价值工程在建筑工程施工招标评标中的应用

本文介绍了评标中常用的几种分析评价方法 ,重点分析了价值工程在建筑业中的使用现状和不足 ,引进了评价指标的非线性标准化函数 ,建立了非线性价值工程评价模型。     

Who poisons the pool? Time-varying asymmetric and nonlinear causal inference between low-risk and high-risk bonds markets

Corporate bond markets enable the efficient allocation of capital among competing firms, as well as an extensive degree of disintermediation. While the role of the junk bond market in financing leveraged buyouts, “fallen angels,” start-ups, small firms, and sovereign governments is known, little is known about interactions between low-risk (AAA) bonds markets and high-risk (CCC and below) bonds markets. In this study, we used a sample of daily data spanning 20 years to investigate the dynamic link in first and second moments between low-risk and high-risk bonds during calm and turbulent periods in the U.S. financial markets. Using asymmetric and nonlinear causality tests, as well as the extended DCC-GJR-GARCH model, we found evidence of an asymmetric and nonlinear unidirectional causal link from high-risk to low-risk bonds markets, which intensifies during bear markets. There is a bidirectional volatility and shock transmission only during normal bond market conditions. The high-risk bonds market induces more destabilizing effects in the corporate bond market than the low-risk bonds market. The time-varying, highly persistent, and negative correlation during normal market conditions provides the opportunity for combining low-risk and high-risk bonds to diversify a portfolio.     

Hierarchical GARCH

There is strong empirical evidence that the GARCH estimates obtained from panels of financial time series cluster. In order to capture this empirical regularity, this paper introduces the Hierarchical GARCH (HG) model. The HG is a nonlinear panel specification in which the coefficients of each series are modeled as a function of observed series characteristic and an unobserved random effect. A joint panel estimation strategy is proposed to carry out inference for the model. A simulation study shows that when there is a strong degree of coefficient clustering panel estimation leads to substantial accuracy gains in comparison to estimating each GARCH individually. The HG is applied to a panel of U.S. financial institutions in the 2007–2009 crisis, using firm size and leverage as characteristics. Results show evidence of coefficient clustering and that the characteristics capture a significant portion of cross sectional heterogeneity. An out-of-sample volatility forecasting application shows that when the sample size is modest coefficient estimates based on the panel estimation approach perform better than the ones based on individual estimation.     

Asymmetric effects of exchange rate changes on the Malaysia-China commodity trade

Previous research that considered the response of the trade balance between Malaysia and China to exchange rate changes used a linear model and did not find any significant long-run link. Suspecting that the results suffer from aggregation bias as well as ignoring nonlinear adjustment of the exchange rate, we consider the trade balance of 59 industries that trade between the two countries and use a nonlinear ARDL model to show that almost a third of the industries are affected by ringgit depreciation against the yuan, in an asymmetric manner. The largest industry, which accounts for more than 25% of the trade, is found to benefit from ringgit depreciation while not being hurt by appreciation. In total, 15 industries that account for 40% of the trade enjoy this property.     

互联网普及如何影响中国家庭债务杠杆率

互联网技术的普及推动了数字金融、移动支付、金融科技等一系列金融创新的发展，基于此，文章利用中国家庭金融调查（CHFS）2017年的数据，细致探讨了互联网这一最基础的技术普及与家庭债务杠杆率的关系及内在作用机理。结果发现：（1）互联网的使用将显著推高家庭的债务杠杆率。在使用工具变量回归、处理效应模型与PSM匹配纠正内生性问题后，这一结论依然成立。（2）除通过促进电子支付、增加金融可及性外，互联网的使用还将通过提高信息搜寻与增强社会互动这两种渠道而带来家庭债务杠杆率的累积。（3）互联网的使用与家庭债务杠杆率间存在显著的非线性关系，即意味着只有当对互联网的使用达到一定程度后，其才会带来家庭债务风险。（4）对于受需求型信贷约束、低收入与中老年群体，互联网使用对这类群体债务杠杆率的影响程度更大。进一步地，除网络平台借款将推高家庭债务杠杆外，文章还考察了互联网的使用对民间借贷的影响，并发现其对家庭非正规借贷杠杆率的影响更为突出。而对于金融知识欠缺的家庭，其债务杠杆率更高。这说明，在鼓励使用互联网推动家庭参与金融市场的同时，也要注意防范过度使用互联网所带来的金融风险，而其中一种有效的途径便是普及金融知识教育，提高居民的金融素养。     

The master equation approach to nonlinear economics

A concept for modelling nonlinear economic dynamics is presented and exemplified by a concrete model. Generally, a configuration of macro-economic variables is considered whose probabilistic evolution is coupled to the decision making of agents and is described by a master equation. The transition rates in the master equation are modelled in terms of utility measures of the agents. Nonlinear dynamic meanvalue equations can be derived from the master equation. The concrete model describes firms producing substitutable durable commodities. They compete with respect to the quality of their products and a positive feedback between quality enhancement and customer's reaction to quality is assumed. The case of two competing firms is treated explicitely. It is shown that beyond a critical value of a “competitivity parameter” a homogenous maret will develop into an inhomogenous one with a winner and a loser firm.     

Comments on “An analysis of the international tourism demand in Spain” by P. González and P. Moral

Although the state-space, unobserved component approach to forecasting has many advantages, it must be applied carefully in practice and should not be used in an uncritical, ‘black box’ fashion. In particular, such an approach to the modelling and forecasting of Spanish tourism data is inappropriate and leads to misleading conclusions, particularly in relation to the uncritical selection and use of explanatory regression variables.     

非平稳条件下的市场可预测性问题研究

本文研究了非平稳对线性、非线性可预测性检验的影响,从非平稳角度解释了"非线性可预测性难题"。在此基础上对传统的Box-Pierce-Ljung线性检验法和广义谱密度非线性检验法进行了修正,并运用严格的理论证明和蒙特卡罗模拟证实了它们在大样本和有限样本情况下的非平稳稳健性。最后,运用修正的检验方法对我国第一只股指期货标的沪深300统一指数的可预测性进行了分析。本文结论对于我国金融市场实证研究具有重要的指导意义,也反映了我国新兴金融市场的独有特性以及成熟统计方法在我国的实用性。     

The kernel trick for nonlinear factor modeling

Factor modeling is a powerful statistical technique that permits common dynamics to be captured in a large panel of data with a few latent variables, or factors, thus alleviating the curse of dimensionality. Despite its popularity and widespread use for various applications ranging from genomics to finance, this methodology has predominantly remained linear. This study estimates factors nonlinearly through the kernel method, which allows for flexible nonlinearities while still avoiding the curse of dimensionality. We focus on factor-augmented forecasting of a single time series in a high-dimensional setting, known as diffusion index forecasting in macroeconomics literature. Our main contribution is twofold. First, we show that the proposed estimator is consistent and it nests the linear principal component analysis estimator as well as some nonlinear estimators introduced in the literature as specific examples. Second, our empirical application to a classical macroeconomic dataset demonstrates that this approach can offer substantial advantages over mainstream methods.     

基于非线性规划的企业销售利润优化模型

针对企业销售利润最优化问题,分析了相关概念和相互之间的关系,以企业销售利润为目标函数,建立了非线性规划模型,说明了约束条件的构成,最后以实例对提出的模型进行了详细说明,有助于企业销售管理决策,具有一定的应用价值。