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Some studies on developed economies have revealed that the impacts of oil price shocks have decreased while conclusions about China remain occluded. We investigate the changing effects of oil price shocks on China's macroeconomy and discuss the causes. A time-varying parameter vector autoregressive (VAR) model reveals that impacts of oil price shocks on China's economy have shown a downward trend since 1997. The responses of the real output are much greater and last longer than those of inflation. Then a new Keynesian dynamic stochastic general equilibrium model is developed to synthetically explore the causes. The results indicate that decreasing oil intensity and monopoly power reduce the effects of oil price shocks, while increasing capital intensity in production amplifies them. Other factors-such as changing price stickiness, deregulation of refined oil prices, and shifts in monetary policy targets-have limited effects on the relationship between oil price shocks and China's macroeconomy. Keywords: Oil price shocks, Macroeconomy, China, Dynamic stochastic general equilibrium model, Time varying, 1. INTRODUCTION The effects of oil price shocks on the macroeconomy have attracted much attention. Many oil-importing countries see a correlation between increases in oil prices and subsequent economic downturns [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2010.05.017 Byline: Yingxue Zhao (a)(b), Shouyang Wang (a), T.C.E. Cheng (c), Xiaoqi Yang (d), Zhimin Huang (e)(f) Keywords: Supply chain management; Cooperative game; Option contract; Negotiating power; Channel coordination Abstract: Manufacturer-retailer supply chains commonly adopt a wholesale price mechanism. This mechanism, however, has often led manufacturers and retailers to situations of conflicts of interest. For example, due to uncertain market demand, retailers prefer to order flexibly from manufacturers so as to avoid incurring inventory costs and to be able to respond flexibly to market changes. Manufacturers, on the other hand, prefer retailers to place full orders as early as possible so that they can hedge against the risks of over- and under-production. Such conflicts between retailers and manufacturers can result in an inefficient supply chain. Motivated by this problem, we take a cooperative game approach in this paper to consider the coordination issue in a manufacturer-retailer supply chain using option contracts. Using the wholesale price mechanism as a benchmark, we develop an option contract model. Our study demonstrates that, compared with the benchmark based on the wholesale price mechanism, option contracts can coordinate the supply chain and achieve Pareto-improvement. We also discuss scenarios in which option contracts are selected according to individual supply chain members' risk preferences and negotiating powers. Author Affiliation: (a) Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China (b) School of International Trade and Economics, University of International Business and Economics, Beijing 100029, PR China (c) Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China (d) Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China (e) School of Business, Adelphi University, Garden City, NY 11530, USA (f) School of Business, Renmin University of China, Beijing 100872, PR China Article History: Received 27 May 2009; Accepted 13 May 2010

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2009.12.012 Byline: Guowei Hua (a)(c), Shouyang Wang (b), T.C.E. Cheng (c) Keywords: Supply chain management; Dual channel; Internet/direct marketing; E-commerce; Game theory Abstract: Manufacturers today are increasingly adopting a dual channel to sell their products, i.e., the traditional retail channel and an online direct channel. Empirical studies have shown that service quality (we focus on the delivery lead time of the direct channel) even goes beyond product price as one of the major factors influencing consumer acceptance of the direct channel. Delivery lead time has significant effects on demand, profit, and pricing strategy. However, there is scant literature addressing the decision on the promised delivery lead time of a direct channel and its impact on the manufacturer's and retailer's pricing decisions. To fill this gap, we examine the optimal decisions of delivery lead time and prices in a centralized and a decentralized dual-channel supply chain using the two-stage optimization technique and Stackelberg game, and analyze the impacts of delivery lead time and customer acceptance of a direct channel on the manufacturer's and retailer's pricing behaviours. We analytically show that delivery lead time strongly influences the manufacturer's and the retailer's pricing strategies and profits. Our numerical studies reveal that the difference between the demand transfer ratios in the two channels with respect to delivery lead time and direct sale price, customer acceptance of the direct channel, and product type have great effects on the lead time and pricing decisions. Author Affiliation: (a) School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China (b) Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China (c) Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China Article History: Received 17 September 2008; Accepted 9 December 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2009.03.009 Byline: Mei Yu (a)(b), Satoru Takahashi (b), Hiroshi Inoue (b), Shouyang Wang (c) Keywords: Portfolio optimization; Linear programming; Absolute deviation; Dynamic programming Abstract: In this paper, we present a new multiperiod portfolio selection with maximum absolute deviation model. The investor is assumed to seek an investment strategy to maximize his/her terminal wealth and minimize the risk. One typical feature is that the absolute deviation is employed as risk measure instead of classical mean variance method. Furthermore, risk control is considered in every period for the new model. An analytical optimal strategy is obtained in a closed form via dynamic programming method. Algorithm with some examples is also presented to illustrate the application of this model. Author Affiliation: (a) School of Finance and Banking, University of International Business and Economics, 100029 Beijing, China (b) School of Management, Tokyo University of Science, Kuki-shi, Saitama 346-8512, Japan (c) Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100080, China Article History: Received 23 June 2007; Accepted 3 March 2009 Article Note: (footnote) [star] This work is supported by Tokyo University of Science and completed when the first author visit Tokyo University of Science. This work is also supported by the 211 project in the University of International Business and Economics.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2007.11.025 Byline: Lean Yu (a)(b), Shouyang Wang (a), Kin Keung Lai (b) Keywords: Multicriteria decision analysis; Fuzzy group decision making; Intelligent agent; Credit scoring; Artificial intelligence Abstract: Credit risk analysis is an active research area in financial risk management and credit scoring is one of the key analytical techniques in credit risk evaluation. In this study, a novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation. In this proposed model, some artificial intelligent techniques, which are used as intelligent agents, are first used to analyze and evaluate the risk levels of credit applicants over a set of pre-defined criteria. Then these evaluation results, generated by different intelligent agents, are fuzzified into some fuzzy opinions on credit risk level of applicants. Finally, these fuzzification opinions are aggregated into a group consensus and meantime the fuzzy aggregated consensus is defuzzified into a crisp aggregated value to support final decision for decision-makers of credit-granting institutions. For illustration and verification purposes, a simple numerical example and three real-world credit application approval datasets are presented. Author Affiliation: (a) Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China (b) Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jeconom.2008.12.013 Byline: Yongmiao Hong (a)(b), Yanhui Liu (c), Shouyang Wang (c) Abstract: Controlling and monitoring extreme downside market risk are important for financial risk management and portfolio/investment diversification. In this paper, we introduce a new concept of Granger causality in risk and propose a class of kernel-based tests to detect extreme downside risk spillover between financial markets, where risk is measured by the left tail of the distribution or equivalently by the Value at Risk (VaR). The proposed tests have a convenient asymptotic standard normal distribution under the null hypothesis of no Granger causality in risk. They check a large number of lags and thus can detect risk spillover that occurs with a time lag or that has weak spillover at each lag but carries over a very long distributional lag. Usually, tests using a large number of lags may have low power against alternatives of practical importance, due to the loss of a large number of degrees of freedom. Such power loss is fortunately alleviated for our tests because our kernel approach naturally discounts higher order lags, which is consistent with the stylized fact that today's financial markets are often more influenced by the recent events than the remote past events. A simulation study shows that the proposed tests have reasonable size and power against a variety of empirically plausible alternatives in finite samples, including the spillover from the dynamics in mean, variance, skewness and kurtosis respectively. In particular, nonuniform weighting delivers better power than uniform weighting and a Granger-type regression procedure. The proposed tests are useful in investigating large comovements between financial markets such as financial contagions. An application to the Eurodollar and Japanese Yen highlights the merits of our approach. Author Affiliation: (a) Department of Economics & Department of Statistical Science, Cornell University, 424 Uris Hall, Ithaca, NY 14853-7601, USA (b) Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian, China (c) Academy of Mathematics and System Science, Chinese Academy of Sciences, Beijing, China

This paper develops economic production quantity (EPQ)-based models with planned backorders to evaluate the impact of the postponement strategy on a manufacturer in a supply chain. We derive the optimal total average costs per unit time for producing and keeping n end-products in a postponement system and a non-postponement system, respectively. By comparing the optimal total average costs of the two systems, we evaluate the impact of postponement on the manufacturer under four circumstances. Our results show that postponement strategy can give a lower total average cost under certain circumstances. We also find that the key factors in postponement decisions are the variance of the machine utilization rates and the variance of the backorder costs. Keywords: Postponement; Economic production quantity (EPQ); Backorder; Supply chain management (SCM), 1. Introduction In order to meet the needs of increasingly demanding customers for more diverse products, many companies have reconfigured their supply chain structures to accommodate mass customization. The new [...]

Data preparation is an important and critical step in neural network modeling for complex data analysis and it has a huge impact on the success of a wide variety of complex data analysis tasks, such as data mining and knowledge discovery. Although data preparation in neural network data analysis is important, some existing literature about the neural network data preparation are scattered, and there is no systematic study about data preparation for neural network data analysis. In this study, we first propose an integrated data preparation scheme as a systematic study for neural network data analysis. In the integrated scheme, a survey of data preparation, focusing on problems with the data and corresponding processing techniques, is then provided. Meantime, some intelligent data preparation solution to some important issues and dilemmas with the integrated scheme are discussed in detail. Subsequently, a cost-benefit analysis framework for this integrated scheme is presented to analyze the effect of data preparation on complex data analysis. Finally, a typical example of complex data analysis from the financial domain is provided in order to show the application of data preparation techniques and to demonstrate the impact of data preparation on complex data analysis. Index Terms--Data preparation, neural networks, complex data analysis, cost-benefit analysis.

A stochastic linear goal programming model for multistage portfolio management is proposed. The model takes into account both the investment goal and risk control at each stage. A scenario generation method is proposed that acts as the basis of the portfolio management model. In particular, by matching the moments and fitting the descriptive features of the asset returns, a linear programming model is used to generate the single-stage scenarios. Scenarios for multistage portfolio management are generated by incorporating this single-stage method with the time-series model for the asset returns. Meanwhile, no arbitrage opportunity exists in the proposed method. A real case is solved via the goal programming model and the scenario generation approach which demonstrates the effectiveness of the model. We also comment on some practical issues of the approach., 1. Introduction In a long-term investment, an investor usually adjusts his/her portfolio in the light of prevailing circumstances according to his/her risk preference. This is referred to as a dynamic [...]

Multiple attribute decision modeling is proposed for determining the most preferred bid markup in a competitive bidding situation. In this note, a previously proposed model is extended by introducing two new bidding criteria, called the conditional positive profit and the expected positive profit ratios, respectively. Three two-criteria decision models and a single criteria decision models using the newly introduced criteria are proposed to determine the bidder's most preferred markup level. The simple additive weighting method and the weighting product method are used to solve these models. Two numerical examples from are used to illustrate the different rankings that are obtained when different models and solution methods are used. The results demonstrate that the bidder's preference over the set of bidding criteria has a significant effect on the selection of the bid markup. Index Terms--Bid markup selection, competitive bidding, multiple criteria decision making.

The past decades have witnessed tremendous research activity in financial engineering in the industrial engineering, operations research, and management science communities. Financial engineering addresses models and analysis of financial markets [...]

The portfolio selection problem is considered in the context of transaction costs in mutual funds portfolio selection.