企业在市场需求及预测不确定下的多期定价研究

建立多阶段线性需求一价格模型，运用动态规划思想，针对耐用品市场需求量不确定及耐用品生产厂商市场信息预测不准的问题展开探讨。得到了如果消费者对耐用品价格的预期与耐用品生产厂商对市场的预期不一致，耐用品需求量的波动及厂商掌握信息量的多少将对耐用品生产厂商的最优定价具有影响等结论。并根据现实市场状况，对信息不对称下的耐用品定价模型的经济含义给予分析，结果表明模型对耐用品生产厂商的市场决策具有理论指导意义。     

耐用品市场企业租售策略研究

在耐用品垄断厂商同时生产耐用产品和易耗部件的前提下,建立了耐用品和易耗部件的有关耐用度的两期需求-价格模型.通过模型可以发现存在租赁的耐用品生产者将会在成本最小化的基础上生产社会效用较高的耐用品,而为了追求更多的利润他们会只考虑降低易耗部件的使用寿命,从而验证了Swan的市场独立化的结论时于生产易耗部件的耐用品租赁者是成立的.然而,对于耐用品的销售市场,Swan的市场独立化结论不再成立.耐用品垄断企业会采取计划废弃的策略,在不同程度上生产耐屯用度较低的耐用品及其易耗部件.在易耗部件使用寿命相对很高时,耐用品垄断企业会最大化其计划废弃策略.     

基于消费者类型的耐用品租赁定价研究

基于消费者类型，从效用角度分析了新、旧耐用品对消费者的市场覆盖；讨论了耐用品垄断商在两周期上最优租赁定价策略及相关特征。发现第一期新耐用品的最优租赁定价高于第二期新耐用品的最优租赁定价，但第一期新耐用品的市场覆盖高于第二期新耐用品的市场覆盖；垄断商在第一期对新耐用品的最优租赁定价先随着耐用度的增加而增加，然后随着耐用度的增加而下降，在第二期，新耐用品的最优租赁定价不受耐用度影响。     

我国A股增发定价相对效率的实证分析

本文以我国A股市场2000年1月到2007年12月实施增发的109家上市公司为样本，对我国目前普遍采用的增发定价方式-累计投标询价的定价效率进行综合考察。以增发价格对上市公司内在特质信息和市场状况信息的反映充分程度来考察增发定价的相对价格效率。通过参数假设检验、多元回归分析等方法，检验增发定价的效率机制，找出影响增发定价效率的不利因素，以及增发新股合理定价应充分考虑的因素。     

耐用品及其易耗部件耐用度对双寡头企业均衡产量的影响研究

为了研究耐用品双寡头企业的竞争策略，基于Bulow的耐用度选择模型，建立了存在易耗部件情况下的双寡头耐用品企业两期需求一价格模型；求得了双寡头耐用品企业租赁情况下的两期均衡产量；并运用MATLAB软件对均衡解进行仿真，分析了双寡头耐用品企业自身和对手的耐用品耐用度及易耗部件耐用度对均衡产量影响。     

地票价格形成机制及影响因素分析

为了使地票价格达到合理水平,促进地票交易制度顺利运行,对地票价格的形成机制及影响因素进行了研究。基于地票的产生和使用两个角度对地票价格进行估算,并运用供求关系原理分析了地票的均衡价格与均衡价格区间。分析表明,地票价格是在市场定价与行政定价双重机制作用下形成的。为此,提出建议:政府必须找准其在地票市场中的定位,合理利用经济手段调控地票价格,同时需加强农民在地票交易中的参与。     

双分数Vasicek利率环境下的篮子期权定价

在双分数布朗运动环境下,讨论具有随机利率的欧式几何篮子期权定价问题。假设股票价格遵循双分数布朗运动驱动的随机微分方程,随机利率服从Vasicek模型,预期收益率和波动率均为常数的情况下,利用保险精算方法,推导出双分数布朗运动环境下具有随机利率的欧式几何篮子期权定价公式。     

双边市场视角下创新驿站运行初期的价格策略研究

创新驿站对我国经济的发展和圆力的增强有着重要的意义，但是基于该平台的定价策略的缺失已经阻碍了其进一步的快速发展。本文结合我国创新驿站处于运行初期的特征，运用新型产业组织形式理论——双边市场理论，构建了平台两端市场规模最大化的模型，对我国创新驿站运行初期的定价策略进行了研究。研究结果表明：创新驿站初期收取的价格受到客户加入平台的初始效用、各行业的转化水平、平台吸引双方加入的成本、交叉网络外部性和政府在运行初期投入的影响。对创新驿站定价的研究有利于创新驿站的高效运行，同时为政府制定相关产业政策提供经济学的理论支撑和指导。     

可控环境生产系统建模、仿真与控制研究进展

可控环境生产系统属于复杂系统,目前对这类系统的建模及控制问题没有形成系统的方法与理论,因此在对这类系统进行控制时,缺乏理论依据.本文在回顾过去几十年可控环境生产系统建模与控制的各种方法的基础上,探讨了当前可控环境生产系统在系统建模、仿真及控制中存在的问题,如支持生产可使用的作物生长数学模型信息的不完整性,水肥系统的模型信息的时间尺度问题,环境模型的动态时变特性以及作物、土壤、大气相关变量的作用关系,耦合的多环境因子的控制问题、包括能耗在内的冲突多目标控制问题和考虑模型误差和气候突变等不确定性的鲁棒控制问题等.在此基础上,根据系统的特性:多变量,非线性,多模型(作物模型,土壤模型,大气模型)、模型之间强耦合、大尺度、能量有限、分布参数、冲突多目标等的特点,提出了该系统有效建模、仿真、控制急需解决的瓶颈问题和可能的研究趋势.     

基于GB-CART集成算法的铁路物流需求量预测

为了准确、快速地对铁路物流需求量进行预测,针对现有铁路物流需求量预测模型存在的问题,采用梯度提升算法对分类与回归树算法进行集成,提出一种GB-CART集成算法。以1990~2014年的铁路物流需求量为研究对象,选取预测年份前3年的铁路物流需求量作为模型输入,预测年份铁路物流需求量作为模型输出,采用GBCART集成算法进行仿真实验,并与单一CART、SVR、RBF和LR模型进行比较。结果表明:GB-CART模型的预测效果与单一CART模型相比得到了大幅度提升,且预测精度高于SVR、RBF和LR,验证了所提出模型的有效性及准确性。     

Long-term forecasting with innovation diffusion models: The impact of replacement purchases

The model presented in this paper integrates two distinct components of the demand for durable goods: adoptions and replacements. The adoption of a new product is modeled as an innovation diffusion process, using price and population as exogenous variables. Adopters are expected to eventually replace their old units of the product, with a probability which depends on the age of the owned unit, and other random factors such as overload, style-changes etc. It is shovn that the integration of adoption and replacement demand components in our model yields quality sales forecasts, not only under conditions where detailed data on replacement sales is available, but also when the forecaster's access is limited to total sales data and educated guesses on certain elements of the replacement process.     

Which survey indicators are useful for monitoring consumption? Evidence from European countries

This paper assesses the information content of two survey indicators for consumption developments in the near future for eight European countries in the period 1985–1998. Empirical work on this topic typically focuses on consumer confidence, the perceptions of buyers of consumption goods. This paper examines whether perceptions of sellers of consumption goods, measured by retail trade surveys, may also improve short‐term monitoring of consumption. We find that both consumer confidence and retailer confidence embody valuable information, when analysed in isolation. For France, Italy and Spain we conclude that adding retail confidence does not improve the indicator model once consumer confidence has been included. For the UK the reverse case is obtained. For the remaining four countries we show that combining consumer sentiment and retail trade confidence into a composite indicator leads to optimal results. Our results suggest that incorporating information from retail trade surveys may offer significant benefits for the analysis of short‐term prospects of consumption. Copyright © 2004 John Wiley & Sons, Ltd.     

Forecasting private consumption with Google Trends data

This paper examines the predictive relationship of consumption‐related and news‐related Google Trends data to changes in private consumption in the USA. The results suggest that (1) Google Trends‐augmented models provide additional information about consumption over and above survey‐based consumer sentiment indicators, (2) consumption‐related Google Trends data provide information about pre‐consumption research trends, (3) news‐related Google Trends data provide information about changes in durable goods consumption, and (4) the combination of news and consumption‐related data significantly improves forecasting models. We demonstrate that applying these insights improves forecasts of private consumption growth over forecasts that do not utilize Google Trends data and over forecasts that use Google Trends data, but do not take into account the specific ways in which it informs forecasts.     

基于市场竞争程度的古诺与斯坦克尔伯格博弈对比分析

建立了多厂商古诺博弈和m个先动厂商、n—m个后动厂商的多厂商新坦克尔伯格博弈，分析了先动厂商竞争程度对其均衡产量和利润的影响情况。结果表明，当先动厂商竞争程度较小时，古诺厂商利润小于斯坦克尔伯格先动厂商利润；当先动厂商竞争程度较大时，古诺厂商利润大于斯坦克尔值格先动厂商利润。     

A neural network versus Black–Scholes: a comparison of pricing and hedging performances

An Erratum has been published for this article in Journal of Forecasting 22(6‐7) 2003, 551 The Black–Scholes formula is a well‐known model for pricing and hedging derivative securities. It relies, however, on several highly questionable assumptions. This paper examines whether a neural network (MLP) can be used to find a call option pricing formula better corresponding to market prices and the properties of the underlying asset than the Black–Scholes formula. The neural network method is applied to the out‐of‐sample pricing and delta‐hedging of daily Swedish stock index call options from 1997 to 1999. The relevance of a hedge‐analysis is stressed further in this paper. As benchmarks, the Black–Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap is used to test the statistical significance of the results. Although the neural networks are superior, the results are sometimes insignificant at the 5% level. Copyright © 2003 John Wiley & Sons, Ltd.     

Forecasting quality and information

It is assumed that demand for information that subjectively appears to be relevant for forecasting improves forecasting quality. To study this hypothesis a number of forecasting experiments were conducted. Fifty managers from the housing business, from banking, and from a research institution were asked to forecast interest rates, using a Delphi process. They communicated via a computer system, and, to support their judgements, they had access to a data bank that was stored in the same system. Their communication with the system was automatically recorded. Part of the data collected in these experiments is used to study the existence of a relationship between information activities and forecasting results. A weak positive relationship is found if non-linear functions are tested, where information demand is corrected by those data retrievals that seem to have resulted from an inability to handle the information system. For further research a more general, albeit less informative, Boolean model is suggested.