Hedge funds,mutual funds,and individual and institutional investors can benefit from using our findings in their risk management strategies.Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions.It holds significant implications for stock market investors and policymakers,providing evidence for potentially using cryptocurrencies for hedging,for diversification,or as a safe haven.展开更多
The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,inve...The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,investors should consider investing in more secure assets,such as real estate property,cash,gold,and crypto assets.In recent years,among secure assets,cryptoassets are gaining more attention than traditional investments.This study compares the Bitcoin market,the gold market,and American stock indexes(S&P500,Nasdaq,and Dow Jones)before and during the COVID-19 pandemic.For this purpose,the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets.Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin,gold,and stock markets.In particular,we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period.This paper provides practical impacts on risk management and portfolio diversification.展开更多
We propose a new predictor-the innovation in the daily return minimum in the U.S.stock market(△MIN^(US))-for predicting international stock market returns.Using monthly data for a wide range of 17 MSCI international ...We propose a new predictor-the innovation in the daily return minimum in the U.S.stock market(△MIN^(US))-for predicting international stock market returns.Using monthly data for a wide range of 17 MSCI international stock markets dur-ing the period spanning over half a century from January 1972 to July 2022,we find that △MIN^(US) have strong predictive power for returns in most international stock markets:△MIN^(US) negatively predicts the next-month stock market returns.The results remain robust after controlling for a number of macroeconomic predictors and con-ducting subsample and panel data analyses,indicating that △MIN^(US) has significant predictive power and it outperforms other variables in international markets.Notably,△MIN^(US) demonstrates excellent predictive power even during the periods driven by financial upheavals(e.g.,Global Financial Crisis and European Sovereign Debt Crisis).Both panel regressions and out-of-sample tests also support the robust predictive performance of △MIN^(US).The predictive power,however,disappears during the non-financial crisis caused by COVID-19 pandemic,which is originated from the health sector rather than the financial sector.The results provide a new perspective on U.S.extreme indicator in stock market return predictability.展开更多
Human activities widely exhibit a power-law distribution.Considering stock trading as a typical human activity in the financial domain,the first aim of this paper is to validate whether the well-known power-law distri...Human activities widely exhibit a power-law distribution.Considering stock trading as a typical human activity in the financial domain,the first aim of this paper is to validate whether the well-known power-law distribution can be observed in this activity.Interestingly,this paper determines that the number of accumulated lead–lag days between stock pairs meets the power-law distribution in both the U.S.and Chinese stock markets based on 10 years of trading data.Based on this finding this paper adopts the power-law distribution to formally define the lead–lag effect,detect stock pairs with the lead–lag effect,and then design a pure lead–lag investment strategy as well as enhancement investment strategies by integrating the lead–lag strategy into classic alpha-factor strategies.Tests conducted on 20 different alpha-factor strategies demonstrate that both perform better than the selected benchmark strategy and that the lead–lag strategy provides useful signals that significantly improve the performance of basic alpha-factor strategies.Our results therefore indicate that the lead–lag effect may provide effective information for designing more profitable investment strategies.展开更多
This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using variousmethods, including panel regression with fixed effec...This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using variousmethods, including panel regression with fixed effects, panel quantile regressions, apanel vector autoregression (PVAR) model, and country-specific regressions. We proxyfor negative and positive investor sentiments using the Google Search Volume Indexfor terms related to the coronavirus disease (COVID-19) and COVID-19 vaccine, respectively. Using weekly data from March 2020 to May 2021, we document significantrelationships between positive and negative investor sentiments and stock marketreturns and volatility. Specifically, an increase in positive investor sentiment leads toan increase in stock returns while negative investor sentiment decreases stock returnsat lower quantiles. The effect of investor sentiment on volatility is consistent acrossthe distribution: negative sentiment increases volatility, whereas positive sentimentreduces volatility. These results are robust as they are corroborated by Granger causalitytests and a PVAR model. The findings may have portfolio implications as they indicatethat proxies for positive and negative investor sentiments seem to be good predictorsof stock returns and volatility during the pandemic.展开更多
The efficiency of a stock market is principally measured by its information efficiency and functionality efficiency. Both metrics are closdy related to the information of stock markets. However, there is no uniform de...The efficiency of a stock market is principally measured by its information efficiency and functionality efficiency. Both metrics are closdy related to the information of stock markets. However, there is no uniform definition of information in the economy field since researchers may have various opinions on the information of stock markets. In this research, a comparatively strict definition of information in sense of economy is presented. Based on this definition, the optimal conditions to reach the maximum information efficiency and functionality efficiency of stock markets are derived. The conclusion is, only when the market＇s operation and information transmission mechanisms are fully effective, its information completeness degree is optimal, all investors take optimal equilibrium actions, and the information efficiency and functionality efficiency of stock markets will be optimal. Based on the conclusions, the information efficiency and functionality efficiency of reality stock markets in China are studied and the corresponding supervision countermeasures are suggested.展开更多
This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇...This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇s t distribution to analyze the proposed model. The empirical results show that the two stock markets are mutually affected each other, and the dynamic conditional correlation （DCC） and the bivariate asymmetric-GARCH （1, 2） model is appropriate in evaluating the relation between them. The empirical result also indicates that Italy and Germany＇s stock markets show a positive relationship. The average value of correlation coefficient equals to 0.8424, which implies that the two stock markets return volatility have a synchronized influence on each other. In addition, the empirical result also shows that there is an asymmetrical effect between Italy and the Germany＇s stock markets, and demonstrates that the good news and bad news of the stock returns＇ volatility will produce the different variation risks for Italy and the Germany＇s stock price markets.展开更多
This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid t...This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.展开更多
This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary ...This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.展开更多
This paper examines the dependence,systemic risk spillover,return and volatility spillover,and portfolio implications across various timescales between the Green Bond(GB)and U.S.S&P 500 Stock(SP),Vanguard Total Wo...This paper examines the dependence,systemic risk spillover,return and volatility spillover,and portfolio implications across various timescales between the Green Bond(GB)and U.S.S&P 500 Stock(SP),Vanguard Total World Stock Index Fund(VT),Bitcoin(BTC),Ethereum(ETH),Ripple,OIL,and GOLD markets.The sample period is August 07,2015–October 6,2023,covering periods of instability during the COVID-19 pandemic and the Russia–Ukraine conflict.Using the wavelet–copula–conditional value-atrisk and wavelet-multivariate asymmetric-GARCH framework,our main results show that the systemic risk and return,volatility spillovers,and diversification opportunities are portfolio-specific and timescale-dependent.Specifically,there is a negative long-term correlation for the pairs GB-SP and GB-OIL,whereas the pair GB–GOLD pair is positively correlated in the short term.GB can mitigate the risk of other markets.In terms of the portfolio implications,GB weakly hedges BTC and ETH during normal and turbulent periods but has a strong ability to hedge VT in the short term and SP in the mid and long term.Regarding hedging effectiveness,the role of GB for GOLD and VT is noted.展开更多
In the era of big data,stock markets are closely connected with Internet big data from diverse sources.This paper makes the first attempt to compare the linkage between stock markets and various Internet big data coll...In the era of big data,stock markets are closely connected with Internet big data from diverse sources.This paper makes the first attempt to compare the linkage between stock markets and various Internet big data collected from search engines,public media and social media.To achieve this purpose,a big data-based causality testing framework is proposed with three steps,i.e.,data crawling,data mining and causality testing.Taking the Shanghai Stock Exchange and Shenzhen Stock Exchange as targets for stock markets,web search data,news,and microblogs as samples of Internet big data,some interesting findings can be obtained.1)There is a strong bi-directional,linear and nonlinear Granger causality between stock markets and investors'web search behaviors due to some similar trends and uncertain factors.2)News sentiments from public media have Granger causality with stock markets in a bi-directional linear way,while microblog sentiments from social media have Granger causality with stock markets in a unidirectional linear way,running from stock markets to microblog sentiments.3)News sentiments can explain the changes in stock markets better than microblog sentiments due to their authority.The results of this paper might provide some valuable information for both stock market investors and modelers.展开更多
文摘This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dynamic Conditional Correlation–Conditional Value-at-Risk(ADCC-CoVaR)model and a bootstrapped Kolmogorov–Smirnov(KS)test,we analyze the period from December 30,2005,to February 28,2024,examining correlations,downside and upside risk spillovers,and highlighting the effects of major events such as the global financial crisis of 2008,the COVID-19 pandemic,and the Russia-Ukraine war.The results show heightened correlations during crises and significant risk spillovers across market pairs,with downside risks often outweighing upside risks.Gold displays minimal risk spillover,highlighting its unique role as a haven asset.We find that spillovers between gold,global commodities,and stocks increased during the pandemic and the Russia-Ukraine conflict,while those involving crude oil remained stable.These findings provide valuable guidance for portfolio managers in navigating volatile markets.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2023S1A5A8077102).
文摘Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional data degrade performance,contrary to common expectations.While more data can still be beneficial,it may introduce systemic concept drift due to the complex nonstationarities of stock price index time series,thereby exacerbating overfitting.One such drift is memory inconsistency:locally measured long memories fluctuate over time,alternately approaching and deviating from the random walk condition.We address this problem by typifying memory inconsistencies into two simplified forms:long-term dependentto-independent(D2I)and long-term independent-to-dependent(I2D)inconsistencies.The first experiment,which uses U.S.stock price indices,suggests that additional training examples may lead to performance deterioration of long short-term memory(LSTM)networks,especially when memory inconsistencies are prominent.Since stock markets are influenced by numerous unknown dynamics,the second experiment,which uses simulated mean-reverting time series derived from the fractional Ornstein–Uhlenbeck(fOU)process,is conducted to focus solely on challenges arising from memory inconsistencies.The experimental results demonstrate that memory inconsistencies disrupt the performance of LSTM networks.Theoretically,additional errors from D2I and I2D inconsistencies increase as the time lag increases.Since LSTM networks are inherently recurrent,causing information from distant steps to attenuate,they fail to effectively capture memory inconsistencies in practical offline learning schemes.Nonetheless,transplanting pretrained memory-consistent gate parameters into the LSTM model partially mitigates the performance deterioration caused by memory inconsistencies,suggesting that memory augmentation strategies have the potential to overcome this problem.As such a memory augmentation method,we propose the Gate-of-Gates(GoG)model,which extends the capacity of LSTM gates and demonstrates that it can mitigate additional errors arising from memory inconsistencies.
文摘This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,the US,and the UK)by implementing the time-varying VAR(TVP-VAR)model for daily data over the period spanning from 01/01/2015 to 05/18/2020.Results showed that stock markets were highly connected during the entire period,but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020.Moreover,we found that the European stock markets(except Italy)transmitted more spillovers to all other stock markets than they received,primarily during the COVID-19 outbreak.Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns.Also,findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset,indicating that information spillovers from a given market may signal either good or bad news for other markets,depending on the prevailing economic situation.These results have important implications for individual investors,portfolio managers,policymakers,investment banks,and central banks.
基金the National Nature Science Foundation of China(Grant Nos.11875042 and 11505114)the Orientational Scholar Program Sponsored by the Shanghai Education Commission,China(Grant Nos.D-USST02 and QD2015016)the Shanghai Project for Construction of Top Disciplines,China(Grant No.USST-SYS-01).
文摘Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.
文摘This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing precrisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre- and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching time- varying copula model performs better than the time-varying copula model.
文摘The purpose of this study is to investigate the financial integration of the stock markets of the ASEAN 5 ＋ 3 countries. These countries include Indonesia, Malaysia, Philippines, Singapore, Thailand, China, Japan, and South Korea. The research determined the stock return volatility for each country＇s index during the first decade of the new millennium. The findings showed that there is the presence of integration and co-integration with Philippine index＇s return with the index＇s returns of the following countries： Indonesia, Singapore, and Thailand. Furthermore, there is evidence of volatility clustering in these stock markets. The study concluded with the policy implications of greater integration in light of the planned cross trading among four ASEAN bourses, namely, Philippines, Singapore, Thailand, and Malaysia by 2012.
基金The National Natural Science Foundation of China (No70501025 & 70572089)
文摘The accuracy and time scale invariance of value-at-risk （VaR） measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory （EVT） is applied to model the extreme tail of standardized residual series of daily/weekly indices losses, and parametric and nonparametric methods are used to estimate parameters of the general Pareto distribution （GPD）, and dynamic VaR for indices of three stock markets in China. The accuracy and time scale invariance of risk measurement methods through back-testing approach are also examined. Results show that not all the indices accept time scale invariance; there are some differences in accuracy between different indices at various confidence levels. The most powerful dynamic VaR estimation methods are EVT-GJR-Hill at 97.5% level for weekly loss to Shanghai stock market, and EVT-GARCH-MLE （Hill） at 99.0% level for weekly loss to Taiwan and Hong Kong stock markets, respectively.
文摘The emphasis of this study is on the practice of the Pooled Mean Group （PMG） estimators to investigate the magnitude of macroeconomic performances： Real Gross Domestic Product （RGDP）, Foreign Exchange Rate （EX）, and Deposit Interest Rate （DINT） affecting on the rate of financial sector returns in Southeast Asian Stock Markets including Stock Exchange Of Thailand （SET） index （Thailand）, the Kuala Lumpur Composite Index （KLSE） index （Malaysia）, Financial Times Share Index （FTSI） （Singapore）, Philippine Stock Exchange （PSE）, and the Jakarta Composite Index （JKSE） （Indonesia）. The Panel Autoregressive Distributed Lag （Panel ARDL） is applied to model the relations. The study applies the Levin, Lin, and Chu （LLC） test （2002） and Im, Pesaran, and Shin （IPS） test （2003） to investigates a set of time series data to examine whether the determinants and the rate of financial sector returns contain a unit root, the next step is investigated the cointegration and causality relationship of the determinants of financial sector influencing on long-run rate of returns of financial sector in Southeast Asian Stock Markets.
文摘The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets,particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic.This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets,focusing on gold and six major stock markets(Japan,USA,UK,China,Germany,and France)using daily data from January 2,2011,to June 2,2022,with 2958 daily observations.We employ DCC-GARCH,wavelet coherence,and cascade-correlation network models to analyze the relationship between Bitcoin and those markets.Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty.We also find evidence of long-term contagion between Bitcoin and the six stock markets,with short-term contagion observed in Chinese and Japanese markets during COVID-19.These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets.Hedge funds,mutual funds,and individual and institutional investors can benefit from using our findings in their risk management strategies.Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions.It holds significant implications for stock market investors and policymakers,providing evidence for potentially using cryptocurrencies for hedging,for diversification,or as a safe haven.
基金supported by the Department of Economics and Management,University of Luxembourgfinancial support from the Department of Economics and Management,University of Luxembourg.
文摘The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,investors should consider investing in more secure assets,such as real estate property,cash,gold,and crypto assets.In recent years,among secure assets,cryptoassets are gaining more attention than traditional investments.This study compares the Bitcoin market,the gold market,and American stock indexes(S&P500,Nasdaq,and Dow Jones)before and during the COVID-19 pandemic.For this purpose,the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets.Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin,gold,and stock markets.In particular,we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period.This paper provides practical impacts on risk management and portfolio diversification.
文摘We propose a new predictor-the innovation in the daily return minimum in the U.S.stock market(△MIN^(US))-for predicting international stock market returns.Using monthly data for a wide range of 17 MSCI international stock markets dur-ing the period spanning over half a century from January 1972 to July 2022,we find that △MIN^(US) have strong predictive power for returns in most international stock markets:△MIN^(US) negatively predicts the next-month stock market returns.The results remain robust after controlling for a number of macroeconomic predictors and con-ducting subsample and panel data analyses,indicating that △MIN^(US) has significant predictive power and it outperforms other variables in international markets.Notably,△MIN^(US) demonstrates excellent predictive power even during the periods driven by financial upheavals(e.g.,Global Financial Crisis and European Sovereign Debt Crisis).Both panel regressions and out-of-sample tests also support the robust predictive performance of △MIN^(US).The predictive power,however,disappears during the non-financial crisis caused by COVID-19 pandemic,which is originated from the health sector rather than the financial sector.The results provide a new perspective on U.S.extreme indicator in stock market return predictability.
基金supported by the National Natural Science Foundation of China(72171059,71771041)the Fundamental Research Funds for the Central Universities(FRFCU5710000220)the Natural Science Foundation of Heilongjiang Province,China(No.YQ2020G003).
文摘Human activities widely exhibit a power-law distribution.Considering stock trading as a typical human activity in the financial domain,the first aim of this paper is to validate whether the well-known power-law distribution can be observed in this activity.Interestingly,this paper determines that the number of accumulated lead–lag days between stock pairs meets the power-law distribution in both the U.S.and Chinese stock markets based on 10 years of trading data.Based on this finding this paper adopts the power-law distribution to formally define the lead–lag effect,detect stock pairs with the lead–lag effect,and then design a pure lead–lag investment strategy as well as enhancement investment strategies by integrating the lead–lag strategy into classic alpha-factor strategies.Tests conducted on 20 different alpha-factor strategies demonstrate that both perform better than the selected benchmark strategy and that the lead–lag strategy provides useful signals that significantly improve the performance of basic alpha-factor strategies.Our results therefore indicate that the lead–lag effect may provide effective information for designing more profitable investment strategies.
文摘This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using variousmethods, including panel regression with fixed effects, panel quantile regressions, apanel vector autoregression (PVAR) model, and country-specific regressions. We proxyfor negative and positive investor sentiments using the Google Search Volume Indexfor terms related to the coronavirus disease (COVID-19) and COVID-19 vaccine, respectively. Using weekly data from March 2020 to May 2021, we document significantrelationships between positive and negative investor sentiments and stock marketreturns and volatility. Specifically, an increase in positive investor sentiment leads toan increase in stock returns while negative investor sentiment decreases stock returnsat lower quantiles. The effect of investor sentiment on volatility is consistent acrossthe distribution: negative sentiment increases volatility, whereas positive sentimentreduces volatility. These results are robust as they are corroborated by Granger causalitytests and a PVAR model. The findings may have portfolio implications as they indicatethat proxies for positive and negative investor sentiments seem to be good predictorsof stock returns and volatility during the pandemic.
基金Humanities and Social Sciences Foundation of Ministry of Education of China(No. 07JA790096)
文摘The efficiency of a stock market is principally measured by its information efficiency and functionality efficiency. Both metrics are closdy related to the information of stock markets. However, there is no uniform definition of information in the economy field since researchers may have various opinions on the information of stock markets. In this research, a comparatively strict definition of information in sense of economy is presented. Based on this definition, the optimal conditions to reach the maximum information efficiency and functionality efficiency of stock markets are derived. The conclusion is, only when the market＇s operation and information transmission mechanisms are fully effective, its information completeness degree is optimal, all investors take optimal equilibrium actions, and the information efficiency and functionality efficiency of stock markets will be optimal. Based on the conclusions, the information efficiency and functionality efficiency of reality stock markets in China are studied and the corresponding supervision countermeasures are suggested.
文摘This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇s t distribution to analyze the proposed model. The empirical results show that the two stock markets are mutually affected each other, and the dynamic conditional correlation （DCC） and the bivariate asymmetric-GARCH （1, 2） model is appropriate in evaluating the relation between them. The empirical result also indicates that Italy and Germany＇s stock markets show a positive relationship. The average value of correlation coefficient equals to 0.8424, which implies that the two stock markets return volatility have a synchronized influence on each other. In addition, the empirical result also shows that there is an asymmetrical effect between Italy and the Germany＇s stock markets, and demonstrates that the good news and bad news of the stock returns＇ volatility will produce the different variation risks for Italy and the Germany＇s stock price markets.
文摘This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.
文摘This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.
文摘This paper examines the dependence,systemic risk spillover,return and volatility spillover,and portfolio implications across various timescales between the Green Bond(GB)and U.S.S&P 500 Stock(SP),Vanguard Total World Stock Index Fund(VT),Bitcoin(BTC),Ethereum(ETH),Ripple,OIL,and GOLD markets.The sample period is August 07,2015–October 6,2023,covering periods of instability during the COVID-19 pandemic and the Russia–Ukraine conflict.Using the wavelet–copula–conditional value-atrisk and wavelet-multivariate asymmetric-GARCH framework,our main results show that the systemic risk and return,volatility spillovers,and diversification opportunities are portfolio-specific and timescale-dependent.Specifically,there is a negative long-term correlation for the pairs GB-SP and GB-OIL,whereas the pair GB–GOLD pair is positively correlated in the short term.GB can mitigate the risk of other markets.In terms of the portfolio implications,GB weakly hedges BTC and ETH during normal and turbulent periods but has a strong ability to hedge VT in the short term and SP in the mid and long term.Regarding hedging effectiveness,the role of GB for GOLD and VT is noted.
基金sponsored by the National Natural Science Foundation of China under Grant Nos.715732447153201371202115 and 71403260。
文摘In the era of big data,stock markets are closely connected with Internet big data from diverse sources.This paper makes the first attempt to compare the linkage between stock markets and various Internet big data collected from search engines,public media and social media.To achieve this purpose,a big data-based causality testing framework is proposed with three steps,i.e.,data crawling,data mining and causality testing.Taking the Shanghai Stock Exchange and Shenzhen Stock Exchange as targets for stock markets,web search data,news,and microblogs as samples of Internet big data,some interesting findings can be obtained.1)There is a strong bi-directional,linear and nonlinear Granger causality between stock markets and investors'web search behaviors due to some similar trends and uncertain factors.2)News sentiments from public media have Granger causality with stock markets in a bi-directional linear way,while microblog sentiments from social media have Granger causality with stock markets in a unidirectional linear way,running from stock markets to microblog sentiments.3)News sentiments can explain the changes in stock markets better than microblog sentiments due to their authority.The results of this paper might provide some valuable information for both stock market investors and modelers.
