Industry risk transmission channels and the spillover effects of specific determinants in China’s stock market: A spatial econometrics approach

This paper uses a spatial econometrics approach to study the industry risks in China’s stock market. We comprehensively consider the real linkage and information risk transmission channels and analyze the risk spillovers of specific determinants. Our empirical results show the following: 1) The real linkage channel and information channel are both effective transmission channels for driving spillover effects, and the information channel is of the utmost importance. 2) The spillover effects of specific determinants exist and are persistent. The superposition of spillover effects may lead to extreme risk. 3) The transmission channels and spillover effects are asymmetric in different regimes.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

Environmental,social, and governance practices and perceived tail risk

Using the implied volatility smirk on individual equity securities to measure perceived tail risk, we find that better environmental, social and governance (ESG) practices significantly reduce ex-ante expectations of a left-tail event. Our findings are robust to using multiple model specifications and to adjusting for potential endogeneity concerns. We also show that, while practices in each ESG pillar are important in reducing perceived tail risk, the environmental pillar plays the most important role. Our results indicate that investors consider strong ESG practices to be insurance against left-tail events rather than wasteful investment borne out of managers’ own values or self-interest.     

Discussions on the spurious hyperbolic memory in the conditional variance and a new model

This paper studies the spurious hyperbolic memory in the conditional variance caused by the Markov Regime-Switching GARCH (MRS-GARCH) process. We firstly propose an illustrative cause of this spuriousness and provide simulation evidence. An MRS Hyperbolic GARCH (MRS-HGARCH) model is then developed to successfully address it. Related statistical properties including the stationarity conditions and asymptotic behaviours of the maximum likelihood estimators of the MRS-HGARCH process are also investigated. An empirical study of the S&P 500 and TOPIX indexes returns is then conducted which demonstrates that our MRS-HGARCH model can provide a more reliable estimator of the hyperbolic-memory parameter and outperform both the HGARCH and MRS-GARCH models.     

数字经济对区域创新绩效及其空间溢出效应的影响

数字化时代的经济运行方式呈现出数据驱动、万物互联、创新迭代的特征，数字经济的数字化、网络化、智能化属性对创新活动产生了深刻影响。运用空间杜宾模型，对361座城市2010-2018年度数据进行实证检验。检验结果表明，数字经济发展不仅能够带动本地创新绩效提升，而且对周边城市创新绩效也具有显著正向影响；与中、西部地区相比，东部地区数字经济对创新绩效的促进作用更强，空间溢出效应也更显著。由此，建议可从挖掘数据要素价值、搭建数字化平台、推动数字技术创新等角度发展数字经济，促进本地区和周边地区创新绩效共同提升。     

城市创新行为改善生态效率了吗？——基于空间关联与溢出视角的考察

文章在理论层面探讨了创新对生态效率的影响机制，并基于城市空间关联与溢出视角，采用我国2007-2016年260个城市面板数据，运用空间计量模型检验了创新对生态效率的影响效应及多维度的城市异质性。进一步地，文章还通过对不同距离阈值上的创新空间溢出效应进行估计，测算了创新空间溢出的距离边界，检验了创新空间溢出驱动城市绿色发展的最佳地理距离区间，从而为增强区域创新合作以提升区域整体绿色发展水平提供了可供参考的量化依据。研究发现：总体而言，创新具有的增长效应、资源与环境效应、结构优化效应是促进城市生态效率改善的重要途径，且产出端创新对生态效率的驱动效应强于投入端创新；但创新对生态效率的影响具有显著的城市异质性，依赖于城市区位、所在城市群、环境政策、资源禀赋、创新能力和市场化水平；城市创新行为具有显著的空间溢出效应，但空间溢出随距离变化呈衰退趋势，350公里以内为创新活动的密集溢出区；生态效率也具有明显的空间溢出效应，经济越发达且联系越紧密、要素流动越自由、创新对生态效率驱动效应越强的地区，生态效率的空间溢出也越强。因此政府应完善创新激励政策、为创新外溢创造良好条件、注重引导创新的方向，并根据区域及城市发展实际，采取差异化的创新驱动绿色发展策略。     

Decomposing local fiscal multipliers: Evidence from Japan

Recent studies on fiscal policy use cross-sectional data and estimate local fiscal multipliers along with spillovers. This paper estimates local fiscal multipliers with spillovers using Japanese prefectural data comparable with the national accounts. We estimate the local fiscal multiplier on output to be 1.7 at the regional level. We decompose the regional fiscal multiplier into the prefectural fiscal multiplier and the region-wide effect. Converting the latter component into the spillover, we find that the spillover is positive and small in size. We also decompose the regional fiscal multiplier on output into multipliers on the expenditure components. Our estimates suggest that there are crowding-in effects of government spending on consumption and investment. Moreover, we find that the regional fiscal multiplier on absorption exceeds 2.0 and that the spillover to absorption is considerable in contrast to the spillover to output.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

Price discovery and volatility spillover in spot and futures markets: evidences from steel-related commodities in China

The price discovery and spillover effect are significant indicators in futures markets. This study examines the price discovery and spillover effects using vector error correction model and generalized autoregressive conditional heteroskedastic for seven types of steel products in Chinese spot and futures markets. The results show that the price discovery exists in all of steel futures market. It is also confirmed that futures prices in all items are mainly leading spot prices via permanent-transitory and information share. In the results of spillover effects, it is found that wire rod, coking coal, coke and silico-manganese have the effects between spot and futures market. In rebar market, there is the spillover effect from spot to futures. This information about futures prices can help the market participants to make decisions when they predict the spot prices.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.