基于ARIMA模型的我国航空货运量预测分析

借助PASW Statistics软件对2002年1月至2009年12月我国航空货运量月度数据序列进行分析,发现我国航空货运量的发展变化具有明显的上升趋势和季节性。通过构建航空货运量的ARIMA预测模型并进行检验,结果表明,ARIMA模型对原始数据序列有着较好的拟合效果,模型的预测误差较小,可应用于短期内我国航空货运量的预测,为进一步的航空货运市场调控提供有效依据。     

A novel auto-regressive fractionally integrated moving average–least-squares support vector machine model for electricity spot prices prediction

In the framework of competitive electricity market, prices forecasting has become a real challenge for all market participants. However, forecasting is a rather complex task since electricity prices involve many features comparably with those in financial markets. Electricity markets are more unpredictable than other commodities referred to as extreme volatile. Therefore, the choice of the forecasting model has become even more important. In this paper, a new hybrid model is proposed. This model exploits the feature and strength of the auto-regressive fractionally integrated moving average model as well as least-squares support vector machine model. The expected prediction combination takes advantage of each model's strength or unique capability. The proposed model is examined by using data from the Nordpool electricity market. Empirical results showed that the proposed method has the best prediction accuracy compared to other methods.     

A robust closed-form estimator for the GARCH(1,1) model

In this paper we extend the closed-form estimator for the generalized autoregressive conditional heteroscedastic (GARCH(1,1)) proposed by Kristensen and Linton [A closed-form estimator for the GARCH(1,1) model. Econom Theory. 2006;22:323–337] to deal with additive outliers. It has the advantage that is per se more robust that the maximum likelihood estimator (ML) often used to estimate this model, it is easy to implement and does not require the use of any numerical optimization procedure. The robustification of the closed-form estimator is done by replacing the sample autocorrelations by a robust estimator of these correlations and by estimating the volatility using robust filters. The performance of our proposal in estimating the parameters and the volatility of the GARCH(1,1) model is compared with the proposals existing in the literature via intensive Monte Carlo experiments and the results of these experiments show that our proposal outperforms the ML and quasi-maximum likelihood estimators-based procedures. Finally, we fit the robust closed-form estimator and the benchmarks to one series of financial returns and analyse their performances in estimating and forecasting the volatility and the value-at-risk.     

税收增长预测的灰色理论模型研究

税收是国家财政收入的重要来源,准确的税收预测结果对于制定各项经济政策具有非常重要的意义。文章在传统GM(1,1)模型的基础上,通过改变背景值,提出了用于中国税收预测的改进GM(1,1)模型。实例分析采用中国1994-2008年共15年的税收数据,预测结果表明:灰色预测可以较好地模拟出税收总量的变化趋势,而改进的GM(1,1)模型比传统的GM(1,1)模型所得到的预测结果更加合理。并且,整个预测过程思路简洁,易于编程实现,在当前中国税收政策变化力度加大、受国内外经济波动影响颇深的情形下,依然不失为一种有效的税收预测方法。     

基于相关系数的钢材需求量组合预测

近年来我国钢材消费量和产量大幅提升,在1997～2006年的10年里分别增长了308%和367%,虽然同期内国内铁矿石产量也增加了119%,但仍无法满足钢铁工业的需求,对外依存度从28.5%提升到51.8%。在对钢材以及铁矿石消费量的预测实践中,由于不同的预测方法能够提供不同的有用信息,其预测精度往往也存在差异,为了分散预测的风险,文章采用基于相关系数的组合预测方法对我国未来的成品钢材需求量进行预测,并对预测结果进行了深入分析。结果表明,基于相关系数的组合预测模型在预测准确性和适应性方面均能得到一定改善,能很好地对钢材需求量进行科学、有效的预测。     

Testing Multiple Forecasters

We consider a cross‐calibration test of predictions by multiple potential experts in a stochastic environment. This test checks whether each expert is calibrated conditional on the predictions made by other experts. We show that this test is good in the sense that a true expert—one informed of the true distribution of the process—is guaranteed to pass the test no matter what the other potential experts do, and false experts will fail the test on all but a small (category I) set of true distributions. Furthermore, even when there is no true expert present, a test similar to cross‐calibration cannot be simultaneously manipulated by multiple false experts, but at the cost of failing some true experts.     

Do we need the constant term in the heterogenous autoregressive model for forecasting realized volatilities?

No-constant strategy is considered for the heterogenous autoregressive (HAR) model of Corsi, which is motivated by smaller biases of its estimated HAR coefficients than those of the constant HAR model. The no-constant model produces better forecasts than the constant model for four real datasets of the realized volatilities (RVs) of some major assets. Robustness of forecast improvement is verified for other functions of realized variance and log RV and for the extended datasets of all 20 RVs of Oxford-Man realized library. A Monte Carlo simulation also reveals improved forecasts for some historic HAR model estimated by Corsi.     

Modeling and forecasting realized covariance matrices with accounting for leverage

The existing dynamic models for realized covariance matrices do not account for an asymmetry with respect to price directions. We modify the recently proposed conditional autoregressive Wishart (CAW) model to allow for the leverage effect. In the conditional threshold autoregressive Wishart (CTAW) model and its variations the parameters governing each asset's volatility and covolatility dynamics are subject to switches that depend on signs of previous asset returns or previous market returns. We evaluate the predictive ability of the CTAW model and its restricted and extended specifications from both statistical and economic points of view. We find strong evidence that many CTAW specifications have a better in-sample fit and tend to have a better out-of-sample predictive ability than the original CAW model and its modifications.     

考虑成分股联跳与宏观信息发布的沪深300指数已实现波动率模型研究

利用日内高频数据计算的已实现波动率较好度量了金融资产的风险,因此对其预测模型的研究具有重要意义。考虑到指数成分股的联跳可能蕴含指数跳跃所未能反映的信息,提出运用非参数方法识别指数成分股的联跳,采用自回归条件风险模型估计成分股联跳强度,并将其引入指数的已实现波动率异质自回归（HAR-RV-CJ）模型中,分析模型预测性能的改进。进一步的,考虑到宏观信息公告的发布可能对股市产生整体性影响,相应影响成分股联跳的几率;因此,在成分股联跳的自回归条件风险模型中引入居民消费价格指数、国内生产总值、贸易差额等宏观信息公告变量,并分析对联跳强度估计以及指数已实现波动率预测的影响。采用2011年1月4日至2013年7月11日沪深300指数及其成分股高频数据的实证表明,指数成分股联跳与指数跳跃具有不同的特征;用成分股联跳强度代替HAR-RV-CJ模型中的跳跃构建的HAR-RV-CI模型,较原始的HAR-RV-CJ模型,以及同时考虑指数跳跃与成分股联跳强度的HAR-RV-CJI模型,具有明显较优的样本内拟合与样本外预测性能。引入宏观信息公告变量可以改进联跳强度自回归条件风险模型的拟合效果,并提高指数已实现波动率模型的样本内拟合能力,但对于指数已实现波动率的样本外预测性能并无明显的帮助。     

A Sales Forecast Model for Short‐Life‐Cycle Products: New Releases at Blockbuster

We develop, in this article, a sales model for movie and game products at Blockbuster. The model assumes that there are three sales components: the first is from consumers who have already committed to purchasing (or renting) a product (e.g., based on promotion of, or exposure to, the product prior to its launch); the second comes from consumers who are potential buyers of the product; and the third comes from either a networking effect on closely tied (as in a social group) potential buyers from previous buyers (in the case of movie rental and all retail products) or re‐rents (in the case of game rental). In addition, we explicitly formulate into our model dynamic interactions between these sales components, both within and across sales periods. This important feature is motivated by realism, and it significantly contributes to the accuracy of our model. The model is thoroughly tested against sales data for rental and retail products from Blockbuster. Our empirical results show that the model offers excellent fit to actual sales activity. We also demonstrate that the model is capable of delivering reasonable sales forecasts based solely on environmental data (e.g., theatrical sales, studio, genre, MPAA ratings, etc.) and actual first‐period sales. Accurate sales forecasts can lead to significant cost savings. In particular, it can improve the retail operations at Blockbuster by determining appropriate order quantities of products, which is critical in effective inventory management (i.e., it can reduce the extent of over‐stocking and under‐stocking). While our model is developed specifically for product sales at Blockbuster, we believe that with context‐dependent modifications, our modeling approach could also provide a reasonable basis for the study of sales for other short‐Life‐Cycle products.