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主管:中国科学院
主办:中国优选法统筹法与经济数学研究会
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Table of Content

    20 November 2018, Volume 26 Issue 11 Previous Issue    Next Issue
    Articles
    Is the Gold Safe Haven of the Oil? The Portfolio Return and Volatility Perspectives
    LIU Bing-yue, JI Qiang, FAN Ying
    2018, 26 (11):  1-10.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.001
    Abstract ( 566 )   PDF (2482KB) ( 404 )   Save
    The oil and gold asset risks are analyzed from two perspectives, i.e. asset portfolio extreme returns and their volatilitiesvia daily oil and gold price data from January 2, 2006 to April 14, 2017.
    First, two time series regression models are employed with residuals modelled from 30 GARCH-D processes, rg,t=μg1+δtro,t+εg,t where δt=δ01+δ11·I(ro,t<qo,t0.10)+δ21·I(ro,t<qo,t0.05)+δ31·I(ro,t<qo,t0.01), and rg,t=μg2+δtro,t+εg,t where δt=δ02+δ12·I(t1tt2)+δ22·I(t3tt4), to verify whether the gold is the hedge or safe haven for the oil. The empirical results, and + for the first regression, and  and  for the second regression, show that the gold is neither the hedge nor the safe haven for the oil from the perspectives of portfolio returns.
    Second, the DCC-GARCH model is employed to explore the co-movements between oil and gold, and the empirical results show that there exist the dynamic characteristics in the co-movements between oil and gold, but the co-movements may be weaker in the extreme crisis period. Then, the variance-minimum portfolio is constructed via solving the programming problem,P1:minωt Var(rp,t|Ft-1), s.t.0 ≤ ωt ≤ 1, based on the DCC-GARCH model, the portfolio return series rp,t=ωt* ro,t+(1-ωt*)rg,t are obtained, and then the conditional distribution of the returns rp,t is modeled to measure the risks of unit asset portfolio. From the perspective of portfolio volatilities, the empirical results show that the variance-minimum portfolio of oil and gold can reduce the unit asset risk exposures, especially during the extreme oil market period, i.e. the 2008 global financial crisis and the crash in oil price after 2014.
    Last, this paper is conducive to understanding the safe haven nature of gold assets, and also offers the investors some practical significances to avoid oil market risks viaoil and gold portfolio strategy.
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    Jump Risk, Structural Breaks and Forecasting Crude Oil Futures Volatility
    GONG Xu, LIN Bo-qiang
    2018, 26 (11):  11-21.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.002
    Abstract ( 623 )   PDF (2449KB) ( 348 )   Save
    The accurate forecasting of volatility in the crude oil futures market is an important issue, which has attracted considerable attention from academics, investors, businessmen and governments. This paper mainly aims to test whether the crude oil futures market has obvious jump risk and structural breaks, and investigate whether these two factors can be used to predict the volatility of crude oil futures. Considering jump risk and structural breaks, the HAR-RV-J-SB, HAR-S-RV-J-SB, and PSlev-J-SB models are developed on the basis of HAR-RV, HAR-S-RV, and PSlev models. Then, applying the transaction data of 5-min WTI crude oil futures from the NYMEX-CME, the in-sample and out-of-sample performances of the above models are analyzed. The empirical results show that the crude oil futures market has obvious jump risk and structural breaks. The out-of-sample performances of the HAR-RV-J-SB, HAR-S-RV-J-SB, and PSlev-J-SB models are better than those of the corresponding HAR-RV, HAR-S-RV, and PSlev models, and the results are robust. In particular, similar results can be obtained when jump risk and structural breaks are added to other existing HAR models such as the HAR-C and LHAR-RV models. The above results suggest that considering jump risk and structural breaks can significantly improve the performances of most existing HAR-type models for predicting the volatility of crude oil futures, so these two factors cannot be ignored when proposing new HAR-type models for modeling and forecasting the volatility of crude oil futures. Additionally, the HAR-type models with jump risk and structural breaks developed in this paper perform good predictive powers for the volatility of crude oil futures. The results contribute to the decision of financial traders for portfolio allocation and risk management plan, the industrial production of manufacturers, as well as the relevant policy setting of policymakers.
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    Impacts of Energy Price Fluctuations on Energy-Environment-Economy System in China
    GUO Zheng-quan, ZHANG Xing-ping, ZHENG Yu-hua
    2018, 26 (11):  22-30.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.003
    Abstract ( 585 )   PDF (1157KB) ( 423 )   Save
    Recently China has experienced many changes in energy field including coal and oil price decline, rapid development of renewable energy, levying environmental tax in 2018, and so on. Energy price fluctuation has significant impacts on the economic and environmental system since energy is an important input in production. A computable general equilibrium model is conducted to explore the impacts of energy prices fluctuations on the China's Energy-Economy-Environment system in various scenarios, in which we take into account the fossil energy prices decline,technical progress in clean power, carbon tax policy and the electricity market reform.
    The energy sectors are disaggregated in detail based on the 2012 Input-Output table of China. The petroleum and natural gas extraction sector is disaggregated into two subsectors of petroleum extraction sector and natural gas extractionsector by using the bi-proportional scaling method. The electric power sector is disaggregated into two subsectors of thermal electricity sector and clean electricity sector. Moreover, the clean electricity is disaggregated into four subdivisions including nuclear electricity, hydroelectricity, wind power, solar energy and others. Consequently, the energy sectors in the CGE model include 11 subsectors including the coal, coking, crude oil, petroleum, natural gas, gas, thermal electricity, nuclear electricity, hydroelectricity, wind power and solar energy and others.
    The empirical results show that the decline of fossil energy price in various policy scenarios has positive effects on actual GDP and social welfare. But it will negatively affect the environmental system, since it will increase energy consumption and carbon emissions, and restrain the clean electricity demand. While the carbon tax policy has the opposite effects, a dynamic adjusted carbon tax policy associated with the fossil energy price fluctuation is an effective policy choice to promote clean power development and mitigate fossil energy consumption and carbon emissions. When the technique efficiency of clean renewable energy is improved significantly, the market-oriented tariff mechanism is conducive to the development of clean renewable power. Otherwise, the electricity price mandated by the government is advantageous. The research results have intensive policy implications.
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    Energy Price, Induced Technological Change and China's Environmental Total Factor Productivity
    YANG Fu-xia, XU Jiang-chuan, YANG Mian, SHI Yan
    2018, 26 (11):  31-41.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.004
    Abstract ( 474 )   PDF (1949KB) ( 301 )   Save
    In the past few years, the financial character of energy becomes increasingly obvious, and the volatility of energy price presents more complicated feature.An increasing number of researchers focus on the impact of energy price volatility on economic growth, social welfare and environmental quality, respectively. In practice, the increase of energy price will stimulate producers developing energy-saving technologies, which improve productive efficiency and reduce emissions simultaneously. Given that the environmental total factor productivity index (ETFP index) can capture the comprehensive performance of economic growth and environmental protection, energy price-induced technological change (PITC) and its impact on ETFP change in China are investigated. To this end, the hyperbolic distance function is parameterize in a tanslog functional form, where energy price is incorporated. Based on this distance function, ETFP change is decomposed into the three components including technical efficiency change, energy price-induced and exogenous technological change. The effects of energy price-induced to ETFP change is assessed using the dataset for 30 administrative provinces in China over the period 1995-2015. On average, the rate of energy price-induced technological progress at the national level is 0.22% in the past 20 years, and its contribution to ETFP growth is 4.16%. The spatial-temproal variations for PITC are also observed. In addition, the direction of energy price-induced technological change is capital and energy saving and labor using. For the output bias, it is SO2 emissions reduction during the study period.Finally, some policy recommendations are also put forward on how to enhance China's energy price-induced technological change.
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    Studies on the Dynamic Risk Spillovers for China's Crude Oil Futures
    ZHANG Da-yong, JI Qiang
    2018, 26 (11):  42-49.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.005
    Abstract ( 529 )   PDF (1667KB) ( 478 )   Save
    Risk spillovers among Shanghai crude oil futures, international crude oil prices, Shanghai stock market index and RMB exchange rate are empirically investigated in this paper. Based on constructing of static and dynamic networks for returns and volatilities, the paper allows us to gather first-hand information on how Shanghai crude oil futures interacts with domestic and international markets. Results demonstrate a strong linkage between crude oil futures and international oil market, however, the interaction with financial markets are relatively weak. Meanwhile, Shanghai crude oil futures has shown to be a net information receiver in the system. Variations in the international crude oil markets have significant impact to this newly introduced oil futures.
    As crude oil remains the most important energy resource world wide, its price dynamics have had significant impacts on all aspects of the global economy. One of the most exciting changes of the energy market in recent years is its financialization process. This newly developed phenomenon has generated new features of the market and also brings challenges to both practitioners and academia, where there has emerged a fast growing body of literature studying the energy market under the broad concept of energy finance.
    One most notable event in the global energy market is that China launched its first crude oil futures contract in Shanghai Futures Exchange on 26 March 2018. Its trading volume soon exceeded that of Omen crude oil futures, making it world's third major crude oil futures. While its success has given investors new opportunities, the associated risk control/management has become an increasing concern. It is urgent and important to understand how much crude oil futures interacts with the financial markets and provide first hand empirical evidence.
    A systemic approach proposed by Diebold and Yilmaz (2009, 2014) is adopted and a four-variable vector autoregressive (VAR) model that includes crude oil futures, international benchmark oil prices, stock market index and foreign exchange rate is established. The model is applied to both returns and volatilities of the underlying variables. Using the generalized forecasting error decomposition (FEVD) technique, which helps avoid the problems of ordering in the VAR model, we are able to explicitly identify how much the system is interacted and show directly the spillover (information) effects among variables. To accommodate the possible time-varying feature, a rolling-windows approach is also applied to show the dynamic nature of this system. The modelling strategy has been proved to be an effective way in discovering risk spillovers across different markets.
    The data used in this paper are collected from the WIND finance database. The Shanghai crude oil futures price (INE), WTI (Brent price for robustness check) crude oil prices, RMB/US dollar exchange rate and Shanghai composite index are used for the empirical analysis. All data are from 26/03/2018 to 31/07/2018 in daily frequency. From the descriptive statistics, it is clearly spotted that international oil prices have the highest volatility, which is followed by Shanghai crude oil futures. The pattern reflects the complications of the recent international environment and further motivated us to study risk spillovers between China and the international markets.
    The main findings of this paper can be summarized as the following key points:first, we provide first-hand empirical evidence is provided that the newly launched Shanghai crude oil futures have close links with the international crude oil market, and it is a net information receiver. For example, the net explanatory power of WTI price changes to the Shanghai crude oil futures is 42.1%. It indicates that the crude oil market is still dominated by the international benchmark, whilst China's crude oil futures market has a long way to go to have real impacts on the global market. Second, the interactions between crude oil futures and the financial market are marginal. It is consistent with the existing literature that China has limited impacts on international crude oil market. The new crude oil futures market is yet to develop to become a key financial instrument. Third, clear evidence of time-varying relationships is found.
    Although the sample covers only four months, the global political/economic environment experienced dramatic changes during this particular period. The China-US trade conflict, the middle-east turmoil and general uncertainties in the global economy have made the international crude oil market more volatile and complicated. Our empirical study makes timely and importantcontributions to guiding further actions of risk management and control. This includes helping investors to form optimal investment strategies taking into account risk spillovers across markets, as well as policy makers to understand the dynamic patterns of the crude oil future market.
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    International Transmission of Volatility Among Crude Oil Prices, Economic Uncertainty and the Stock Market
    WANG Qi-Zhen, WANG Yu-Dong
    2018, 26 (11):  50-61.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.006
    Abstract ( 523 )   PDF (5282KB) ( 433 )   Save
    Since the 2008 financial crisis,global stock market volatility, oil prices fluctuations and economic uncertainty have made it important to study risk conduction effects between different markets.After a comprehensive evaluation of shortcomings of existing researches and new improvement methods, a fresh method proposed by Diebold and Yilmaz (2012) is used in this paper to investigate the international transmissionamong crude oil prices, the US economic uncertainty and China's stock market. In this paper, we use the monthly data of crude oil prices, the US economic uncertainty index and China's stock price from January 1986 to December 2016 are used and the static volatility spillover index, dynamic volatility spillover index analysis and nonlinear test are studied respectively.The empirical results show that international oil prices explain most of the fluctuations. The directional spillover index is bidirectional and asymmetric. In the whole sample stage, the fluctuation of the system mainly comes from the impact of other variables, and the spillover effect of the international oil price is the largest. There are nonlinear effects of international oil prices, the US economic uncertainty and volatility spillover of China's stock price to other variables. For the former two variables, the spillover effect of positive variables is larger than that of negative variables. For the latter variable, the result is exactly opposite, i.e. the spillover effect of negative variables is larger than that of positive variables.
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    Multiscale Impacts of Oil Price Fluctuations Driven by the Demand and Supply on the Stock Market
    HUANG Shu-pei, AN Hai-zhong, GAO Xiang-yun, WEN Shao-bo
    2018, 26 (11):  62-73.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.007
    Abstract ( 548 )   PDF (4985KB) ( 258 )   Save
    Previous studies prove that the oil price driven by different factors could exert diverse impacts on the stock market. Moreover, the oil price and stock indices as well as their interaction are characterized by the multiscale features since there are multiple stakeholders associating with different objectives rooting in various time horizons. However, existing studies consider the impacts of different oil prices on the stock market only in the holistic time horizon, which only could offer a limited picture concerning the interaction between the international oil market and the global stock market. Therefore, in this paper the impact of the oil price shocks driven by the oil supply and demand under various time horizons is explored. A combined research framework involving the wavelet transform and the Vector Auto-regress model is proposed. Brent oil price is chosen to represent changes of the international oil market since that 50% of the world oil trade is based it, when the Morgan Stanley Capital International world index is used to reflect the changes of the global stock market. The global oil production and the oil consumption of the OECD (Organization for Economic Co-operation and Development) countries are taken as proxies for the oil supply and demand. All data sets are sampled from February 1998 to December 2015 in monthly frequency. During the empirical processes, the Brent oil price and the world stock index are decomposed into 6 time scales, then the oil price changes driven by the oil supply and demand at each time scale are identified based on their dynamic correlation obtained through the wavelet coherence, and whether different oil prices could influence the global stock market or not and the features of the impact of oil prices in terms of the direction, amplitude, and duration are examined. The results show that both oil price changes driven by the supply and demand could exert significant influence on the stock market in short, medium and long time horizons, but the oil price driven by the demand could not cause the changes of the stock market in ultra-short and ultra-long time scales. Concerning the influence direction of the oil prices, the stock market responds to both types of oil price changes with random direction in short and medium terms, while the stock market has positive changes in the long term. When it comes to the duration of the impact of the oil prices, it is found that the lasting time become longer from 20 months in the short term to over 60 months in the long term with the increase of the time scales. In term of the amplitude of the influences of oil prices, the amplitudes in the short and medium terms are at least 60% stronger than that in the long term. According to above results, it is infered that the influences of the oil price changes transmit to the stock market with different channels. In the short term, the information spilling over between the oil market and the stock market relates two markets. In the medium term, the oil price changes could change the cost or benefit of the production entities and further change their performance in the stock market. In the long term, the oil price changes could redistribute the international wealth and lead more capital into the stock market, which may boom the stock market. Hence, it is necessary for the policy makers and investors to make specific decision refer to different time horizons and stock market shocks caused by different oil price types. In this paper, the multiscale features of the financial time series and the differences caused by different oil price shock types are considered simultaneously. The impacts of the oil price driven by the oil supply and demand are compared in 6 time scales. There are obvious differences and similarities across different time scales, which offer more detailed observation and forward one more step concerning the interaction between the oil market and the stock market.
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    The Analysis of Dependence Relationship between Oil and Stock Prices: Evidence from China and American Industrial Sector Indices
    YU Le-an, ZHA Rui, HE Kai-jian, TANG Ling
    2018, 26 (11):  74-82.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.008
    Abstract ( 561 )   PDF (785KB) ( 327 )   Save
    The oil market and stock market are the important part of modern economy, playing an important role in the economy. The relationship between these markets plays a key role in analyzing the fluctuation of price and risk transmission. The vine copula model is used in this paper to research the dependence relationship is used among the oil price, Chinese stock price and American stock price, and then the dependence relationship to manage risk.The vine copula model is used to model the dependence relationship of the oil price and ten industrial stock prices in China and American respectively,therefore the dependence relationship and dependence structure is estimated. And then oil price and the industrial stock price that has stronger dependence relationship with oil price are selected to construct the portfolio and meantime measure the risk of portfolio.The research findings of this paper show the dependence relationships of oil price, Chinese and American stock prices are different in industry, the dependence relationship between China stock price and oil price is weaker than the dependence relationship between American stock price and oil price.At the same time, dependence relationships are also used to establish two portfolios and estimate their risk,empirical results find that the vine copula model has a better performance in estimating the risk of portfolio which is established by stronger dependence relationship. The research will expand the application of the vine copula model and the risk measurement using vine copula.
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    Market Competition and Price Dispersion-Effect Mechanism and Empirical Evidence
    WANG Xiang-nan
    2018, 26 (11):  83-93.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.009
    Abstract ( 615 )   PDF (1496KB) ( 334 )   Save
    Price dispersion refers to the phenomenon that the same good is sold at different prices at the same time in a given market. Price dispersion violates the "law of one price" and reveals the low efficiency of market running, however, price dispersion is a universal phenomenon. Can market competition reduces price dispersion in an industry (or product)? Few literatures have formally studied this topic in the Chinese market. Firstly, the effect mechanism, results and conditions of competition on price dispersion are demenstrated by mathematical model under the framework of consumer information search theory and firm spatial competition theory, respectively. Then, the data of price and relevant variables of all the car insurance firm for about 300 prefecture-level cities during 2005-2014 are collected, in consideration of the several advantages of Chinese insurance market data. Using the statistical description of related indicators and regression analysis in multiple dimensions, it is mainly found that that even eliminating the product heterogeneity, apparent price dispersion lies in car insurance market, and the mean and median of the coefficient of variation for the price is 0.472 and 0.445, respectively. Market competition can reduce price dispersion among firms in the car insurance market. The number of car insurance firm increases by ten (the market concentration ratio decrease by one standard deviation in the sample) can decrease the standard deviation of car insurance price by about 25 percent (5 percent-6 percent) of its sample's average level, as a whole. The results are robust to static and dynamic panel data econometric models, to several price dispersion indices, to the majority of indices measuring competition, and to several subgroup analysis. The methods of this paper can be referenced to study the relationship between market characteristics and price dispersion in other fields. The policy implication of this paper is that to improve the efficiency of market running, the policy makers should enhance competition among the supply side and reduce search costs of the demand side. Finally, this papers' limitations and further research directions are put forward from the aspects of the research hypothesis, sample characteristics and new topics.
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    Pricing Chinese Inflation-indexed Bonds in the Context of Pension Entering the Capital Market
    LIU Yu-lin, YIN Xing-min, LI Zhi-hui
    2018, 26 (11):  94-104.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.010
    Abstract ( 474 )   PDF (2608KB) ( 223 )   Save
    Along with the deepening of our country population aging degree, the expansion of the pension gap, and the inflation upward pressure, how pension fund invests to realize the value has become an important subject relations of the national development and social stability. Inflation-indexed bonds are viewed as an important tool to resist inflation risk, but there are no inflation-indexed bonds in China. Under this background, a two factor continuous time pricing model characterized by two fundamental variables is developed:the instantaneous inflation rate i(t) and the instantaneous nominal risk-free interest rate r(t), both follow the standard Vasicek model. Such a starting point differences this paper from previous related literature and is motivated by the economic reason that inflation rate and interest rate can both become negative. Based on these assumptions, the theoretical explicit solution of inflation-indexed bonds price under the risk neutral measure is got, and the numerical simulation is used to analyze the resistant effect of inflation indexed bonds on inflation, and the effect of the nominal interest rate, inflation rate, volatility and maturities on inflation-indexed bonds prices, using rolling regression and seemingly unrelated regression(SUR) method to estimate relevant parameters. The data window ranges from January 4, 2002 to January 4, 2016, a total of 5114 daily data. Research shows that the inflation-indexed bonds prices are positively with inflation rate and volatility, negatively with the interest rate, and the effect of volatility is greater than inflation rate and more than the interest rate. If the expected rate of inflation is higher than interest rates, inflation-indexed bonds will be issued at a premium, and the longer the maturities, the higher the prices. This paper makes it possible to price asset under the condition of negative inflation rate and interest rate, provides the possibility for the diversified investments of pension fund to avoid inflation risk and increase the value, and also provides the basis for the countries to promote the innovation of financial derivatives.
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    Pricing and Inventory Decision-making for Fresh Agricultural Products with Strategic Consumers
    TANG Yue-wu, FAN Ti-jun, LIU Sha
    2018, 26 (11):  105-113.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.011
    Abstract ( 660 )   PDF (2015KB) ( 523 )   Save
    Fresh agricultural products such as vegetables, fruits, aquatic products and meat products are damaged and spoiled heavily in supply chains in China. The retail market is a key stage of the loss for factors like unreasonable inventory and pricing decisions. In consideration of the perishability of fresh agricultural products, retailers adopt dynamic pricing strategy to decrease their losses and promote their profits. Strategic customers weigh the benefits of purchasing today against the benefits of waiting and purchasing in the future, which makes the pricing and inventory decision-making of the fresh agriculture product retailers faced much more challenges in the retail market.
    In this paper, one monopolistic fresh agricultural product retailer faced with numerous strategic consumers is considered. The retailer maximizes its profit by adjusting products' prices and inventory level in two sale periods. Strategic consumers decide which period to purchase the fresh agricultural product according to the expectation changes in price and value. A two-period model is developed to study the Decision-making process. The fresh agricultural product's decreasing of freshness is discretized by introducing of value residual rate and a rational expected equilibrium method is used in the model. The single-period scenario is also analyzed.
    Through the model analysis and numerical simulation, a rational expectation equilibrium solution of retailer's optimal pricing and inventory level is obtained. The influence mechanism of fresh agricultural product's value residual rate on the retailer's optimal pricing, optimal inventory level and profit is also discussed. Results show that the retailer's optimal price and the inventory level both increase with the increment of the value residual rate in the single-period model. Moreover, there is a threshold in the variation trend of the retailer's optimal second-period price with the value residual rate in the two-period model.
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    Combined Contract Model of Three-echelon Supply Chain Based on Risk Aversion of Retailer under Yield and Demand Uncertainties Condition
    ZHU Bao-lin, CUI Shi-xu, JI Shou-feng, QIU Ruo-zhen
    2018, 26 (11):  114-123.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.012
    Abstract ( 545 )   PDF (1665KB) ( 435 )   Save
    The development of modern science and technology improves efficiency and shortens product cycle. At the same time, however it brings about the uncertainty and increased risk of supply chain. In recent years, researches on the uncertainty of supply chain output and demand are gradually concerned by scholars. But the researches were conducted usually under the assumption of the neutrality of a risk without consideration of the influence of risk preference on decision makers. In reality, decision makers usually show the characteristics of risk preference. In the paper, a three-echelon supply chain system is discussed, which consists of single supplier, manufacturer and retailer with risk aversion under yield and demand uncertain conditions. The optimal decision models of supply chain system are established under the mechanism of centralization and decentralization. The combined contract is applied to coordinate the three-echelon supply chain system mentioned above. Contract mechanism is the common method used in supply chain coordination. A risk-sharing contract and a GL contract are introduced to coordinate supply chain against the deficiencies brought about by the use of single contract to coordinate the three-echelon supply chain. The GL contract is the improved profit-sharing contract, including both gains and losses of supply chain members. The contract mechanism is described as follows:①the GL contract is applied between manufacturers and retailers, with a parameter(β,γ).β is manufacturers' profit sharing proportion to retailers, and γis sharing proportion of manufacturers' losses to retailers. The profit proportion of retailers is (1-β), and the loss proportion is (1-γ). ②the risk-sharing contract is applied between suppliers and manufacturers. The following conclusions are achieved by the analysis of the number example. First, the expected profit of supply chain decreases with the increase of demand uncertainty(λ). Second, the expected profit of supply chain increases with the increase of random output factor(σ)of manufacturers and suppliers. Finally, the expected profit of retailers decreases with the increase of degree of risk aversion. The order quality of retailers changes with the increase of risk aversion. The validity of models and contract coordination is illustrated by a numerical example.
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    Framework Design of Civil Aircraft Fault Intelligent Diagnosis Network based on Reliability gene Pool
    FANG Zhi-geng, WANG Huan, DONG Wen-jie, CAO Ying-sai
    2018, 26 (11):  124-131.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.013
    Abstract ( 480 )   PDF (2229KB) ( 293 )   Save
    As a typical complex product, the large civil aircraft is a comprehensive embodiment of technology for industrialized countries. Fault diagnosis is a key factor which can determinate the business success of its operation. However, most of the conventional fault diagnosis models for such products are driven by data, ignoring the internal structure and operational logic relationship of large civil aircraft, which can adversely affect the accuracy and intelligence of fault diagnosis. Actually, considering the operation state of the whole equipment and subsystems, the state analysis and intelligent fault diagnosis can improve the accuracy and intelligence of fault diagnosis.
    To solve this problem, the objective of this paper is to give a deep insight into the large civil aircraft system's physical structure, reliability diagram and operation logic relationship and construct the reliability gene bank of large civil aircraft for the whole life cycle on the basis of mathematics, computer and big data technology. The mechanism of heredity, renewal and evolution of the reliability gene is also analyzed. Then, effective fault diagnosis algorithms are designed for some common failure modes of large civil aircraft, based on which the whole civil aircraft fault intelligent diagnosis network framework is constructed. At last, the practicability and effectiveness of the proposed framework is demonstrated through a case study.
    The network framework of intelligent diagnosis for civil aircraft fault is put forward by building the reliability gene bank. It is a new breakthrough in the theory of civil aircraft fault diagnosis. The follow-up study will focus on the practical application of the network framework, constantly improve and refine its applicability.
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    Decision-making Analysis of Production Model for Crowdsourcing Supply Chain with Hybrid On/Off Line Customized Design
    LI Ji-zi, ZHANG Nian, LIU Chun-ling
    2018, 26 (11):  132-144.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.014
    Abstract ( 483 )   PDF (3659KB) ( 437 )   Save
    Innovation is becoming a top priority for many firms with the aim to catering for customers' preferences. Hence, more and more firms are exploring new ways to improve their innovation competencies. The ‘crowdsourcing online design’ mode has been viewed as an effective and efficient tool to aid firms to enhance their strength in designing innovation, and it is a belief that this kind of online initiative can be supplementary to offline design and sharp the edge of firms' competition. However, to our best knowledge, the problem of how to seamlessly align upstream online crowdsourcing design with downstream production, in the scenario of supply chain, is still unaddressed. To solve this problem, a crowdsourcing supply chain consists of an offline designing firm, multiple online designers, a supplier and a manufacturer is studied to maximize the overall profit of the hybrid on/offline system, using mixed integer nonlinear programming (MINLP) approach.
    The main work in this paper includes four parts. First, a stylized framework in which crowdsourcing supply chain is driven by order due-date and scheduled on regular time and overtime in the presence of Engineering to order (ETO) is proposed. Second, in this framework, a supply chain production model with ETO is formulated by taking into account that characteristics of offline customized design, and the determinants are identified is extended while implementing offline design/production system. Third, the above model to the hybrid on/offline model of crowdsourcing supply chain, and the impact of due-date and crowdsourcees on production operation is analyzed, and the switching conditions are devised for on/offline design. Finally, the particle swarm algorithm is employed to solve the above models. Through the numerical study the influence of design allocation between, online and offline, regular time and overtime, on the crowdsourcing supply chain profit, are explored respectively. Further, a sensitivity analysis is conducted to identify the critical parameters for positing and controlling the potential risks.
    The results show that, when the upcoming order is in a small quantity, through hybrid on/offline customized design does not obviously and significantly reduce cost of crowdsourcing supply chain, whereas the advantage of cost-saving and risk-mitigating in crowdsourcing supply chain can be obtained with the increase in the number of order. Moreover, it also reveals that for online design order, it had better arrange at the beginning and end of periods. Meanwhile, online design orders should be avoided to allocate to produce at the regular time once scheduled for offline design, instead, it is more profitable to produce at the overtime. In addition, it is interesting to find that, by giving more reward to crowdsourcees, there is little effect on the change in entire supply chain cost, on the contrary, the incentives encourage more crowdsources to engage in crowdsourcing online design.
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    Application of GPRs Network Division Optimization Theorem in the Flow Process Network
    KONG Feng, ZHANG Rui, WU Tian
    2018, 26 (11):  145-152.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.015
    Abstract ( 449 )   PDF (3175KB) ( 237 )   Save
    In this paper, the critical activities decomposition paradox and the total floats paradox in the traditional algorithm of GPRs multi-time difference network are found. The critical activities decomposition paradox is that the critical activity which is decomposed into two activities with FTS=0 logical relation will lead to the total duration shortened. The total floats paradox is that activities which are decomposed will increase the total float. The reasons of these two paradoxes and propose critical activities decomposition optimization theorem and total float decomposition optimization theorem are analyzed. The new methods make the total project duration and the distribution of total time of the network optimized. They can also provide more scientific and sufficient conditions for project WBS and resource optimization. In addition the division optimization theorem is combined with the flow process network in order to provide a scientific optimization method for the construction section in the flow process.
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    Design and Optimization of the Complex Forming Equipment's Product Architecture with Service-oriented
    YAN Jian-wen, YUAN Cheng-ming, ZHANG Qiang, FAN Yu
    2018, 26 (11):  153-165.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.016
    Abstract ( 488 )   PDF (4767KB) ( 202 )   Save
    Complex forming equipment is the foundation of high-end manufacturing industry, and it is an important guarantee for the industrial upgrading and the technological progress. The rapid developing information technologies, such as Internet and Big data, have become an indispensable part of the equipment manufacturing industry. The design and optimization of the complex forming equipment's product architecture with service-oriented is studied. Through the analysis of characteristics of design architectures of complex forming equipment in the Internet and Big data era, a framework of product architecture is established as the corresponding relationships between the requirement domain, the function domain and the architecture domain. Thus, the product-service modeling methods and integration framework of complex forming equipment based on QFD and DSM are proposed. The modularization optimization method is also developed with the consideration of the operation costs.
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    Decision Models for Risk-averse V2G Reserve Considering Stochastic Demand and Revenue Sharing
    ZHANG Fan-yong, HUANG Shou-jun, YANG Jun
    2018, 26 (11):  166-175.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.017
    Abstract ( 512 )   PDF (1468KB) ( 254 )   Save
    The decision models for risk-averse V2G reserve considering stochastic demand and revenue sharing under the CVaR measurement criterion are proposed,and the analytical solutions of channel members' optimal decision behavior in the integrated and decentralized decision are derived. On this basis,the equilibrium strategies when the random demand variable follows the uniform distribution are comparatively analyzed. The research shows that the optimal V2G reserve factor under integrated decision is positively correlated with the channel overall risk aversion,while the relevance between equilibrium selling price of V2G and channel overall risk aversion is uncertain,and this uncertainty is affected by the distribution function of the stochastic demand variable. The optimal V2G reserve factor under decentralized decision is only related to the grid company' risk aversion,while the equilibrium selling price of V2G is affected by the common influence of risk aversion,purchasing price of grid company,and electric vehicle user' revenue sharing coefficient. The optimal V2G reserve revenue sharing coefficient of electric vehicle user is positively correlated with his risk aversion,but negatively related to the risk aversion of grid company. The results of numerical simulation indicate that revenue-sharing contract in the vast majority of cases does not perfectly coordinate decentralized decision behavior in V2G reserve channel.
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    Research on Overseas Channels and Consumer Groups of Featured Brands: Based on Dual Perspective of Customer Experience and Oral Spreading
    ZHOU Zhong, XIONG Yan, ZHONG Yong
    2018, 26 (11):  176-185.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.018
    Abstract ( 569 )   PDF (2393KB) ( 326 )   Save
    Featured brands relies on high quality customer experience and efficient oral spreading when entering overseas markets. With limited market resources and budget, how to choose the channels and target customers is particularly critical. Based on the consumer behavior theory and the theory of word of mouth, a consumer network characterized by the small-world of particular overseas region is constructed. The consumer intention of purchasing featured brands is further discussed by considering customer experience of sales channels. The impact factors and process mechanisms of the brands' oral spreading between consumer individuals are also analyzed. Using the modeling and simulation approach with the Matlab software, the results of the designed multiple scenarios analysis show that, the higher degree of difference between consumer groups, the obviously slower speed of the featured brands' oral spreading, and the markedly less amount of potential consumers. The performance of varied sales channels show differentiation trends on the brands reputation spreading and the attraction to the target consumers. In response, targeted marketing strategies are suggested to be developed to attract the tight connected consumer groups with influence power. In overseas market with significant difference between consumer groups, the direct traditional channel can help enhance their purchase intention rapidly. But to regional markets of large-scale consumers living discretely while a minority of groups have dominated influence, it helps develop more potential consumers that having high purchase intention by taking the advantage of network channels on consumer coverage. This research can provide theoretical support and practice guiding on consumer groups selection and sales channel design for featured brands entering overseas markets, specially under the complex social background of overseas multiple consumer groups.
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    A Cost-sensitive Semi-supervised Ensemble Model for Customer Targeting
    XIAO Jin, LIU Xiao-xiao, XIE Ling, LIU Dun-hu, HUANG Jing
    2018, 26 (11):  186-196.  doi: 10.16381/j.cnki.issn1003-207x.2018.11.019
    Abstract ( 515 )   PDF (2940KB) ( 288 )   Save
    With the advent of the era of big data, more and more customer data are grasped by the enterprises and their marketing concept has changed from "product-centric" to "customer-centric". The enterprises pay more attention to customer relationship management (CRM) than before. In order to avoid the disadvantage of conventional marketing means, such as low efficiency, high cost and so on, many enterprises have started to use database marketing to improve the effectiveness and pertinence of their marketing activities. As one of the most important issues in database marketing, the customer targeting modeling is used to identify target customers from potential customers who are the most likely respond to the marketing means, thus helping the enterprise work out marketing strategies. It takes advantage of various customer information, including identity information, consumer preference, historical purchase records and so on to build the customer targeting model, and then predicts which customers are more likely to respond to marketing means.Actually, the customer targeting modeling is a classification problem.In a real customer targeting modeling, a small number of labeled samples and a large number of unlabeled ones can always be obtained. Most of the existing studies have used the paradigm of supervised learning, which merely built model with the labeled samples, and it's difficult to achieve satisfactory results. In order to solve this problem, semi-supervised learning (SSL) technology is introdueed, and it is combined with cost sensitive learning (CSL) and random subspace (RSS) which is one of the multiple classifiers ensemble methods, and the cost-sensitive semi-supervised ensemble model (CSSE) is proposed. This model uses the cost-sensitive SVM to handle the imbalanced class distribution in customer targeting modeling. Meanwhile, it can build a model with both labeled and unlabeled samples. Further, RSS is adopted to train a series of base classifiers and the final classification results are obtained by integration. The experiment is carried out in a customer targeting database of a car insurance company from CoIL2000 prediction competition, and the results show that CSSE model has better customer targeting performance compared with two supervised ensemble models, two single semi-supervised models, and two semi-supervised ensemble models.Apart from the AUC value which is frequently used, hit rate, Lorenz curve and lift chart are also used to evaluate the customer targeting performance more intuitively. It provides a good idea to further research, that is, more targeted and more reasonable evaluate indicators shouold be used to improve the practicality of the model in the research field. In CRM, there are many other classification problems that are similar to customer targeting modeling, such as customer churn prediction, customer credit scoring. Therefore, the proposed model can also be applied to these fields, and can achieve satisfactory classification performance.
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