Modeling dynamic conditional correlations with leverage effects and volatility spillover effects: Evidence from the Chinese and US stock markets affected by the recent trade friction

In this article, we investigate the dynamic conditional correlations (DCCs) with leverage effects and volatility spillover effects that consider time difference and long memory of returns, between the Chinese and US stock markets, in the Sino-US trade friction and previous stable periods. The widespread belief that the developed markets dominate the emerging markets in stock market interactions is challenged by our findings that both the mean and volatility spillovers are bidirectional. We do find that most of the shocks to these DCCs between the two stock markets are symmetric, and all the symmetric shocks to these DCCs are highly persistent between Shanghai’s trading return and S&P 500′s trading or overnight return, however all the shocks to these DCCs are short-lived between S&P 500′s trading return and Shanghai’s trading or overnight return. We also find clear evidence that the DCC between Shanghai’s trading return and S&P 500′s overnight return has a downward trend with a structural break, perhaps due to the “America First” policy, after which it rebounds and fluctuates sharply in the middle and later periods of trade friction. These findings have important implications for investors to pursue profits.     

Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach

This paper proposes a new volatility-spillover-asymmetric conditional autoregressive range (VS-ACARR) approach that takes into account the intraday information, the volatility spillover from crude oil as well as the volatility asymmetry (leverage effect) to model/forecast Bitcoin volatility (price range). An empirical application to Bitcoin and crude oil (WTI) price ranges shows the existence of strong volatility spillover from crude oil to the Bitcoin market and a weak leverage effect in the Bitcoin market. The VS-ACARR model yields higher forecasting accuracy than the GARCH, CARR, and VS-CARR models regarding out-of-sample forecast performance, suggesting that accounting for the volatility spillover and asymmetry can significantly improve the forecasting accuracy of Bitcoin volatility. The superior forecast performance of the VS-ACARR model is robust to alternative out-of-sample forecast windows. Our findings highlight the importance of accommodating intraday information, spillover from crude oil, and volatility asymmetry in forecasting Bitcoin volatility.     

Bank capital shortfall in the euro area

We quantify the bank capital shortfall that results from a financial crisis by estimating a macro-finance dynamic stochastic general equilibrium model that captures the interactions between the financial and real sectors of the euro-area economy. The introduction of both deposit and shadow banks captures several characteristics of the banking system and reveals a financial amplification mechanism. By using a combination of a large positive risk shock and a large negative investment shock, we show that a crisis similar to that observed in 2008 would generate a bank capital shortfall between 2.2% and 3% of euro-area GDP, which corresponds to approximately 207–282 billion euros.     

金融冲击、去杠杆与中国宏观经济波动

去杠杆是我国供给侧结构性改革的重要任务之一,而平稳有序地去杠杆是防范化解金融风险的关键:本文通过梳理我国在2016年前后的经济数据,刻画了去杠杆进程中我国宏观经济存在的“扩张—收缩”波动特征。基于此现实,本文在金融加速器理论基础上构建金融经济周期模型,尝试利用违约成本的变化引入金融冲击,从未预期和预期冲击两个视角理解去杠杆背景下中国的宏观经济波动。模型数值模拟结果表明,去杠杆过程前后信贷、杠杆率以及信用利差等重要宏观经济变量的波动不仅源自未预期违约成本的变化。违约成本预期的变化同样也可以很好地解释近年来我国重要宏观经济变量的“扩张—收缩”波动特征,为理解我国去杠杆进程中的宏观经济波动提供了一个新视角。基于本文结果,政府实施去杠杆政策时不仅应充分考虑违约成本的实际变动,还应重视金融机构的预期因素。     

上海金属期货市场的非线性波动特征研究

期货市场是一个典型的非线性动力系统,通过对上海期货交易所(SHFE)的铜、铝期货合约进行非线性波动特征检验,采用基于GED(广义误差分布)的GARCH族模型考察期货收益率的ARCH效应、杠杆效应,并用R/S分析法检验期货收益率和波动率的长期记忆性,得到的实证结果表明:铜、铝期货价格波动有明显的集丛性,铜期货收益率波动没有"杠杆效应",而对铝期货来说,"利好"对条件方差的冲击大于"利空"的冲击.R/S分析结果显示:铜、铝期货收益率均呈现长期记忆性,铜期货有一个约43个日历月的非周期循环,而铝期货并没有明显的非周期循环.更重要的是,实证结果表明期货收益波动率有明显的长期记忆性,因此,在对期货市场波动率建模时应充分考虑这一点.     

中国科技创新结构如何影响杠杆率?

杠杆率水平与系统性金融风险息息相关。杠杆率的高低与科技创新结构存在着密切关系,已有文献忽略了科技创新结构对杠杆率作用机理的研究。科技创新结构如何影响杠杆率?对此问题,本文进行系统性的理论分析,并以省际面板数据为对象进行实证检验。研究表明,我国省级科技研发劳动投入比例结构,以及创新产出结构对杠杆率具有重要影响,两类创新劳动投入对于杠杆率都具有显著作用,但影响方向相反;我国省级杠杆率具有空间效应,既有共性因子,又存在异质性特征。以上发现对于现阶段降低我国系统性金融风险,特别是为从创新的角度设计、制定和完善我国有关杠杆率的金融政策提供了科学的理论指导和实证证据。