Financial Hedging and Optimal Procurement Policies under Correlated Price and Demand

We consider a firm that procures an input commodity to produce an output commodity to sell to the end retailer. The retailer's demand for the output commodity is negatively correlated with the price of the output commodity. The firm can sell the output commodity to the retailer through a spot, forward or an index‐based contract. Input and output commodity prices are also correlated and follow a joint stochastic price process. The firm maximizes shareholder value by jointly determining optimal procurement and hedging policies. We show that partial hedging dominates both perfect hedging and no‐hedging when input price, output price, and demand are correlated. We characterize the optimal financial hedging and procurement policies as a function of the term structure of the commodity prices, the correlation between the input and output prices, and the firm's operating characteristics. In addition, our analysis illustrates that hedging is most beneficial when output price volatility is high and input price volatility is low. Our model is tested on futures price data for corn and ethanol from the Chicago Mercantile Exchange.     

Coordinating operational policy with financial hedging for risk-averse firms

A risk-averse firm׳s financial hedging activity can impact the decision making in its daily operations. We introduce a CE-based approach that can help the firm to simplify the procedure in making hedging-consistent decisions. A key feature of this new approach is that it allows for the existence of nonfinancial random factors, which give rise to the risk exposure that cannot be hedged in the financial market. By using a CE operator, we show that the optimal operational policy can be obtained by maximizing the CE-based value function. Although the CE operator may bring additional nonlinearity to the value function, we find that the commonly desired base-stock policy can remain optimal under specific conditions. We hope that this new approach can help pave the way for future investigation on joint operations management and financial hedging problems in dynamic settings.     

Technical Note – Pricing Below Cost Under Exchange‐Rate Risk

Pricing below cost is often classified as “dumping” in international trade and as “predatory pricing” in local markets. It is legally prohibited from practice because of earlier findings that it leads to predatory behavior by either eliminating competition or stealing market share. This study shows that a stochastic exchange rate can create incentives for a profit‐minded monopoly firm to set price below marginal cost. Our result departs from earlier findings because the optimal pricing decision is based on a rational behavior that does not exhibit any malicious intent against the competition to be considered as violating anti‐trust laws. The finding is a robust result, because our analysis demonstrates that this behavior occurs under various settings such as when the firm (i) is risk‐averse, (ii) can postpone prices until after exchange rates are realized, (iii) is capable of manufacturing in multiple countries, and (iv) operates under demand uncertainty in addition to the random exchange rate.     

需求-汇率风险聚集下的汇率风险对冲与批发价激励

以一个由上游制造商和下游零售商组成的二级供应链为建模背景,在汇率风险和需求风险聚集(pooling)在下游零售商的情况下,分别建立了有无批发价激励情形下的两个动态博弈模型,获得了相应的均衡。通过分析相应的均衡,结果表明,(1)在两种情形下,零售商的汇率风险对冲行为具有稳定供应链生产行为的作用,但批发价激励能够提高零售商的汇率风险对冲比例和供应链节点企业间的交易价格;(2)与无批发价激励的情形相比,有批发价激励时的制造商利润较高,从而制造商有使用批发价激励零售商对冲汇率风险的动机;(3)在风险聚集下,"通过较低的批发价来激励零售商对冲汇率风险"这一策略能够实现供应链盈利水平与风险承担之间的权衡:需求和汇率风险增加均会使得供应链节点企业和供应链整体的盈利水平降低,同时也将降低风险聚集处的节点企业和供应链整体的利润方差。     

Flexible Capacity Investments and Product Mix: Optimal Decisions and Value of Postponement Options

In this article, we study a firm's interdependent decisions in investing in flexible capacity, capacity allocation to individual products, and eventual production quantities and pricing in meeting uncertain demand. We propose a three‐stage sequential decision model to analyze the firm's decisions, with the firm being a value maximizer owned by risk‐averse investors. At the beginning of the time horizon, the firm sets the flexible capacity level using an aggregate demand forecast on the envelope of products its flexible resources can accommodate. The aggregate demand forecast evolves as a Geometric Brownian Motion process. The potential market share of each product is determined by the Multinomial Logit model. At a later time and before the end of the time horizon, the firm makes a capacity commitment decision on the allocation of the flexible capacity to each product. Finally, at the end of the time horizon, the firm observes the demand and makes the production quantity and pricing decisions for end products. We obtain the optimal solutions at each decision stage and investigate their optimal properties. Our numerical study investigates the value of the postponed capacity commitment option in supplying uncertain operation environments.     

Supply Diversification with Responsive Pricing

We study sourcing and pricing decisions of a firm with correlated suppliers and a price‐dependent demand. With two suppliers, the insight—cost is the order qualifier while reliability is the order winner—derived in the literature for the case of exogenously determined price and independent suppliers, continues to hold when the suppliers' capacities are correlated. Moreover, a firm orders only from one supplier if the effective purchase cost from him, which includes the imputed cost of his unreliability, is lower than the wholesale price charged by his rival. Otherwise, the firm orders from both. Furthermore, the firm's diversification decision does not depend on the correlation between the two suppliers' random capacities. However, its order quantities do depend on the capacity correlation, and, if the firm's objective function is unimodal, the total order quantity decreases as the capacity correlation increases in the sense of the supermodular order. With more than two suppliers, the insight no longer holds. That is, when ordering from two or more suppliers, one is the lowest‐cost supplier and the others are not selected on the basis of their costs. We conclude the paper by developing a solution algorithm for the firm's optimal diversification problem.     

Supplier competition and its impact on firm׳s sourcing strategy

Most research on firms׳ sourcing strategies assumes that wholesale prices and reliability of suppliers are exogenous. It is of our interest to study suppliers׳ competition on both wholesale price and reliability and firms׳ corresponding optimal sourcing strategy under complete information. In particular, we study a problem in which a firm procures a single product from two suppliers, taking into account suppliers׳ price and reliability differences. This motivates the suppliers to compete on these two factors. We investigate the equilibria of this supplier game and the firm׳s corresponding sourcing decisions. Our study shows that suppliers׳ reliability often plays a more important role than wholesale price in supplier competition and that maintaining high reliability and a high wholesale price is the ideal strategy for suppliers if multiple options exist. The conventional wisdom implies that low supply reliability and high demand uncertainty motivate dual-sourcing. We notice that when the suppliers׳ shared market/transportation network is often disrupted and demand uncertainty is high, suppliers׳ competition on both price and reliability may render the sole-sourcing strategy to be optimal in some cases that depend on the format of suppliers׳ cost functions. Moreover, numerical study shows that when the cost or vulnerability (to market disruptions) of one supplier increases, its profit and that of the firm may not necessarily decrease under supplier competition.     

Combined Pricing and Portfolio Option Procurement

In this paper, we study a single‐product periodic‐review inventory system that faces random and price‐dependent demand. The firm can purchase the product either from option contracts or from the spot market. Different option contracts are offered by a set of suppliers with a two‐part fee structure: a unit reservation cost and a unit exercising cost. The spot market price is random and its realization may affect the subsequent option contract prices. The firm decides the reservation quantity from each supplier and the product selling price at the beginning of each period and the number of options to exercise (inventory replenishment) at the end of the period to maximize the total expected profit over its planning horizon. We show that the optimal inventory replenishment policy is order‐up‐to type with a sequence of decreasing thresholds. We also investigate the optimal option‐reservation policy and the optimal pricing strategy. The optimal reservation quantities and selling price are shown to be both decreasing in the starting inventory level when demand function is additive. Building upon the analytical results, we conduct a numerical study to unveil additional managerial insights. Among other things, we quantify the values of the option contracts and dynamic pricing to the firm and show that they are more significant when the market demand becomes more volatile.     

Competing Mechanisms in a Common Value Environment

Consider strategic risk‐neutral traders competing in schedules to supply liquidity to a risk‐averse agent who is privately informed about the value of the asset and his hedging needs. Imperfect competition in this common value environment is analyzed as a multi‐principal game in which liquidity suppliers offer trading mechanisms in a decentralized way. Each liquidity supplier behaves as a monopolist facing a residual demand curve resulting from the maximizing behavior of the informed agent and the trading mechanisms offered by his competitors. There exists a unique equilibrium in convex schedules. It is symmetric and differentiable and exhibits typical features of market‐power: Equilibrium trading volume is lower than ex ante efficiency would require. Liquidity suppliers charge positive mark‐ups and make positive expected profits, but these profits decrease with the number of competitors. In the limit, as this number goes to infinity, ask (resp. bid) prices converge towards the upper (resp. lower) tail expectations obtained in Glosten (1994) and expected profits are zero.     

A Multi‐Supplier Sourcing Problem with a Preference Ordering of Suppliers

We study a sourcing problem faced by a firm that seeks to procure a product or a component from a pool of alternative suppliers. The firm has a preference ordering of the suppliers based on factors such as their past performance, quality, service, geographical location, and financial strength, which are commonly included in a supplier scorecard system. Thus, the firm first uses available inventory from supplier 1, if any, then supplier 2, if any, and so on. The suppliers differ in costs and prices. The buyer firm seeks to determine which suppliers to purchase from and in what quantities to maximize its total expected profit subject to the preference ordering constraint. We present the optimal solution to this problem, and show that it has a portfolio structure. It consists of a sub‐set of suppliers that are ordered by their underage and overage costs. This portfolio achieves a substantial profit gain compared to sourcing from a unique supplier. We present an efficient algorithm to compute the optimal solution. Our model applies to component sourcing problems in manufacturing, merchandizing problems in retailing, and capacity reservation problems in services.     

Shareholders' Wealth‐Maximizing Operating Decisions and Risk Management Practices in a Mixed Contracts Economy

This paper establishes a critically important positive role for operations management practices and financial hedging. We show that operations management decisions and financial hedging are intertwined, and we advance a framework that can identify their combined effects on investors' wealth. We show that: (a) firms (publicly traded corporations) will optimally hold adequate riskless working capital (e.g., cash) to minimize the cost of obtaining non‐financial inputs, and the magnitude of this cash holding depends on operating details, and (b) operations management and financial hedging can lower firms' cash requirements, and boost productivity, defined as the wealth created in the firm per dollar of invested capital. Productivity‐enhancing practices—by “freeing up” some of the firm's cash—can maximize the investors' wealth. We show that these results obtain because firms' contracts with many of the providers of non‐financial inputs are not traded, and because investors can invest not just in public corporations but also in businesses “outside the markets” (e.g., proprietorships, partnerships, and private equity).     

MANAGERIAL HEDGING AND PORTFOLIO MONITORING

Incentive compensation induces correlation between the portfolio of managers and the cash flow of the firms they manage. This correlation exposes managers to risk and hence gives them an incentive to hedge against the poor performance of their firms. We study the agency problem between shareholders and a manager when the manager can hedge his compensation using financial markets and shareholders can monitor the manager's portfolio in order to keep him from hedging, but monitoring is costly. We find that the optimal incentive compensation and governance provisions have the following properties: (i) the manager's portfolio is monitored only when the firm performs poorly, (ii) the manager's compensation is more sensitive to firm performance when the cost of monitoring is higher or when hedging markets are more developed, and (iii) conditional on the firm's performance, the manager's compensation is lower when his portfolio is monitored, even if no hedging is revealed by monitoring. Moreover, the model suggests that the optimal level of portfolio monitoring is higher for managers of firms whose performance canbehedged more easily, such as larger firms and firms in more developed financial markets. (JEL: G30, D82)     

期望效用视角下的风险对冲效率

与传统文献将风险下降比率作为风险对冲效率指标不同,本文引入期望效用理论来比较最小方差对冲策略、最小在险价值(VaR)对冲策略和最小条件在险价值(CVaR)对冲策略的对冲效率,从而将人们的风险态度同对冲策略选择联系起来,以实现不同风险态度的投资者选择不同风险对冲策略的目的。借用风险中性效用函数、二次效用函数和CARA效用函数,本文严格证明:在这三种对冲策略中,最小方差对冲策略过于保守,最小VaR对冲策略最为激进,风险厌恶程度大的投资者偏好最小方差对冲策略,风险中性投资者和风险厌恶程度小的投资者更偏好最小VaR对冲策略,最小CVaR对冲策略介于二者之间。     

基于非排它性互换合约的管理层激励研究

管理者进行金融衍生品市场的非排它性股权互换交易会降低报酬合约的事后激励强度,并对企业价值和风险产生影响.本文在管理者可同时影响企业风险和企业价值的条件下,构建基于股权互换避险交易的管理者激励薪酬动态博弈模型.结果表明,当管理者投入产出效率高于某临界值时,互换避险的存在不会降低激励合约的强度;否则激励合约强度的减弱程度随...     

Pricing and Capacity Rationing with Customer Disappointment Aversion

Customers are averse to disappointment that arises when economic outcomes fall short of expectations. In this study, we study a two‐period model in which the firm may create rationing in either period. In the anticipation of possible disappointment due to stock‐outs, strategic customers decide when to purchase and the firm determines the prices and rationing levels in each period. We explore the impact of disappointment aversion on customers' strategic purchasing behavior and the firm's pricing and rationing decisions. Without disappointment aversion, it is optimal for the firm to adopt a uniform pricing policy without rationing. However, when strategic customers are averse to disappointment, a firm may be able to increase profits with an appropriate level of rationing. We analyze both the mark‐up and mark‐down policies. We show that, in a mark‐down scenario, the firm always benefits from disappointment aversion behavior by using an appropriate level of rationing in a low‐price period. However, in a mark‐up scenario, whether it is beneficial for the firm to induce disappointment aversion behavior depends on how customers frame payoffs in different periods when forming utilities. Particularly, when customers compartmentalize payoffs in different periods to form utilities, the firm should not induce disappointment aversion behavior.     

Operational Hedging and Diversification under Correlated Supply and Demand Uncertainty

When facing supply uncertainty caused by exogenous factors such as adverse weather conditions, firms diversify their supply sources following the wisdom of “not holding all eggs in one basket.” We study a firm that decides on investment and production levels of two unreliable but substitutable resources. Applying real options thinking, production decisions account for actual supply capabilities, whereas investment decisions are made in advance. To model triangular supply and demand correlations, we adapt the concepts of random capacity and stochastic proportional yield while using concordant ordered random variables. Optimal profit decreases monotonically in supply correlation and increases monotonically in supply–demand correlation. Optimal resource selection, however, depends on the trivariate interplay of supply and demand and responds non‐monotonically to changing correlations. Moreover, supply hedges (i.e., excess capacity at alternative sources) can be optimal even if supply resources are perfectly positively correlated. To accommodate changing degrees of correlation, the firm adjusts the lower margin capacities under random capacity; but under stochastic proportional production capability, it uses either low‐ or high‐margin capacities to create tailored “scale hedges” (i.e., excess capacity at one source which can partially substitute for diversification).     

基于时变Markov的DCC-GARCH模型最小风险套期保值研究

考虑Markov状态转移概率的时变特征,在传统DCC-GARCH基础上,提出基于Markov时变转移概率的DCC-GARCH模型（TVTP-DCC-GARCH）研究最小风险套期保值比例的估计方法,并利用两阶段极大似然法对模型参数进行估计。进一步分别从样本内和样本外估计沪深300指数期货和现货的最优套期保值比率,对套期保值的绩效进行检验,并将检验结果分别与Markov转移概率恒定的DCC-GARCH（FTP-DCC-GARCH）、DCC-GARCH、OLS、1：1完全套期保值以及无套期保值的沪深300指数现货的绩效进行对比。实证结果表明,利用基于Markov状态转移的DCC-GARCH模型研究沪深300指数期货的套期保值问题具有一定合理性,且在参数估计中TVTP-DCC-GARCH模型的拟合效果最佳;在套期保值有效性方面,TVTP-DCC-GARCH模型优于其他模型,说明在DCC-GARCH模型中引入时变状态转移概率能够有效提高套期保值组合的绩效。     

产能不确定供应商的风险厌恶行为对供应链的影响

针对具有产能不确定性的风险厌恶供应商和风险中性零售商组成的供应链系统,本文采用经典的风险指标Value-at-risk(VaR)衡量供应商的风险厌恶程度,并将其作为供应商的优化约束条件之一。在斯坦伯格顺序博弈的模型框架下,零售商做定价和订货量决策,供应商做批发价决策。本文求得供应商批发价在VaR约束下的理论上下界,研究了风险厌恶程度对最优批发价的影响。与风险中性环境下的结果相比,在设立合理目标利润的前提下,高风险厌恶程度可能使得供应商提高批发价,低风险厌恶程度则对供应商批发价无影响。当目标利润设立很高时,供应商为了逐利将会降低批发价诱导零售商提高订货量。     

沪深300股指期货对冲效率研究

本文首先通过在VECM-GARCH模型中引入非对称基差,研究了基差对我国沪深300股指期货和现货回报的条件均值与风险结构影响的非对称效应。在此基础上分别以方差最小化（MVHR）和效用最大化（UMHR）为标准,考察了包含VECM-GARCH-X在内六种不同模型在样本内外的风险对冲效果,并探索投资者风险厌恶系数与对冲成本对最优套期保值模型选取的影响。最后得出：基差对沪深300股指期货和现货回报的条件均值和风险结构都存在显著的非对称效应,在一般风险厌恶水平下考虑基差非对称效应的VECM-GARCH-X模型能够总体上提高对冲效率,但是无法弥补动态调整增加的额外交易成本,因此固定对冲比率的OLS模型在实践中仍然更优;同时最优套期保值模型的选择与投资者的风险厌恶系数显著相关,风险厌恶程度越大,动态套期保值模型的效果就越好,这一发现也得到了论文最后最优调整频率研究结论的进一步证实。     

Dynamic Supply Risk Management with Signal‐Based Forecast,Multi‐Sourcing,and Discretionary Selling

We examine the critical role of advance supply signals—such as suppliers’ financial health and production viability—in dynamic supply risk management. The firm operates an inventory system with multiple demand classes and multiple suppliers. The sales are discretionary and the suppliers are susceptible to both systematic and operational risks. We develop a hierarchical Markov model that captures the essential features of advance supply signals, and integrate it with procurement and selling decisions. We characterize the optimal procurement and selling policy, and the strategic relationship between signal‐based forecast, multi‐sourcing, and discretionary selling. We show that higher demand heterogeneity may reduce the value of discretionary selling, and that the mean value‐based forecast may outperform the stationary distribution‐based forecast. This work advances our understanding on when and how to use advance supply signals in dynamic risk management. Future supply risk erodes profitability but enhances the marginal value of current inventory. A signal of future supply shortage raises both base stock and demand rationing levels, thereby boosting the current production and tightening the current sales. Signal‐based dynamic forecast effectively guides the firm's procurement and selling decisions. Its value critically depends on supply volatility and scarcity. Ignoring advance supply signals can result in misleading recommendations and severe losses. Signal‐based dynamic supply forecast should be used when: (a) supply uncertainty is substantial, (b) supply‐demand ratio is moderate, (c) forecast precision is high, and (d) supplier heterogeneity is high.