Volatility spillovers between WTI and Brent spot crude oil prices: an analysis of granger causality in variance patterns over time

There has been an increase in price volatility in oil prices during and since the global financial crisis (GFC). This study investigates the Granger causality patterns in volatility spillovers between West Texas International (WTI) and Brent crude oil spot prices using daily data. We use Hafner and Herwartz’s (2006) test and employ a rolling sample approach to investigate the changes in the dynamics of volatility spillovers between WTI and Brent oil prices over time. Volatility spillovers from Brent to WTI prices are found to be more pronounced at the beginning of the analysis period, around the GFC, and more recently in 2020. Between 2015 and 2019, the direction of volatility spillovers runs unidirectionally from WTI to Brent oil prices. In 2020, however, a Granger-causal feedback relation between the volatility of WTI and Brent crude oil prices is again detected. This is due to the uncertainty surrounding how the COVID-19 pandemic will evolve and how long the economies and financial markets will be affected. In this uncertain environment, commodities markets participants could be reacting to prices and volatility signals on both WTI and Brent, leading to the detection of a feedback relation.     

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.     

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.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

中国股市行业风险与宏观经济之间的风险传导机制

本文采用行业收益率溢出指数度量股市行业风险,并进一步研究中国股市行业风险与宏观经济的相互影响,同时引入股息率和利率两个中介渠道深入挖掘其传导机制。我们运用GARCH-in-Mean模型对股市行业风险和宏观经济变量之间的一阶矩和二阶矩相互关系同时进行分析,结果发现,股市行业风险和宏观经济变量之间水平值和波动率都存在双向影响,对外溢出效应较大的行业起主导作用。此外,股市行业风险对宏观经济变量的影响方面,股息率和利率均起到中介渠道作用;宏观经济变量对股市行业风险的影响方面,只是利率起到中介渠道作用。股市行业风险与宏观经济的传导效应在不同时期差异显著。本文研究结论有助于深刻理解金融与实体经济之间的风险传导机制,对防范系统性风险、防止金融和实体经济“风险共振”以及提升金融服务实体经济能力等具有参考意义。     

Cross-border exchanges and volatility forecasting

We test for the performance of a series of volatility forecasting models (GARCH 1,1; EGARCH 1,1; CGARCH) in the context of several indices from the two oldest cross-border exchanges (Euronext; OMX). Our findings overall indicate that the EGARCH (1,1) model outperforms the other two, both before and after the outbreak of the global financial crisis. Controlling for the presence of feedback traders, the accuracy of the EGARCH (1,1) model is not affected, something further confirmed for both the pre and post crisis periods. Overall, ARCH effects can be found in the Euronext and OMX indices, with our results further indicating the presence of significant positive feedback trading in several of our tests.     

外商直接投资对工业技术溢出效应的分析——基于江苏省与广东省的比较研究

江苏和广东是我国FDI流入最为集中的地区,有关资料显示,两个地区的外资流入量占我国外资流入量的60%左右,同时流入两省2/3的资金流向工业。本文检验了1995～2006年间江苏与广东的外商直接投资对工业的技术外溢效应,同时检验了FDI外溢效应影响因素人力资本、贸易开放度和R&D对外溢效应的影响。研究认为,从总体上看两省利用FDI有正的溢出效应,但并不显著。     

THE CHANGING VOLATILITY OF THE SOUTH AFRICAN ECONOMY

During the last decade economic literature explored the presence of and reasons for what became known as “the great moderation” in the US and other G7 countries. “The great moderation” describes the decrease in economic volatility experienced in many of the G7 countries. This paper finds that in South Africa volatility is also not constant (it even finds that there are autoregressive conditional heteroskedastic effects present) and that volatility also decreased, particularly since 1994. Following the literature, the paper explores several reasons for this decrease and finds that smaller shocks, better monetary policy and improvements in the financial sector that place less liquidity constraints on individuals and allow them to manage their debt better are some of the main reasons for the reduction in the volatility of the South African economy. The literature on the G7 also suggests that better inventory management contributed to the lower volatility. However, this seems not to be true for South Africa.     

公司股份回购负财务效应分析

股份回购早已成为上市公司为配合资本结构决策或股利政策而广泛采用的财务举措,而在我国股份回购无疑也是证券市场上的一项重大制度突破和金融创新。本文首先阐述了股份回购的基本理论,然后结合东北老工业基地公司股份回购的相关情况,着重地分析了股份回购的负财务效应。最后针对股份回购负财务效应分析中所发现的问题,提出了一系列相应的建议和解决措施,使其无论在理论上还是实践上的研究都得到了进一步的完善。     

货币政策地区效应——基于地区总供给曲线的理论与实证分析

本文着重检验了我国区域经济中的基于产出缺口的地区短期总供给曲线的存在性,进而分析地区短期总供给曲线对货币政策地区效应的影响。文章首先对地区短期总供给曲线存在的可能性进行了理论解释,应用卡尔曼滤波方法估算了我国各地区的产出缺口,然后运用ARMA模型说明预期通货膨胀率形成机制,最后以通货膨胀率与其预期之差、外部需求冲击等作为解释变量对产出缺口进行回归,结果发现,我国东部地区的短期总供给曲线较中西部地区的短期总供给曲线更平坦,因而东部地区货币政策效应要强于中西部地区。