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.     

Measuring and forecasting regional environmental and economic efficiency in Italy

Environmental and economic efficiency has being receiving growing attention among researchers. In general terms, this concept is related to the capability of the economic systems to employ natural resources efficiently, so as to increase economic and human wealth. This clearly implies that both the economic and ecological aspects of decisions ought to be considered. Bearing this in mind, this article considers economic and ecological performance together, by applying data envelopment analysis and the Malmquist productivity index to investigate the efficiency of the 20 Italian regions from 2004 to 2011. The results reveal that the northern regions have been more efficient than the southern ones, highlighting the strong geographical differences between the two. Furthermore, this article uses the grey system theory to forecast regional, economic and environmental efficiency. The results of the forecasting analysis show that the north–south duality remains strong and will possibly increase since the regions in the south get worse in term of environmental and economic efficiency.     

Distributed ARIMA models for ultra-long time series

Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This paper develops a novel distributed forecasting framework to tackle the challenges of forecasting ultra-long time series using the industry-standard MapReduce framework. The proposed model combination approach retains the local time dependency. It utilizes a straightforward splitting across samples to facilitate distributed forecasting by combining the local estimators of time series models delivered from worker nodes and minimizing a global loss function. Instead of unrealistically assuming the data generating process (DGP) of an ultra-long time series stays invariant, we only make assumptions on the DGP of subseries spanning shorter time periods. We investigate the performance of the proposed approach with AutoRegressive Integrated Moving Average (ARIMA) models using the real data application as well as numerical simulations. Our approach improves forecasting accuracy and computational efficiency in point forecasts and prediction intervals, especially for longer forecast horizons, compared to directly fitting the whole data with ARIMA models. Moreover, we explore some potential factors that may affect the forecasting performance of our approach.     

基于重心法与物流量预测的物流园区选址

在研究物流园区选址理论的基础上,为使物流运输成本最小和物流园区的建设符合长远发展,在对物流园区选址时使用理论中的重心法并建立相应选址数学模型,将数学模型与趋势外推预测法预测的物流园区物流量结合,在定量的基础上计算得到物流园区的选址位置,同时为精确地得到选址位置开发了基于重心法的选址模型软件。最后通过吉木乃县物流园区选址说明该方法的可行性。     

灰色理论在城市总体规划中的应用研究

在编制城市总体规划时收集的资料和信息往往是不完全和不对称的，很多数据需要通过科学的理论去建立合理的模型来推算，本文采用灰色理论建模，以GDP预测为例详细说明了灰色预测模型GM（1，1）的建模过程。该模型对于预测城市总体规划中水文、人口、用地规模、城市化水平、交通运输发展、公共交通以及客货运量等具有普遍指导意义。     

Application of ANFIS to exchange rate forecasting

The need of exchange rate forecasting in order to preventing its disruptive movements has engrossed many policy-makers and economists for many years. The determinants of exchange rate have grown manifold making its behavior complex, nonlinear and volatile so that nonlinear models have better performance for its forecasting. In this study the accuracy of ANFIS as the nonlinear model and ARIMA as the linear models for forecasting 2, 4 and 8 days ahead of daily Iran Rial/∈ and Rial/US$ was compared. Using forecast evaluation criteria we found that nonlinear model outperforms linear model in all three horizons.     

On predictive distributions of public net liabilities

The analysis of the sustainability of public sector finances requires an accounting of all future revenues and all future spending that we would expect, under current tax laws and current entitlements. The classical calculation does not acknowledge the inherent uncertainty of the future economic and demographic developments, so the results can be misleading. Our aim is to produce a more robust summary of the sustainability of the public sector than the one currently available. By taking a forecasting point of view, our formulation takes into account the uncertainty of future productivity, stock and bond markets, and demography. Methodological complications that arise in the stochastic setting are discussed. Estimates of the relative roles of economics and demographics in the uncertainty of public net liabilities are presented.     

企业财务筹资风险的预测与控制

在市场经济条件下企业财务风险加大，特别是筹资风险更大，因此，应加强企业财务风险管理，规避筹资风险，加强预测和控制，以使企业财务康稳健运营。     

Energy and habitat: Designing a sustainable urban ecosystem

As the world's cities grow so new ways of satisfying their demands for food and fuel resources will have to be found, especially in the third world where total fuel demands are expected to expand several fold in one generation. An ecological model for a future complex urban community is presented to illustrate the place of energy in a growing system and how habitat—the physical component—possesses life cycles interdependent with living species. Recycling, biomass fuels, telecommunications, marine living and the propagation of viable organizations are all elements that will enable the globe to sustain future expanding metropolises.     

On the (nonlinear) relationship between exchange rate uncertainty and trade — An investigation of US trade figures in the group of seven

In this paper bilateral models formalizing monthly growth of US imports and exports are employed to investigate the potential of nonlinear relationships linking exchange rate uncertainty and trade growth. Parametric linear and nonlinear as well as semiparametric time series models are evaluated in terms of fitting and ex ante forecasting. The overall impact of exchange rate variations on trade growth is found to be weak. In periods of large exchange rate variations, trade growth forecasts gain from conditioning on volatility. Empirical results support the view that the relationship of interest might be non-linear and, moreover, lacks homogeneity across countries and imports vs. exports. JEL no. C14, C22, F31, F41