The predictive value of temporally disaggregated volatility: evidence from index futures markets

This paper examines the benefits to forecasters of decomposing close-to-close return volatility into close-to-open (nighttime) and open-to-close (daytime) return volatility. Specifically, we consider whether close-to-close volatility forecasts based on the former type of (temporally aggregated) data are less accurate than corresponding forecasts based on the latter (temporally disaggregated) data. Results obtained from seven different US index futures markets reveal that significant increases in forecast accuracy are possible when using temporally disaggregated volatility data. This result is primarily driven by the fact that forecasts based on such data can be updated as more information becomes available (e.g., information flow from the preceding close-to-open/nighttime trading session). Finally, we demonstrate that the main findings of this paper are robust to the index futures market considered, the way in which return volatility is constructed, and the method used to assess forecast accuracy. Copyright © 2008 John Wiley & Sons, Ltd.     

Revisions in analysts' earnings forecasts: evidence of non‐linear adaptive expectations

Prior studies use a linear adaptive expectations model to describe how analysts revise their forecasts of future earnings in response to current forecast errors. However, research shows that extreme forecast errors are less likely than small forecast errors to persist in future years. If analysts recognize this property, their marginal forecast revisions should decrease with the forecast error's magnitude. Therefore, a linear model is likely to be unsatisfactory at describing analysts' forecast revisions. We find that a non‐linear model better describes the relation between analysts' forecast revisions and their forecast errors, and provides a richer theoretical framework for explaining analysts' forecasting behaviour. Our results are consistent with analysts' recognizing the permanent and temporary nature of forecast errors of differing magnitudes. Copyright © 2000 John Wiley & Sons, Ltd.     

谈欧盟国际贸易救助司法审查中的两个重要问题——“挪威海鲜股份有限公司诉欧洲委员会”案例分析

作为WTO的创始缔约方,欧盟在国际贸易救助司法审查制度的制度建构方面积累了许多成熟的经验和做法。欧洲初审法院在“挪威海鲜股份有限公司诉欧洲委员会”一案的判决中就行政侵权损害赔偿之诉的独立性以及行政非契约责任的构成问题作了独到而深刻的阐述。分析借鉴该案例将使我国相关立法部门、司法部门及律师实务界受益良多。     

欧盟重返亚洲及近年来的欧中经济关系

两极格局时期，西欧国家出于自身战略安全的需要，一直把对外关系的重心放在欧美关系上，在亚洲实行“收缩”政策。冷战结束后，随着世界政治经济化、经济政治化的进一步发展，欧盟越来越认识到亚洲的重要性。欧盟出于自身政治经济和战略利益的需要，开始推行“重返亚洲”的战略，亚欧关系近年来得到了很大发展。但由于历史、文化、传统和社会制度等方面的差异，以及欧亚双方各自战略利益的不同，再加上美日的影响，不利于欧盟重返亚洲和欧亚加强合作的因素仍然存在。随着中国在世界政治经济中的影响日益骤增，中国在欧盟全球战略中的地位越来越重要。在双方的共同努力下，欧中经济关系有了长足的发展，但并不尽如人意，还存在着影响双方关系进一步发展的障碍。欧盟重返亚洲，亚欧加强合作，特别是欧中加强合作，是历史之必然趋势，它对世界格局和亚欧国家本身都会产生重大影响。     

Can forecasting performance be improved by considering the steady state? An application to Swedish inflation and interest rate

This paper investigates whether the forecasting performance of Bayesian autoregressive and vector autoregressive models can be improved by incorporating prior beliefs on the steady state of the time series in the system. Traditional methodology is compared to the new framework—in which a mean‐adjusted form of the models is employed—by estimating the models on Swedish inflation and interest rate data from 1980 to 2004. Results show that the out‐of‐sample forecasting ability of the models is practically unchanged for inflation but significantly improved for the interest rate when informative prior distributions on the steady state are provided. The findings in this paper imply that this new methodology could be useful since it allows us to sharpen our forecasts in the presence of potential pitfalls such as near unit root processes and structural breaks, in particular when relying on small samples. Copyright © 2008 John Wiley & Sons, Ltd.     

大米草半纤维素选择性酶解研究

采用由东方肉座菌EU7-22表达的重组木聚糖酶HoXyn11A、黑曲霉BE-2表达的重组木聚糖酶AnXyn10C和商品木聚糖酶分别降解大米草半纤维素,探索其降解的最佳工艺条件,结果发现该3种木聚糖酶最适pH值为4.5～5.5,最佳反应温度为45～55℃,酶用量为200～300 IU·g^-1底物,反应时间为12～24 h.对比结果表明:通过黑曲霉BE-2表达的重组木聚糖AnXyn10C能够在24 h内基本将大米草半纤维素降解为低聚木糖,效率远高于商品酶和东方肉座菌EU7-22表达的重组木聚糖酶HoXyn11A.黑曲霉BE-2表达的重组木聚糖AnXyn10C是大米草半纤维素降解酶的良好来源.     

Real‐time inflation forecast combination for time‐varying coefficient models

We use real‐time macroeconomic variables and combination forecasts with both time‐varying weights and equal weights to forecast inflation in the USA. The combination forecasts compare three sets of commonly used time‐varying coefficient autoregressive models: Gaussian distributed errors, errors with stochastic volatility, and errors with moving average stochastic volatility. Both point forecasts and density forecasts suggest that models combined by equal weights do not produce worse forecasts than those with time‐varying weights. We also find that variable selection, the allowance of time‐varying lag length choice, and the stochastic volatility specification significantly improve forecast performance over standard benchmarks. Finally, when compared with the Survey of Professional Forecasters, the results of the best combination model are found to be highly competitive during the 2007/08 financial crisis.     

Information content of DSGE forecasts

This paper examines the question whether information is contained in forecasts from dynamic stochastic general equilibrium (DSGE) models beyond that contained in lagged values, which are extensively used in the models. Four sets of forecasts are examined. The results are encouraging for DSGE forecasts of real GDP. The results suggest that there is information in the DSGE forecasts not contained in forecasts based only on lagged values, and that there is no information in the lagged‐value forecasts not contained in the DSGE forecasts. The opposite is true for forecasts of the GDP deflator.     

Combination of forecasts using self‐organizing algorithms

Based on the theories and methods of self‐organizing data mining, a new forecasting method, called self‐organizing combining forecasting method, is proposed. Compared with optimal linear combining forecasting methods and neural networks combining forecasting methods, the new method can improve the forecasting capability of the model. The superiority of the new method is justified and demonstrated by real applications. Copyright © 2005 John Wiley & Sons, Ltd.     

Forecasting from non-linear models in practice

If a simple non-linear autoregressive time-series model is suggested for a series, it is not straightforward to produce multi-step forecasts from it. Several alternative theoretical approaches are discussed and then compared with a simulation study only for the two-step case. It is suggested that fitting a new model for each forecast horizon may be a satisfactory strategy.