我国保险市场与银行市场间的风险溢出效应研究——基于上市银行和保险公司的实证分析

本文运用我国上市保险公司和上市银行2008年1月至2016年11月的股票收益率数据构建了两市场的二阶段波动率模型。第一阶段,运用一元ARMA-GARCH模型对两个市场的波动性问题进行了测度。结果表明,两个市场的收益率序列都受到前期收益率的影响,存在风险暴露问题。第二阶段,运用二元VAR(2)-GARCH(1,1)-BEKK模型对两市场间的溢出效应进行了测度。实证结果显示,第一,均值溢出方程表明我国银行市场对保险市场存在微弱且短期的均值溢出效应,反之则没有;第二,波动溢出方程及WALD检验的结果证实我国保险市场和银行市场间存在双向波动溢出效应。本文认为,两市场的风险具有明显的关联性,任一市场的风险均可通过资本市场的“二阶效应”而传染给另一市场并形成风险扩散效应。     

基于GARCH模型对上证指数收益风险波动的实证分析

金融市场的不确定性导致价格波动的随机性,而研究波动的随机性不仅可以支持金融理论,也对金融实践有重大意义.本文以上证指数的日收益率为研究对象,采用EVIEWS通过建立ARCH类模型对上证指数日收益率进行实证分析,浅释股指收益率与风险之间的关系.结果表明,GARCH（1,1）模型能够较好的拟合上证股票收益率的波动特征,如“高峰厚尾”、集聚等现象,而GARCH（1,1）-M模型在一定程度上能较好的表现出风险与收益率之间的关系.同时也验证了沪市在中长期内不存在杠杆效应.     

基于上证综指的GARCH族模型实证分析

以1997年1月11日至2009年11月27日的上证综合指数的日度收盘数据为样本,运用GARCH族模型对其进行实证分析,检验了在这一段时间内我国股票市场的波动情况以及波动的杠杆效应,分析结果表明了上证指数对数收益率服从非正态分布,具有尖峰厚尾和明显的ARCH效应,并且股票的收益率具有明显的风险溢价和杠杆效应。     

我国债券市场和股票市场收益率波动溢出效应实证研究

本文通过建立ARCH族模型对债券市场和股票市场的收益率波动进行了处理,并用条件方差进行了Granger因果关系检验,用来衡量波动的溢出效应,最后我们发现以上证指数和企债指数衡量的收益率波动之间存在双向的因果关系,即存在着波动溢出效应.     

中国债券市场与股票市场间波动溢出效应——基于SJC-Copula模型的分析

本文基于SJC-Copula模型分析债券市场和股票市场间的波动溢出效应,并以此进一步分析波动溢出效应对债券市场风险规避能力的影响。研究选取2003年3月31日至2009年8月31日中信标普国债指数日数据和上证指数日数据,验证了两市波动溢出效应的存在性,同时发现波动溢出效应显著增强了债券市场规避风险的能力。     

沪深股市的引导性关系研究

采用向量自回归修正模型、脉冲响应函数和预测方差分解的方法,对上证指数、深证综合指数之间的互动性进行实证分析,结论是:沪深股市之间具有长期均衡关系;沪深股市溢出效应具有非对称性,深圳股市对外部冲击的反应要先于沪市,深指之走向和波动主要由自身趋势决定,而上证指数更倾向于受到深指的影响.     

我国汇市与股市之间的价格和波动溢出效应实证研究

为了考察我国汇市与股市之间的价格和波动溢出效应,本文利用"汇改后"人民币对美元的汇率与上证综指的日数据建立了多元向量自回归模型GARCH模型.研究发现,我国汇市与股市之间的价格溢出效应不明显,汇率波动率的ARCH效应不对股票市场产生显著的冲击,外汇市场波动的持久性会对股票市场产生显著的影响;股票收益率的ARCH效应不会对外汇市场产生明显的冲击,但股票收益率波动的持久性会显著影响汇率的变化率.     

上证综合指数波动研究——基于ARCH模型

证券市场的波动性与证券市场的不确定性及风险紧密相连;同时波动率也是套利定价模型、资本资产定价模型的核心变量。探索证券市场波动特征是市场投资者和理论研究者主要关心的问题之一。为了更好帮助正确认识我国证券市场的波动特征,有效规避市场风险,本文在借鉴国内外诸多研究成果的基础上,通过对我国上证综指的日收益率波动性的实证研究,来检验我国证券市场的波动特征并探索其产生的影响。通过陈述本文的研究背景、方法及目的,介绍本文研究思路和框架。再对2000年1月1日到2015年8月1日期间3768个上证指数日收盘价样本做ARCH效应分析和检验,得出上证指数收益率可以使用ARCH模型的结论。从而观察到上证指数日收益率序列具有聚类特征、尖峰厚尾和长记忆的特征。     

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.     

石油价格与股票市场的溢出效应——基于中美数据的比较分析

本文以中美股票市场和国际原油市场的数据为样本,用VAR模型和二元GARCH模型研究了中美股市价格和国际石油价格的收益率及波动的溢出效应。研究结果表明,中国股市价格和国际石油价格之间,既不存在任何方向的收益率溢出效应,也不存在任何方向的波动溢出效应;而国际石油价格的变化率对于美国股市收益率确有负向先导作用,并且两者之间具有双向的波动溢出。     

Does economic policy uncertainty drive nonlinear risk spillover in the commodity futures market?

We propose the rolling tail-event driven network technique (RTENET) to measure the dynamic nonlinear tail risk spillover of 20 US commodity futures. In addition, we investigate the effect of economic policy uncertainty (EPU) on risk spillover based on quantile-on-quantile regression (QQR). We find that the risk spillover effect increases sharply and that the market is tightly connected when EPU is at a high level. Crude oil, silver and corn, the three greatest risk transmitters in the system, need more attention. More importantly, the effect of EPU on the risk spillover of the commodity futures market is asymmetric and heterogeneous. When the risk spillover falls within extremely high quantiles, a significant positive effect of EPU is observed. In addition, grain and soft crops are more sensitive to EPU. Our findings provide a reference for policy-makers and investors to manage commodity futures markets in different uncertainty periods.     

Does sovereign debt ratings news spill over to international stock markets?

The evidence here indicates that sovereign debt rating and credit outlook changes of one country have an asymmetric and economically significant effect on the stock market returns of other countries over 1989–2003. There is a negative reaction of 51 basis points (two-day return spread vis-á-vis the US) to a credit ratings downgrade of one notch in a common information spillover around the world. Upgrades, however, have no significant impact on return spreads of countries abroad. Closeness (e.g., geographic proximity) and emerging market status amplify the effect of a spillover. Downgrade spillover effects at the industry level are more pronounced in traded goods and small industries.     

基于分位数关联的政策连续性跨国溢出研究

本文采用基于条件分位数的溢出指数方法,研究了不同冲击规模及方向下政策连续性的跨国溢出,考察相较于中间状态,极端上升与下降状态下的溢出变化及两者之间的非对称溢出效应,并构建相对溢入溢出指数,研究极端冲击对不同国家方向性溢出的异质性影响。研究发现:(1)政策连续性的总溢出以及各国溢入水平在不同条件分位数下呈U形结构,冲击规模对总溢出及各国溢入水平具有显著的正向影响。(2)在极端状态下,总溢出和大部分国家方向性溢出水平较中间状态显著提升,并且极端上升与下降状态具有非对称的溢出效应。其中,总溢出在极端下降状态涨幅更大,各国的溢出比溢入在两种极端状态下表现出更强烈的非对称性。(3)极端冲击的影响具有国别异质性,中国的方向性溢出水平大幅上升,对发达国家的左尾定向溢出加强,国际影响力显著增强。本文研究不仅对极端状态下保持经济系统稳定具有启示意义,也可为建设“双循环”新发展格局提供实证支持。     

基于动态Copula-CoVaR模型的影子银行风险溢出效应研究

依据2007-2017年中国金融市场运行数据,构建动态Copula-CoVaR模型,考量影子银行风险溢出效应。结果表明：影子银行与传统金融市场存在双向净风险溢出效应,随着时间的推移,溢出效应在逐渐增大;极端风险溢出效应存在不对称特征,影子银行对传统金融市场的冲击较大,且这种冲击具有滞后效应。鉴此,监管部门应夯实金融体系运行基础,创新影子银行监管工具,完善其协调监管模式。     

Asymmetric volatility spillovers between economic policy uncertainty and stock markets: Evidence from China

This study explores the spillovers between economic policy uncertainty (EPU) and stock market realized volatility (RV). The monthly index of Chinese and US EPU and RV are used to analyze the pairwise directional spillovers. We find that RV is a net receiver that is more vulnerable to shocks from U.S. EPU than to shocks from Chinese EPU. We further decompose the RV into good and bad volatility to test the asymmetric spillover effect between the stock market and EPU. The results suggest that EPU has a bigger effect on bad volatility in the stock market throughout most of the sample period. However, we find that good volatility spillovers become larger during periods of stimulated reform, whereas bad volatility spillovers become larger during periods of international disputes. We show that Chinese stock market volatility is sensitive to both U.S. and Chinese EPU and that the spillover is asymmetric in different periods.     

The extreme risk connectedness of the new financial system: European evidence

The paper empirically analyses the tail risk connectedness between FinTech and the banking sector in the European context over 2015–2022. For this purpose, we use the Tail-Event driven NETworks (TENET) risk model, i.e., we can capture the behaviour of extreme (negative and positive) risk spillover within the financial system. The results highlight how most tail risk spillovers are from banks to FinTech firms. Also, the findings suggest that the spillovers of cross-sector tail risk are more significant in downside (bearish) risk conditions than in upside (bullish) one. We find evidence of an asymmetric effect of extreme risk spillover to the real economy. Finally, we evaluate the monetary policy’s impact on extreme risk. Our findings highlight the importance of closer monitoring risk spillover between FinTech institutions and the European banking system to maintain financial stability.     

Volatility spillover in the foreign exchange market: the Indian experience

We find evidence of significant volatility co-movements and/or spillover from different financial markets to the forex market in India. Among a large number of variables examined, volatility spillovers from domestic stock, government securities, overnight index swap, Ted spread and international crude oil markets to the foreign exchange market are found to be significant. There is evidence of asymmetric reactions in the forex market volatility. Comparisons between pre-crisis and post-crisis volatility indicate that the reform measures and changes in financial markets microstructure during the crisis period had significant impact on volatility spillover. During the post-crisis period, the lagged volatility component that represents persistent or fundamental changes had significant spillover effect on forex volatility, rather than the temporary shocks component. There is evidence of a decline in the asymmetric response in the forex volatility during the post-crisis period in India.     

Returns and volatility spillover in the European banking industry during global financial crisis: Flight to perceived quality or contagion?

This paper empirically investigates return, volatility and leverage spillover effects between banking industrial stock markets of the major economies (ME) (Germany, UK and US) and the smaller stressed European Union countries (SE), (Italy, Ireland, Greece, Spain and Portugal) from 2002 to 2014 which includes the global financial crisis period (2007–2014). Thus the paper investigates the influence of the global crisis on the spillover between the banking industrial stock markets of Europe and the US. We apply a multivariate GARCH–GJR framework to investigate the effects of the financial crisis with respect to spillover. Our results indicate an increase in both means and volatility spillover between the major economies and the stressed EU economies from the pre-crisis to the crisis period. During the pre-crisis period there is ample evidence of spillover from Germany, UK and the US to the smaller EU economies. Little evidence of a significant spillover from the smaller economies to the major economies is found during this period. We find that return and volatility transmission mechanisms between the major economies and the smaller EU countries are asymmetric during the crisis period. During the crisis, the level and amount of spillover from the major economies increase. But now there is also clear evidence of spillover from smaller EU economies to the major economies, this is especially true for Germany and the UK. Evidence of spillover effects suggests the existence of exploitable trading strategies and has important implications to investors in the areas of option pricing, portfolio optimization and risk management.     

Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method

This paper analyzes dynamic volatility spillovers between four major energy commodities (i.e., crude oil, gasoline, heating oil and natural gas) in the oil-natural gas future markets. We construct a time-varying spillover method by combining the TVP-VAR-SV model and the spillover method of Diebold and Yilmaz (2009, 2012, 2014). We use the spillover method to obtain time-varying total, directional and pairwise volatility spillover indices. Our results summarize as follows: (1) The volatility spillover indices present peaks and troughs during some periods, such as shale gas revolution, financial crisis, and oil price crash; (2) After the U.S. shale gas revolution, the size of volatility spillover from natural gas future market has reduced sharply, but volatility doesn't decouple from the other three oil future markets; (3) The directional spillover is asymmetric. The crude oil and heating oil futures market are main net transmitter of volatility risk information, while the gasoline and natural gas futures markets are the net receiver; (4) For natural gas future market, the pairwise volatility spillover from crude oil future market has the most significant influence.     

Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS

This paper investigates the dynamic dependence and risk spillover between BRICS stock returns and different types of oil shocks, combining the Structural VAR model and time-varying copula-GARCH-based CoVaR approach. Our findings indicate that the dependence between BRICS stock returns and oil shocks is time-varying and exhibits different behaviours depending on the shock types in the oil market. In general, the shape of the CoVaRs in each country is comparatively different, depending on its special market situation and domestic policies. There is significant risk spillover from oil-specific demand shock to stock returns in all the BRICS countries. Finally, in Brazil, Russia and India, there is a significant asymmetric effect between upside and downside risk spillover based on oil aggregate demand shock and oil-specific demand shock.