Noise trading and stock market volatility

We investigate the relative effects of fundamental and noise trading on the formation of conditional volatility. We find significant positive (negative) effects of investor sentiments on stock returns (volatilities) for both individual and institutional investors. There are greater positive effects of rational sentiments on stock returns than irrational sentiments. Conversely, there are significant (insignificant) negative effects of irrational (rational) sentiments on volatility. Also, we find asymmetric (symmetric) spillover effects of irrational (rational) bullish and bearish sentiments on the stock market. Evidence in favor of irrational sentiments is consistent with the view that investor error is a significant determinant of stock volatilities.     

Forecasting European stock volatility: The role of the UK

This study investigates the lead–lag relationships of volatility among European stock markets. Using weakly realized variance measures, we examine volatility spillover dynamics between the UK and other major stock markets in Europe, thereby identifying a long-run leading role for the UK market portfolio. Lagged UK volatility can significantly predict volatilities in non-UK countries, whereas lagged non-UK volatility has a limited association with UK volatility. Moreover, pairwise Granger causality estimations, predictive regression specifications, and out-of-sample validations reveal that volatility shocks in the UK are gradually reflected in market fluctuations across Europe with varying market-specific delays. Our findings support the limited attention explanation for the volatility predictability of the lagged UK equity index.     

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a high-frequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting; the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias; and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility.     

Neural network volatility forecasts

We analyse whether the use of neural networks can improve ‘traditional’ volatility forecasts from time-series models, as well as implied volatilities obtained from options on futures on the Spanish stock market index, the IBEX-35. One of our main contributions is to explore the predictive ability of neural networks that incorporate both implied volatility information and historical time-series information. Our results show that the general regression neural network forecasts improve the information content of implied volatilities and enhance the predictive ability of the models. Our analysis is also consistent with the results from prior research studies showing that implied volatility is an unbiased forecast of future volatility and that time-series models have lower explanatory power than implied volatility. Copyright © 2008 John Wiley & Sons, Ltd.     

Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network

In the post-epidemic period, the international economic structure has been readjusted, with risks contagious across financial and economic systems. This paper primarily uses the high-frequency TENET network and the Granger-causality network to describe the interconnectedness between the tail risk of stock volatility and investor sentiment, then the two-layer network is constructed by the generalized variance decomposition method to examine the inter-layer connectedness. Based on the two-layer network, the heterogeneity frequency response of network connectedness and dynamic network structure are further analyzed from the perspective of frequency domain. The study found that the tail risk of high-frequency stock volatility displays industry heterogeneity and time-varying property, and investor sentiment contagion network provides information transmission medium for stock risk. The double-layer network study found that stock volatility in consumer goods industry exhibits higher risk spillover to investor sentiment. The diversified financial industry, real estate industry and energy industry in the two-layer network are systemically important industries. In addition, the study of the frequency domain dynamic network found that the connectedness volatility in the short-term risk network of stock volatility was significantly higher than that of the investor sentiment network, and the short-term risk spillover effect of the network played a leading role in the total risk spillover. The research conclusions provide reference for preventing systemic risks from the perspective of systemically important industries and cyclical fluctuations.     

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.     

国际原油价格对我国新能源行业股票价格的波动溢出效应研究

在大气污染日益严峻的情况下,新能源行业受政府大力支持和投资者青睐。新能源与原油一定程度上互为替代品,理论上国际原油价格必然对我国新能源行业股票价格有显著的波动溢出效应,但有些学者却持反对态度,认为我国股票市场对外还没有完全开放,新能源行业发展又很不成熟,所以该溢出效应很难显著。文章运用VAR- Asymmetric- BEKK模型进行比较研究得出：在未去除我国整体股市行情因素时,国际原油价格波动对我国新能源行业股票价格波动溢出效应不显著;而在去除我国整体股行情因素时,国际原油价格波动对我国新能源行业股票价格波动溢出效应在1%显著性水平下显著。表明存在从国际原油价格向我国新能源行业股票价格的波动溢出效应,只是该溢出效应被我国股市总体行情掩盖了。     

Asymmetric volatility spillover among Chinese sectors during COVID-19

Inter-sectoral volatility linkages in the Chinese stock market are understudied, especially asymmetries in realized volatility connectedness, accounting for the catastrophic event associated with the COVID-19 outbreak. In this paper, we examine the asymmetric volatility spillover among Chinese stock market sectors during the COVID-19 pandemic using 1-min data from January 2, 2019 to September 30, 2020. In doing so, we build networks of generalized forecast error variances by decomposition of a vector autoregressive model, controlling for overall market movements. Our results show evidence of the asymmetric impact of good and bad volatilities, which are found to be time-varying and substantially intense during the COVID-19 period. Notably, bad volatility spillover shocks dominate good volatility spillover shocks. The findings are useful for Chinese investors and portfolio managers constructing risk hedging portfolios across sectors and for Chinese policymakers monitoring and crafting stimulating policies for the stock market at the sectoral level.     

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.     

Transmission of Stock Returns and Volatility between the U.S. and Japan: Evidence from the Stock Index Futures Markets

In this paper, we examine the nature of transmission of stock returns and volatility between the U.S. and Japanese stock markets using futures prices on the S&P 500 and Nikkei 225 stock indexes. We use stock index futures prices to mitigate the stale quote problem found in the spot index prices and to obtain more robust results. By employing a two-step GARCH approach, we find that there are unidirectional contemporaneous return and volatility spillovers from the U.S. to Japan. Furthermore, the U.S.'s influence on Japan in returns is approximately four times as large as the other way around. Finally, our results show no significant lagged spillover effects in both returns and volatility from the Osaka market to the Chicago market, while a significant lagged volatility spillover is observed from the U.S. to Japan. This revised version was published online in August 2006 with corrections to the Cover Date.     

基于MSV与CoVaR模型的公司债市场与股票市场间风险溢出效应研究

依据2015—2017年中证公司债指数与沪深300指数的日收益数据,运用GC-t-MSV模型,检验中国公司债市场与股票市场间的风险溢出效应,并通过条件在险价值(CoVaR)模型度量两市场间风险溢出效应。结果表明：公司债市场与股票市场间存在不对称的双向风险溢出效应,且公司债市场对股票市场的风险溢出效应强于股票市场对公司债市场的风险溢出效应;公司债市场与股票市场的波动受其自身波动的影响较大,鉴此,监管部门和投资者应增强对公司债市场的关注,根据公司债市场的风险变化及时采取应对措施,充分发挥其风险信号作用。     

Can happiness predict future volatility in stock markets?

In this paper, we use the Twitter based happiness index as a proxy for investor sentiment in order to examine whether happiness influences future market volatility of country VIX indexes. Our sample includes the major stock markets of the USA, Canada, UK, Germany, France, Netherlands, Switzerland, Japan, China, Hong Kong, India, Brazil, South Korea, and South Africa. Using linear and nonlinear causality tests, we find that Twitter happiness significantly causes the future volatility of the sample countries. The robustness checks show no divergence from our primary findings and provide strong evidence of a nonlinear relationship between investor sentiment and future stock market volatility.     

A Likelihood Ratio Test of Price Volatilities: Comparing Stock Index Spot and Futures

This paper uses three methods to estimate the price volatility of two stock market indexes and their corresponding futures contracts. The classic variance measure of volatility is supplemented with two newer measures, derived from the Garman-Klass and Ball-Torous estimators. A likelihood ratio test is used to compare the classic variance measure of price volatilities of two stock market indexes and their corresponding futures contracts during the bull market of the 1980s. The stock market volatilities of the Standard & Poor's 500 (S&P 500) and New York Stock Exchange (NYSE) indexes were found to be significantly lower than their respective futures price volatilities. Since information may flow faster in the futures markets than in the corresponding stock market, our results support Ross's information-volatility hypothesis. It was also noted that the NYSE spot volatility was lower than the S&P 500 spot volatility. If the rate of information flow and firm size are positively related, then the lower NYSE spot volatility is explained by the size effect. The futures price volatilities for the two indexes were insignificantly different from each other. With stock index spot-futures price correlations approaching unity, one implication of our results for index futures activity is that smaller positions in futures contracts may suffice to achieve hedging or arbitrage goals.     

基于高频数据的股指期货与现货市场波动溢出和信息传导研究

本文利用沪深300指数和当月股指期货连续合约的高频数据,采用非参数方法估计日度股票指数和股指期货的整体波动、连续性波动和跳跃,发现两个市场波动成分存在双向的格兰杰因果关系,但是期货市场的跳跃并不会影响后续股票市场的跳跃。此外,已实现相关系数在股指期货上市初期表现出了较大的变动,整体表现出了较强的联动趋势。最后,日内高频价格之间存在稳定的协整关系,两个市场存在双向的信息传导,股指期货的价格发现功能得到发挥。     

Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models

This paper investigates how geopolitical risks influence the prediction performance on the US stock market volatility with machine learning models. Further, it compares the predictive performance of individual and combination forecast methods. With SHAP algorithm, it could identify which factor has a great impact and fully extract the information of geopolitical risks in predicting. Empirical results show that military build-ups and escalation of war have great importance on predicting realized volatility among various geopolitical risks. The research further emphasizes the superior performance of machine learning and forecast combination methods, especially SVR method and trimmed mean combination. In addition, by allocating portfolio according to the machine learning-based volatility forecasts, particularly elastic net and random forest, a mean-variance investor can achieve sizeable financial benefits. Our paper provides substantial implications for political risk management and volatility forecasting.     

The Lead‐Lag Relationship Between Cash and Stock Index Futures in a New Market

This paper investigates the lead‐lag relationship in daily returns and volatilities between price movements of the FTSE/ATHEX‐20 and FTSE/ATHEX Mid‐40 stock index futures and the underlying cash indices in the relatively new futures market of Greece. Empirical results show that there is a bi‐directional relationship between cash and futures prices. However, futures lead the cash index returns, by responding more rapidly to economic events than stock prices. This speed is much higher in the more liquid FTSE/ATHEX‐20 market. Moreover, results indicate that futures volatilities spill information over to the corresponding cash market volatilities in both investigated futures markets, but volatilities in the cash markets have no effect on the volatilities of futures markets. Overall, it seems that new market information is disseminated faster in the futures market compared to the stock market. This implies that the futures markets can be used as price discovery vehicles, providing further evidence that derivatives markets contribute to completing and stabilising capital markets in Greece. A further finding of this study is that futures volume and disequilibrium effects between cash and futures prices are important variables in the explanation of volatilities in cash and futures markets.     

南橘北枳:A股市场的经济关联与股票回报

本文系统检验并比较了中国A股市场中行业动量、区域动量、供应链动量以及科技关联动量等经济关联动量的显著性及预测周期。本文发现,中国股票市场中经济关联因子呈现出与美国股票市场不同的规律,在月度层面行业动量显著,而科技关联因子只在周度上具有显著的预测能力。进一步分析科技关联动量发现,中国股票市场中科技关联因子能预测目标公司未来1-3周的股票收益和未来基本面的变化,据此构建的多空策略能够产生周度0.16%的超额收益(年化8.67%);机制检验发现,科技关联因子预测期短的原因是由于中国股票市场中存在较多具有博彩倾向的散户投资者;有限注意和市场摩擦两个机制检验证明科技关联动量源自错误定价。进一步检验发现,科技关联动量在国有企业和创新政策颁布后更加显著。本文补充了现有A股市场的动量研究,有助于理解中国股票市场规律、提升资本市场有效性。     

投资者关注与市场反应——来自中国证券交易所交易公开信息的自然实验

涨停的股票能否被交易公开信息披露取决于收益率排名中的随机因素,与股票的基本面特征无关。本文利用这一机制设计自然实验检验了投资者关注对股价的影响。实证结果显示交易公开信息披露使股票受到投资者更多的关注,增加了小额资金的净流入,减少了大额资金的净流入和股价的短期收益率,抑制了股价短期波动率,同时降低了股价在长期发生反转的可能性。频繁登上交易公开信息的知名营业部买入的股票受到更多关注,相应的市场反应也更加显著。进一步的研究表明监管性信息披露引发的投资者关注通过降低市场信息不对称抑制了股价反转。     

Dynamic volatility spillovers and investment strategies between the Chinese stock market and commodity markets

Building on the increased interest in the volatility spillover effects between Chinese stock market and commodity markets, this paper investigates the dynamic volatility spillovers of Chinese stock market and Chinese commodity markets based on the volatility spillover index under the framework of TVP-VAR. The result shows that there is a highly dependent relationship between the stock market and commodity markets. On average, the Chinese stock market is the net recipient of spillover, non-ferrous metals and chemical industry have a very obvious spillover impact on the stock market. The degree of total volatility spillover is different in different periods. After major crisis events, the volatility correlation between markets increases. Since the outbreak of COVID-19, the spillover effect of the stock market on the commodity market has been significantly enhanced. Then optimal portfolio weights and hedge ratios are calculated for portfolio diversification and risk management. The result shows that the ability of most commodities to hedge against risks is significantly reduced when the crisis occurs; NMFI (precious metals) and CRFI (grain) still have good hedging ability after the crisis, but the effectiveness of hedging risk is relatively low. Besides, the combination of CRFI and SHCI (the Shanghai composite index) is the most effective for risk reduction.     

波动溢出网络视角的金融风险传染研究

当前各类经济风险交叉关联,金融系统的风险溢出效应备受关注,为刻画我国金融系统性风险传染的路径特征,本文从波动溢出网络的视角分析金融系统内部的风险传染机制。首先使用广义动态因子模型对收益波动的共同波动率成分和特质性波动率成分进行区分。然后,根据货币市场、资本市场、大宗商品交易市场、外汇市场、房地产市场和黄金市场之间的特质性波动溢出效应,利用基于TVP-VAR模型的方差分解溢出指数分析金融系统波动溢出的动态联动性和风险传递机制。在分析方向性波动溢出效应的基础上,采用方差分解网络方法构建起信息溢出复杂网络,从网络视角分析金融系统内部的风险传染特征。实证研究发现,房地产市场和外汇市场的净溢出效应绝对值相较于其他市场更大,其受其他市场风险冲击的影响强于对外风险溢出效应,而股票市场的单向对外风险溢出效应强度最大。在波动溢出的基础上,进一步考虑股市波动率指数与其他市场波动率指数进行投资组合的资产配置权重,计算了波动率指数投资组合的最优组合权重和对冲策略。研究结论有助于更好地理解我国金融系统的风险传染机制,对监管机构加强宏观审慎监管、投资者规避投资风险具有重要意义。