金融系统多维度流动性间溢出效应研究 ——基于三元VAR-GARCH-BEEK模型的分析

本文通过建立三元VAR(6)-GARCH(1,1)-BEEK模型从均值和方差层面刻画了我国金融系统不同维度流动性之间的溢出效应。研究发现,市场流动性、融资流动性与货币流动性之间存在双向均值溢出效应;融资流动性对市场流动性具有单项均值溢出效应,对货币流动性则具有单项波动溢出效应;货币流动性、融资流动性与市场流动性之间存在双向波动非对称溢出,且市场流动性的波动溢出效应较强。综上,本文认为监管者应加强不同维度流动性间的传导转换效率,同时密切监控金融市场资金状况,防止金融市场流动性出现大幅波动,并在执行货币政策时,兼顾各维度流动性变化对货币政策效力的影响。     

中国利率与股市间波动溢出效应的实证研究

采用多变量EGARCH模型分别对中国利率与沪深股市间的波动溢出效应进行的实证研究表明,股票收益率对利率收益率有着显著的短期动态影响;利率与沪深股市间存在着显著的双向波动溢出,除了利率对深圳市场的方向外,其他方向的波动溢出均存在着不对称性.     

利率市场化视角下金融市场间溢出效应检验

利率市场化进程通常伴随着金融市场的溢出效应。本文基于三变量VAR模型,实证分析了中国货币市场、债券市场、股票市场三个金融市场间的均值溢出效应,并以利率市场化进程中"取消贷款利率下限"这一事件作为分段节点,对比了利率市场化两阶段中溢出效应的差异。研究结果表明:在利率市场化初期债券市场收益率对股票市场收益率存在单向溢出效应,而随着利率市场化的深入,特别是在放开贷款利率下限之后的两年多的时间里,股票市场收益率对债券市场收益率与货币市场收益率对债券市场收益率均存在单向溢出效应。     

中美国债市场信息溢出效应检验

随着中国金融市场不断开放,中美国债市场之间的联动性进一步提高,信息溢出也进一步加强.本文构建动态信息溢出指数,分析了中美国债市场的总溢出、方向溢出及净溢出效应.研究发现:在样本期内,中美两国国债市场之间波动率总溢出效应强于收益率总溢出,两个市场存在不对称的双向溢出效应,其收益率和波动率溢出在动态路径上也存在显著差异,具有显著的时变性.2019年之前两国收益率总溢出水平基本维持在10以内,2019年后收益率溢出指数明显提高;波动率总溢出受金融事件影响导致极端值较多,溢出水平也相对较高.总体而言,无论是收益率溢出还是波动率溢出,多数时期美国国债市场对中国国债市场表现为正向净溢出,中国国债市场更多地受到美国国债市场的影响.     

美国货币政策对中国产出的溢出效应——基于SVAR模型的研究

本文通过构建SVAR模型对美国货币政策对中国产出溢出效应进行了实证检验。结果表明,以利率为代表指标的价格型美国货币政策扩张对中国产出在经过一段时期时滞后产生正向溢出效应,以货币供应量为代表指标的数量型美国货币政策扩张对中国产出产生正向溢出效应。根据子样本实证结果,量化宽松期间利率的溢出效应下降,货币供应量的溢出效应增加。     

政策不确定性与金融市场系统性风险——基于信息溢出的视角

本文从静态和动态两个方面测度了经济政策不确定性与金融市场收益率、波动率间的信息溢出效应,分析了股票市场、外汇市场、债券市场、黄金市场和货币市场五个金融子市场的信息溢出贡献度及其动态特征。研究发现,经济政策不确定性与金融市场间存在显著信息溢出效应,并呈现出时变性和双向性特征。样本期内,存在金融市场收益率对经济政策不确定的正向净溢出,金融市场波动率对经济政策不确定则表现为负向净溢出。从数值上看,政策不确定性与金融市场波动率间信息溢出强于与金融市场收益率间信息溢出。各个金融子市场在方向性溢出效应中贡献率结果显示,不同时期不同金融市场与经济政策不确定性间溢出效应存在差异。     

商业银行对P2P网贷利率波动的影响分析

运用单元和多元GARCH族模型和动态条件相关模型(DCC),分析了P2P网贷利率与传统金融市场的波动溢出效应。分析发现,一是网贷利率具有尖峰厚尾、波动集聚的特征,但不具有杠杆效应,且受自身前期波动影响显著;二是网贷利率受shibor和商业银行股票市场收益率的波动溢出影响,且进一步发现,shibor对网贷利率是负向波动溢出效应,商业银行股票收益率对网贷利率则是正向波动溢出效应。商业银行与网贷市场之间,不仅与shibor基准利率有所联系,商业银行的经营行为还会对网贷市场利率产生正向影响。     

我国燃料油期货与现货市场价格发现和波动溢出效应研究

借助VECM、带误差修正项的双变量TGARCH模型以及DCC模型,对我国燃料油期货和现货市场间的价格发现和波动溢出效应进行了研究。结果表明：我国燃料油期货和现货价格之间存在长期均衡和双向引导关系,期货在价格发现中起主导作用;期货市场不存在显著的杠杆效应,现货市场存在明显的负向非对称效应;两个市场间仅存在单向的波动溢出,表现为现货市场向期货市场正向溢出;误差修正项对两个市场的波动具有很好的解释能力,可以更加准确地刻画两个市场的波动性;DCC模型表明信息在两个市场间是流动的,两市的整合程度较高,但两市的相关系数还不是很高。     

我国创业板市场和主板市场之间风险溢出效应的实证分析

本文利用GARCH-Diagonal BEKK模型、Chi-plot图以及Granger因果检验等方法,对我国创业板市场与主板市场之间的风险溢出效应(均值溢出效应和波动溢出效应)进行了实证分析,以此来分析创业板市场与主板市场之间的波动性、相关性及波动影响程度.研究结果表明:我国证券市场明显存在从主板市场到创业板市场的均值溢出效应;创业板市场与主板市场之间也存在波动溢出效应,两个市场的波动相关性随着创业板的发展而逐步提升;创业板市场与主板市场之间的风险传导强度呈现倒“V”特征(即先增加后减少).为进一步降低创业板市场的波动风险、促进创业板市场健康发展,本文提出强化创业板市场导向、健全创业板规则体系、强化投资者风险意识等政策建议.     

国际商品市场与股票市场的溢出效应——基于中美数据的VAR-MGARCH分析

基于VAR-MGARCH-BEKK模型,对国际商品市场与中美股票市场之间的均值与波动溢出效应进行了经验分析。结果表明,国际商品市场与中美股票市场之间存在着相互的均值溢出效应,国际商品市场对中美股票市场存在波动溢出效应,同时,美国股票市场对国际商品市场存在波动溢出效应;另外,中国应该尽快编制科学合理并适合自身国情的商品指数。     

Monetary Policy and Inflation Expectations in Latin America: Long-Run Effects and Volatility Spillovers

This paper uses multiple cointegration analysis to estimate simultaneously a monetary reaction function and the determinants of expected inflation for Brazil, Chile, Colombia, and Mexico. In addition, M-GARCH modeling is used to test for the presence of volatility spillovers between the monetary stance and inflation expectations. The analysis shows that there are long-term relationships between the interest rate, expected inflation, and the inflation target, and that greater volatility in the monetary stance increases the volatility of expected inflation in Brazil, Colombia, and Mexico.     

Return and Volatility Spillovers Between Large and Small Stocks in the UK

Abstract:  This paper investigates return and volatility spillover effects between the FTSE 100, FTSE 250 and FTSE Small Cap equity indices using the multivariate GARCH framework. We find that return and volatility transmission mechanisms between large and small stocks in the UK are asymmetric. In particular, there are significant spillover effects in both returns and volatility from the portfolios of larger stocks to the portfolios of smaller stocks. For volatility, there is also evidence of limited feedback from the portfolios of smaller stocks to the portfolios of larger stocks, although sub-period analysis suggests that this is to some extent period-specific. Simulation evidence shows that non-synchronous trading potentially explains some, but not all, of the spillover effects in returns, and that it explains none of the spillover effects in volatility. These results are consistent with a market in which information is first incorporated into the prices of large stocks before being impounded into the prices of small stocks.     

Return and Volatility Spillovers and Cojump Behavior Between the U.S. and Korean Stock Markets

ABSTRACT

In this study, we examine return spillover, volatility transmission, and cojump behavior between the U.S. and Korean stock markets. In particular, we focus on cojump behavior between the two markets in order to explain the transmission of unexpected shocks. We find that the U.S. stock market causes return spillover effects in the Korean stock market, and there is significant volatility transmission between the two markets. Importantly, we find a stronger association in size, as compared with intensity, of cojumps between the U.S. and Korean stock markets, particularly during the recent financial crisis.     

Volatility Spillovers of Stock Markets between China and the Countries along the Belt and Road

ABSTRACT

This article intensively studies the stock market volatility spillover effects between China and the countries along the Belt and Road (B&R) based on the covered selection of Morgan Stanley Capital International Inc (MSCI) index by using multiplicative error model to measure stock market volatility with daily price range. The results show that during the whole sample period, there are bilateral linkages of volatility between the stock markets of China and all of B&R countries. Most of B&R and China’s markets are sensitive to positive news but the asymmetry is trivial. Financial crisis intensified the volatility spillover effects across countries while the markets’ volatilities tend to be influenced by the negative shocks from foreign markets. The B&R markets as risk absorbers exhibit significant sensitivities to the negative news from Chinese market during the crisis period.     

Trading with Asymmetric Volatility Spillovers

Abstract:  We study the profitability of trading strategies based on volatility spillovers between large and small firms. By using the Volatility Impulse-Response Function of Lin (1997) and its extensions, we detect that any volatility shock coming from small companies is important to large companies, but the reverse is only true for negative shocks coming from large firms. To exploit these asymmetric patterns in volatility, different trading rules are designed based on the inverse relationship existing between expected return and volatility. We find that most strategies generate excess after-transaction cost profits, especially after very bad news and very good news coming from large or small firm markets. These results are of special interest because of their implications for risk and portfolio management.     

Spillovers of stock return volatility to Asian equity markets from Japan and the US

This paper examines the magnitude of return and volatility spillovers from Japan and the US to seven Asian equity markets. I construct a volatility spillover model that deals with the US shock as an exogenous variable in a bivariate EGARCH for Japan and Asian markets. First, only the influence of the US is important for Asian market returns; there is no influence from Japan. Second, the volatility of the Asian market is influenced more by the Japanese market than by the US. Third, there exists an adverse influence of volatility from the Asian market to the Japanese market.     

Interdependence and Volatility Spillovers Under Market Liberalization: The Case of Istanbul Stock Exchange

This paper analyzes stock returns and volatility relations between the Istanbul Stock Exchange (ISE) and the global market as represented by stock markets in the US, the UK, Japan and Germany. Results from monthly data and multivariate cointegration tests suggest that the ISE became significantly integrated in the global market only in the period following market liberalization in late 1989. We also find evidence based on GARCH estimations that capital liberalization actually mitigated, rather than intensified, volatility in the ISE. Our results further suggest that the Asian crisis in mid‐1997 and the consequent Russian economic meltdown in mid‐1998 are partly responsible for the recent excessive volatility in the Turkish market. The results also identify the US and the UK markets as dominate sources of volatility spillovers for the ISE, even in the period following the Asian‐Russian crises. Consequently, it appears that the two matured markets of the US and the UK shoulder significant responsibility for the stability and financial health of smaller emerging markets like the ISE.     

Weather Effects on Stock Returns and Volatility in South Asian Markets

We study the effect of mood-proxy variables on index returns and volatility in six South Asian markets. Our mood-proxy variables include six weather (temperature, humidity, cloud cover, air pressure, visibility, and wind speed), three weather indicator variables (fog, thunder storm and rain or drizzle) and two biorhythmic variables (SAD and lunar phases). We adopt a robust approach and attempt to select the best parsimonious econometric model for each market. Our findings suggest that mood-proxy variables have some convincing influences in South Asian capital markets. In some instances, these variables are influencing returns while in other instances they are influencing volatility.     

Price and volatility spillovers between interest rate and exchange value of the US dollar

In this paper, the dynamic relationships between interest rate and exchange value of the US dollar are studied via a multivariate Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model. In terms of price changes, movements of interest rates have positive effects on movements of exchange rates. However, changes in exchange rates do not explain changes in interest rates. Nevertheless, there exists volatility spillovers between the two markets, indicating that their second moments are related. Overall evidence suggests that these two markets have short-term dynamic interactions. The existence of volatility spillovers also suggests that the relationships between these two economic variables are not necessarily linear.