IMPACT OF POS DATA SHARING ON SUPPLY CHAIN MANAGEMENT: AN EXPERIMENTAL STUDY

We examine the impact of point of sale (POS) data sharing on ordering decisions in a multi‐echelon supply chain. In particular, we focus on how exposure to POS data may help reduce the “bullwhip effect,” the tendency of orders to increase in variability as one moves up a supply chain. Theoretical studies have shown that exposure to POS data can lead to a reduction in the bullwhip effect when suppliers have no prior knowledge of the demand distribution. The benefit of sharing POS data in stable industries, where the demand distribution is commonly known, is less clear. We study this phenomenon from a behavioral perspective in the context of a simple, serial, supply chain subject to information lags and stochastic demand. We find, using a controlled simulation experiment, that sharing POS information does help reduce some components of the bullwhip effect in a stable demand setting, namely the order oscillation of upstream members. We offer one possible explanation for this improvement by examining the relationship between order decisions and demand line information.     

Decision Making and Cognition in Multi‐Echelon Supply Chains: An Experimental Study

Supply chain performance often depends on the individual decisions of channel members. Even when individuals have access to relevant information, order variation tends to increase when moving up the supply chain, a phenomenon known as the bullwhip effect. While prior research has investigated several structural/environmental factors which can mitigate the bullwhip effect, the underlying behavioral factors contributing to it are an open question. Using a production and distribution decision‐making simulation representing a four‐stage serial supply chain, we find that the cognitive profile of decision makers contributes to the bullwhip effect. We found that the specific decision tendency to underweight the supply line is linked to an individual's level of cognitive reflection. Furthermore, performance differs for entire supply chains and for specific echelons, and holds under standard mitigation efforts. The findings have implications for supply chain design, education, and industry.     

Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

The bullwhip effect describes the tendency for the variance of orders in supply chains to increase as one moves upstream from consumer demand. We report on a set of laboratory experiments with a serial supply chain that tests behavioral causes of this phenomenon, in particular the possible influence of coordination risk. Coordination risk exists when individuals' decisions contribute to a collective outcome and the decision rules followed by each individual are not known with certainty, for example, where managers cannot be sure how their supply chain partners will behave. We conjecture that the existence of coordination risk may contribute to bullwhip behavior. We test this conjecture by controlling for environmental factors that lead to coordination risk and find these controls lead to a significant reduction in order oscillations and amplification. Next, we investigate a managerial intervention to reduce the bullwhip effect, inspired by our conjecture that coordination risk contributes to bullwhip behavior. Although the intervention, holding additional on‐hand inventory, does not change the existence of coordination risk, it reduces order oscillation and amplification by providing a buffer against the endogenous risk of coordination failure. We conclude that the magnitude of the bullwhip can be mitigated, but that its behavioral causes appear robust.     

非平稳需求状态下虚拟企业牛鞭效应的存在性分析

本文从供应链管理的角度出发,将牛鞭效应的概念和模型引入虚拟企业的运营过程中,运用可变系数的自回归移动平均(ARIMA)模型对虚拟企业上下游成员企业需求信息进行预测,研究非平稳需求状态下虚拟企业内上下游企业订货水平的差异,证明了虚拟企业运营中牛鞭效应的存在性,最后提出除虚拟企业的敏捷经营、信息共享对牛鞭效应的影响以外,需求的波动也是影响牛鞭效应的重要因素,并讨论了信息完全共享的情况下牛鞭效应存在的条件.     

基金交易行为与市场波动——基于小波与互谱分析的数据挖掘

本文选取2007～2009年基金十大重仓股数据进行小波分析,研究重仓股在不同阶段波动率的特征———微观层面基金行为对这种波动率的直接动量冲击的影响以及金融市场层面沪深300指数对此波动率的联动性的影响。实证结果发现,基金增仓和持股可以减小股票波动率,但减仓会加大股票波动率,呈现出非对称稳定市场的特性。在金融市场层面分析上,论文应用溢出效应检验互谱分析方法,发现这种波动率主要是由基金持股组合调整造成的,市场波动对个股波动率影响不大,即股票波动率主要是由基金交易行为造成所引致。     

UPSTREAM VOLATILITY IN THE SUPPLY CHAIN: THE MACHINE TOOL INDUSTRY AS A CASE STUDY

Cyclicality is a well‐known and accepted fact of life in market‐driven economies. Less well known or understood, however, is the phenomenon of amplification as one looks “upstream” in the industrial supply chain. We examine the amplification phenomenon and its implications through the lens of one upstream industry that is notorious for the intensity of the business cycles it faces: the machine tool industry. Amplification of demand volatility in capital equipment supply chains, e. g., machine tools, is particularly large relative to that seen in distribution and component parts supply chains. We present a system dynamics simulation model to capture demand volatility amplification in capital supply chains. We explore the lead‐time, inventory, production, productivity, and staffing implications of these dynamic forces. Several results stand out. First, volatility hurts productivity and lowers average worker experience. Second, even though machine tool builders can do little to reduce the volatility in their order streams through choice of forecast rule, a smoother forecasting policy will lead companies to retain more of their skilled work force. This retention of skilled employees is often cited as one of the advantages that European and Japanese companies have had relative to their U. S. competitors. Our results suggest some insights for supply chain design and management: downstream customers can do a great deal to reduce the volatility for upstream suppliers through their choice of order forecast rule. In particular, companies that use smoother forecasting policies tend to impose less of their own volatility upon their supply base and may consequently enjoy system‐wide cost reduction.     

股票收益波动与Beta系数的时变性

本文利用扩展的S-S模型,对上海股市2000年间的日收益数据进行实证分析,以进一步探讨小公司股票、大公司股票收益波动和市场波动之间的关系。研究结果发现,在市场波动加剧时大公司股票与小公司股票的反应是不同的,小公司的系统风险更易于增大。因此在进行事件研究时,必须考虑到Beta系数的时变性。     

投资者情绪指标与股票市场——基于扩展卡尔曼滤波方法的研究

基于扩展卡尔曼滤波(EKF)方法,首次构造出过滤市场噪声的投资者情绪指标,并在此基础上应用向量自回归模型分析我国投资者情绪指标与股票收益、股本规模等因素的经验关系,实证结果表明:(1)扩展卡尔曼滤波方法可以获得一个更加清晰反映投资者情绪的状态变量;(2)情绪的变化量比情绪指标本身具有更强的市场收益预测能力;(3)大规模公司股票的收益对投资者情绪的影响程度高于小规模公司股票,而投资者情绪对小规模公司股票的影响显著高于大规模公司的股票,并且情绪波动能够预测小规模股票的短期收益惯性和跨期收益反转的特征,证明情绪波动是影响资产定价的重要主观因素。     

Beyond Information Sharing: An Empirical Analysis of Vendor‐Managed Inventory

Using a unique, item‐level data set, we examined benefits to downstream firms (distributors) from the decision‐transfer component of vendor‐managed inventory (VMI), the feature that distinguishes VMI from other information‐sharing, collaborative supply chain programs. Our major findings are that the decision‐transfer component of VMI adds significant benefits to the downstream firm in terms of inventory and stockout reductions above and beyond information sharing, and that these two benefits may be realized at different times following VMI implementation; that is, inventory reduction, initially, may be the major benefit to distributors from VMI, while the benefits of stockout reduction may more likely be realized after the first year of implementation. In addition, VMI provides benefits to the upstream firm (manufacturer) by reducing the downstream firm's inventory variability, a likely contributor to the bullwhip effect. Based on our empirical analysis, the decision‐transfer component of VMI, on average, reduces inventory levels by 7%, stockouts by 31%, and inventory variability by 9%.     

Generalized Method of Integrated Moments for High‐Frequency Data

We propose a semiparametric two‐step inference procedure for a finite‐dimensional parameter based on moment conditions constructed from high‐frequency data. The population moment conditions take the form of temporally integrated functionals of state‐variable processes that include the latent stochastic volatility process of an asset. In the first step, we nonparametrically recover the volatility path from high‐frequency asset returns. The nonparametric volatility estimator is then used to form sample moment functions in the second‐step GMM estimation, which requires the correction of a high‐order nonlinearity bias from the first step. We show that the proposed estimator is consistent and asymptotically mixed Gaussian and propose a consistent estimator for the conditional asymptotic variance. We also construct a Bierens‐type consistent specification test. These infill asymptotic results are based on a novel empirical‐process‐type theory for general integrated functionals of noisy semimartingale processes.     

信息共享下库存量牛鞭效应的影响因素研究

目前的研究多从供应链上游角度出发考虑传统牛鞭效应,而本文从供应链下游的角度研究库存量牛鞭效应得到了不一样的管理学启示.在需求函数方面,建立的需求模型包括市场规模、价格敏感性系数等更有现实意义的要素.在此,本文建立了包括一个零售商和一个制造商的简单两级供应链,得出了制造商在采用补充至订货点策略和最小均方差预测技术,在两种不同的信息共享模式下的库存量牛鞭效应表达式,并对他们的影响因素进行了分析.而且通过数值分析对模型进行了验证并得到新的结果.通过研究发现,信息共享能够显著降低制造商的库存量牛鞭效应;零售商和制造商的库存量牛鞭效应都不受市场规模的影响;零售商的库存量牛鞭效应在一定条件下不存在;相比于零售商提前期,制造商提前期对制造商的库存量牛鞭效应影响更大.同时,价格敏感性系数、价格自相关系数等因素对制造商库存量牛鞭效应也有不同程度的影响.     

Changing risk preferences in supply chain inventory decisions

The risk preferences of managers may cause their inventory ordering decisions to deviate from the optimal policy. Past studies in operations management have produced mixed results. This study examines this proposition using decision data collected from a supply chain experiment. This article finds that changing the risk preferences of managers with respect to demand changes and supplier failures is a significant behavioural factor in explaining deviations in ordering decisions. This result provides an additional behavioural cause in explaining the bullwhip effect in supply chains. It also provides insights on the challenges for reducing the bullwhip effect in supply chains.     

需求信息滞后下的零售商决策与牛鞭效应分析

牛鞭效应是一种在企业经营中广泛存在的普遍现象,对企业的生产经营产生了极大的负面影响。本文考虑一个供应商和一个零售商组成的简单两级供应链系统。在这个系统中,首先建立在需求信息滞后情况下的零售商库存决策和混合决策(库存决策和定价决策)模型;然后进行决策对牛鞭效应的影响分析。结果显示:在需求信息存在滞后的情况下,零售商最优决策的定价和订货点的期望值是确定的,与需求信息滞后期的长度无关;零售商的库存决策能产生牛鞭效应,且牛鞭效应会随需求滞后期增加而逐渐减少;零售商的混合决策能否产生牛鞭效应,取决于需求自相关系数的变化,而与需求滞后期无关,但随着需求滞后期会改变期变化程度。此外,库存决策还是混合决策,在需求不具有滞后时较具有滞后时牛鞭效应表现得较为为减弱;零售商的混合决策与库存决策相比,只有在需求的自相关系数取较小值时,才表现得更为强烈,否则会减弱。因而对于零售商而言,适当的需求信息滞后以及供应商对零售商的定价柔性能减少牛鞭效应。     

Parametric Inference and Dynamic State Recovery From Option Panels

We develop a new parametric estimation procedure for option panels observed with error. We exploit asymptotic approximations assuming an ever increasing set of option prices in the moneyness (cross‐sectional) dimension, but with a fixed time span. We develop consistent estimators for the parameters and the dynamic realization of the state vector governing the option price dynamics. The estimators converge stably to a mixed‐Gaussian law and we develop feasible estimators for the limiting variance. We also provide semiparametric tests for the option price dynamics based on the distance between the spot volatility extracted from the options and one constructed nonparametrically from high‐frequency data on the underlying asset. Furthermore, we develop new tests for the day‐by‐day model fit over specific regions of the volatility surface and for the stability of the risk‐neutral dynamics over time. A comprehensive Monte Carlo study indicates that the inference procedures work well in empirically realistic settings. In an empirical application to S&P 500 index options, guided by the new diagnostic tests, we extend existing asset pricing models by allowing for a flexible dynamic relation between volatility and priced jump tail risk. Importantly, we document that the priced jump tail risk typically responds in a more pronounced and persistent manner than volatility to large negative market shocks.     

制度因素对我国国际储备的惯性影响

本文在缓冲储备理论中引入了习惯形成因素,在BS模型的基础上构造了一个既包括国际收支波动性和机会成本同时又考虑了习惯形成因素的动态调整模型。利用1996-2009年间中国国际储备的月度数据进行了实证分析,结果表明:(1)一国国际储备是国际收支不确定性和机会成本的函数,它们三者之间存在长期均衡关系,也就是说BS理论预期模型在经过适当的修正后对中国外汇储备规模的实证研究是适用的。(2)习惯形成参数越大,以往的制度因素形成的惯性对后期储备积累的影响就越大,国际收支不确定性以及机会成本对储备的影响也越小,并且这个惯性一旦形成,很难立刻改变。(3)正是由于考虑了习惯形成因素,外汇储备规模关于国际收支不确定性的弹性以及机会成本的弹性更接近于BS模型的理论预期值。     

Uncertainty and Unemployment

This paper studies the impact of time‐varying idiosyncratic risk at the establishment level on unemployment fluctuations over 1972–2009. I build a tractable directed search model with firm dynamics and time‐varying idiosyncratic volatility. The model allows for endogenous separations, entry and exit, and job‐to‐job transitions. I show that the model can replicate salient features of the microeconomic behavior of firms and that the introduction of volatility improves the fit of the model for standard business cycle moments. In a series of counterfactual experiments, I show that time‐varying risk is important to account for the magnitude of fluctuations in aggregate unemployment for past U.S. recessions. Though the model can account for about 40% of the total increase in unemployment for the 2007–2009 recession, uncertainty alone is not sufficient to explain the magnitude and persistence of unemployment during that episode.     

股市预期收益率与波动关系的研究

利用SV_m模型对Koopman等的研究结论进行了验证,对SV_m模型进行了扩展,提出了一种能捕捉非对称效应的A-SV_m模型,并用该模型和SV_m模型对预期收益率与波动的关系进行了实证研究.研究结果与Koopman等人的结论不同,表明预期收益率与波动之间的关系是时变的,而且波动(条件方差)对预期收益率的影响并不显著.结合Harrison、Campbell等人的研究对结果进行了解释.     

基于企业生命周期的上市公司融资结构研究

本文以筛选得到的354家我国上市公司2002-2007年的数据为样本,借鉴产业经济学增长率产业分类法将上市公司大致界定处于成长期、成熟期或衰退期,通过统计分析和建立计量经济模型,从企业自身特征的角度研究了处于不同生命周期阶段的企业融资结构特征、差异性和导致差异的原因。研究表明,处于不同生命周期阶段的上市公司融资结构具有显著差异。其中长期负债率在成长期相对较高,短期负债率在成熟期相对于成长期与衰退期较高,但与成长期的差别较小。进一步讨论得到在我国上市公司自身与投资者相互选择的作用下,从公司自身特征的角度看,公司的长期负债率与其自身的资产担保价值密切相关,短期负债率与其公司业绩、成长性、公司规模和内部治理密切相关。     

On variance amplification in a three-echelon supply chain with minimum mean square error forecasting

We analyse a three echelon supply chain model. First-order autoregressive end consumer demand is assumed. We obtain exact analytical expressions for bullwhip and net inventory variance at each echelon in the supply chain. All of the three supply chain participants employ the order-up-to policy with the minimum mean square error forecasting scheme. After demonstrating that the character of the stochastic ordering process observed at each level of the supply chain is mathematically tractable, we show that the upper stream participants have complete information of the market demand process. Then we quantify the bullwhip produced by the system, together with the amplification ratios of the variance of the net inventory levels. Our analysis reveals that the level of the supply chain has no impact upon the bullwhip effect, rather bullwhip is determined by the accumulated lead-time from the customer and the local replenishment lead-time. We also find that the conditional variance of the forecast error over the lead-time is identical to the variance of the net inventory levels and that the net inventory variance is dominated by the local replenishment lead-time.     

The Realized Laplace Transform of Volatility

We introduce and derive the asymptotic behavior of a new measure constructed from high‐frequency data which we call the realized Laplace transform of volatility. The statistic provides a nonparametric estimate for the empirical Laplace transform function of the latent stochastic volatility process over a given interval of time and is robust to the presence of jumps in the price process. With a long span of data, that is, under joint long‐span and infill asymptotics, the statistic can be used to construct a nonparametric estimate of the volatility Laplace transform as well as of the integrated joint Laplace transform of volatility over different points of time. We derive feasible functional limit theorems for our statistic both under fixed‐span and infill asymptotics as well as under joint long‐span and infill asymptotics which allow us to quantify the precision in estimation under both sampling schemes.