Do jet fuel price movements help forecast airline fares and the demand for air travel?

The paper studies the predictive content of jet fuel prices for the U.S. aviation industry through in-sample and out-of-sample forecasting exercises. Our results suggest the possibility of limited improvements in the predictions of airline fares, and little evidence of predictability from jet fuel prices to measures of air travel demand.     

经济周期波动转折点的向量自回归门限模型分析--基于12个国家（或地区）1970～2012年数据

经济周期波动转折点的识别与检验问题一直是经济周期理论所关注的重要内容。改进目前使用较为广泛的经济周期转折点CDR指标,扩展其取值范围并消除该指标外生决定的缺点。以12个国家（或地区）1970～2012年的季度数据,分别构建以CDR、修正后的CDR为门限变量的向量自回归门限模型,进行样本外预测能力比较。实证结果显示,以MCDR指标作为门限变量的模型预测能力优于CDR。     

Do misalignments predict aggregated stock-market volatility?

This paper considers forecasting regressions of “realized volatility” on a misalignment measure. Results show that this misalignment measure is useful to predict in and out-of-sample stock-market volatility at monthly horizons. The analysis also suggests a threshold effect.     

Have structural changes eliminated the out-of-sample ability of financial variables to forecast real activity after the mid-1980s? Evidence from the Canadian economy

This article evaluates how consistently reliable the information content of individual financial variables is for Canada's future output growth. We estimate the timing of structural changes in linear growth models and check robustness to specification changes, multiple breaks, and business cycle asymmetry. Our simulated out-of-sample forecast evaluation strategy, using the Mean Square Error F-type (MSE-F) and the new encompassing (ENC-NEW) tests, shows that the leading information content of most financial variables for Canada's future Gross Domestic Product (GDP) growth has deteriorated substantially after 1984:04, but the 1–3-year term spread exhibits a consistently reliable predictive ability at the 1 and 2 quarter horizons and has significant forecasting ability at the 8 quarter horizon. Also, the real M1 money growth has regained its ability to forecast output growth since 1991:01.     

Understanding the recent performance of China's stock market

Employing time-series extrapolation and an out-of-sample forecast based on a bivariate VAR (vector auto-regression), we argue that the current boom of China's stock market represents a recovery that corrects the previous divergence of the stock market from the aggregate economic performance. Nevertheless, we caution that the speed of the recent rise in stock prices is alarming. If the current speed continues, then the stock market will soon become overheated in the sense that the level of stock prices will exceed the level justified by economic fundamentals.     

Economic policy uncertainty and the Chinese stock market volatility: new evidence

This study investigates the impacts of the economic policy uncertainty (EPU) indexes of China and the G7 countries on Chinese stock market volatility and further constructs a new diffusion index based on these indexes using principal component analysis (PCA) to achieve enhanced predictive ability. The in-sample results indicate that the EPU indexes of China and some of the G7 countries show a significantly negative impact on future volatility. Moreover, our constructed diffusion index also has a significantly negative impact. Furthermore, the out-of-sample results show that this diffusion index exhibits a significantly higher forecast accuracy than the EPU itself and combination forecasts. Finally, various robustness checks are consistent with our main conclusions. Overall, we construct a new and useful indicator that can substantially increase forecast accuracy with respect to the Chinese stock market.     

房地产信托投资基金研究的文献计量分析

从数量特征、合作特征、学科特征和研究热点等维度对房地产信托投资基金的国际研究现状进行了文献计量分析,监控了国际研究态势,发现了该领域的研究热点和方向。     

基于联合分析的新产品市场占有率预测——学生笔记本电脑市场的实证分析

市场竞争日趋激烈,企业要想在市场竞争中立于不败之地,在开发一种新产品时,势必要预测新产品在市场上的份额,本文在简述联合分析基本原理的基础上,以开发学生笔记本电脑市场的新产品为例,采用联合分析方法中的最大效用值法来预测新产品的市场占有率。     

Asymmetric ACD models: Introducing price information in ACD models

This paper proposes an asymmetric autoregressive conditional duration (ACD) model, which extends the ACD model of Engle and Russell (1998). The asymmetry consists of letting the duration process depend on the state of the price process. If the price has increased, the parameters of the ACD model can differ from what they are if the price has decreased. The model is applied to the bid-ask quotes of two stocks traded on the NYSE and the evidence in favour of asymmetry is strong. Information effects (Easley and O'Hara 1992) are also empirically relevant. As the model is a transition model for the price process, it delivers `market forecasts' of where prices are heading. A trading strategy based on the model is implemented using tick-by-tick data.While remaining responsible for any error in this paper, the authors would like to thank R. Anderson, G. Le Fol, C. Gouriéroux, J. Jasiak, W. Pohlmeier, A. Roell, O. Scaillet, S. Wei and three anonymous referees for useful remarks and suggestions on previous versions. The authors would also like to thank A. Ruttiens from KBC-CBC for useful discussions on practical issues related to trading. Support of the European Commission Human Capital and Mobility Program through the network `Econometric inference using simulation methods' is gratefully acknowledged. This paper presents research results of the Belgian Program on Interuniversity Poles of Attraction initiated by the Belgian State, Prime Minister's Office, Science Policy Programming. The scientific responsibility is assumed by the authors.     

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.     

Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market

Based on methods developed by Bollerslev et al. (2016), we explicitly accounted for the heteroskedasticity in the measurement errors and for the high volatility of Chinese stock prices; we proposed a new model, the LogHARQ model, as a way to appropriately forecast the realized volatility of the Chinese stock market. Out-of-sample findings suggest that the LogHARQ model performs better than existing logarithmic and linear forecast models, particularly when the realized quarticity is large. The better performance is also confirmed by the utility based economic value test through volatility timing.