Alternative marginal-cost pricing for road networks

In the literature, several studies have algebraically characterized the set of toll vectors or patterns that, when added to a user equilibrium problem, its solution would be system optimal. Toll vectors in this set are termed “valid.” While the toll vector commonly advocated in the literature, i.e., one that equates the toll on each link to its marginal external cost, is always valid, other valid toll vectors generally exist and many leave some utilized links in the network untolled. On the surface, this may appear unreasonable and seems to violate the principle of marginal-cost pricing. This note shows that, when travel demands are elastic, all valid toll vectors satisfy this principle, in that the total tolls for each path equals the congestion externality an additional traveler on the path imposes on others.     

Pareto-improving and revenue-neutral congestion pricing schemes in two-mode traffic networks

This paper studies a Pareto-improving and revenue-neutral congestion pricing scheme on a simple two-mode (highway and transit) network: this scheme aims at simultaneously improving system performance, making every individual user better off, and having zero total revenue. Different Pareto-improving situations are explored when a two-mode transportation system serves for travel groups with different value-of-time (VOT) distributions. Since the congestion pricing scheme suggested here charges transit users negative tolls and automobile users positive tolls, it can be considered as a proper way to implement congestion pricing and transit subsidy in one step, while offsetting the inequity for the poor. For a general VOT distribution of commuters, the condition of Pareto-improving is established, and the impact of the VOT distribution on solving the inequity issue is explored. For a uniform VOT distribution, we show that a Pareto-improving and revenue-neutral pricing scheme always exists for any target modal split pattern that reduces the total system travel time.     

Design and evaluation of road pricing: state-of-the-art and methodological advances

This article provides an in-depth review of the state-of-the-art and describes methodological advances in the design and evaluation of road network pricing schemes. A number of paradigm shifts from the two polar cases of the marginal social cost pricing of road traffic congestion and revenue-maximizing road toll pricing are analyzed, as induced by the need to address realistic design complexities and constraints. The crucial role of the joint consideration of pricing strategies with optimal capacity provision and several network management measures is manifested and an integrated evaluation framework is suggested to incorporate a wide range of road pricing impacts into the scheme design process.     

Joint pricing and design of urban highways with spatial and user group heterogeneity

This paper addresses the joint optimization of capacity investments and toll charges imposed on multi-group users in monopolistic private highways within general road networks. A game-theoretic formulation is provided that leads to a nonconvex bilevel program. The proposed modeling framework handles several complex issues raised in realistic applications, such as regulations on the levels of tolls and service, and the discrete nature of highway capacity, using a genetic optimization technique. Real-application results show the importance of considering the spatial heterogeneity of prices, and the tradeoff between investments and pricing strategies in regulated private highways.     

An efficient approximation method for American exotic options

The authors suggest a modified quadratic approximation scheme, and apply this scheme to American barrier (knock‐out) and floating‐strike lookback options. This modified scheme introduces an additional parameter into the quadratic approximation method, originally suggested by G. Barone‐Adesi and R. Whaley (1987), to reduce pricing errors. When the barrier is close to the underlying asset's current price, the approximation formula is more accurate than lattice methods because the optimal exercise boundary is independent of the underlying asset's current price. That is, the proposed method overcomes the “near‐barrier” problem that occurs in lattice methods. In addition, the pricing error decreases when the underlying asset's volatility is high. This approximation scheme is more efficient than B. Gao, J. Huang, and M. Subrahmanyam's (2000) method. As a second application of the modified approximation scheme, the authors provide an approximation formula for American floating‐strike lookback options which is the first approximation formula ever suggested in the literature. Compared to S. Babbs' (2000) binomial approach, our approximation method is more efficient after controlling for pricing errors, and is more accurate after controlling for computing time. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:29–59, 2007     

A generalization of the Barone‐Adesi and Whaley approach for the analytic approximation of American options

This article introduces a general quadratic approximation scheme for pricing American options based on stochastic volatility and double jump processes. This quadratic approximation scheme is a generalization of the Barone‐Adesi and Whaley approach and nests several option models. Numerical results show that this quadratic approximation scheme is efficient and useful in pricing American options. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:478–493, 2009     

First-Order Schemes in the Numerical Quantization Method

The numerical quantization method is a grid method that relies on the approximation of the solution to a nonlinear problem by piecewise constant functions. Its purpose is to compute a large number of conditional expectations along the path of the associated diffusion process. We give here an improvement of this method by describing a first-order scheme based on piecewise linear approximations. Main ingredients are correction terms in the transition probability weights. We emphasize the fact that in the case of optimal quantization, many of these correcting terms vanish. We think that this is a strong argument to use it. The problem of pricing and hedging American options is investigated and a priori estimates of the errors are proposed.     

DEFAULTABLE OPTIONS IN A MARKOVIAN INTENSITY MODEL OF CREDIT RISK

This paper is a follow‐up to “Valuation and Hedging of Defaultable Game Options in a Hazard Process Model” by the same authors. In the present paper we give user friendly assumptions ensuring that the general conditions in the previous paper are satisfied. We also give a systematic procedure to construct suitable intensity models of credit risk, and, in the Markovian case, we provide a variational inequality approach to the pre‐default pricing problem. We finally illustrate our results on a study of defaultable convertible bonds.     

Pricing and return strategy of online retailers based on return insurance

With the development of e-commerce, online shopping has become increasingly common, and as a result, consumers inevitably encounter the problem of returns. Therefore, pricing, return policy and return insurance strategy have attracted considerable research attention. In this paper, we construct four models to study pricing, return policy and return insurance strategy. We show that when a product's net residual value is greater than or equal to zero, online retailers should offer a money-back guarantee (MBG) return policy; however, they do not have to offer free return insurance because the latter does not increase their market share and profit, nor does it increase consumer surplus. The optimal strategy of insurance providers is unaffected by whether the policyholder is an online retailer or a consumer and should be neutral, which helps insurance providers gain the trust of policyholders. Consumers should buy products only when online retailers offer an MBG return policy; however, consumers should not do so if the online retailer provides free return insurance.     

VALUATION OF CONTINUOUSLY MONITORED DOUBLE BARRIER OPTIONS AND RELATED SECURITIES

In this paper, we apply Carr's randomization approximation and the operator form of the Wiener‐Hopf method to double barrier options in continuous time. Each step in the resulting backward induction algorithm is solved using a simple iterative procedure that reduces the problem of pricing options with two barriers to pricing a sequence of certain perpetual contingent claims with first‐touch single barrier features. This procedure admits a clear financial interpretation that can be formulated in the language of embedded options. Our approach results in a fast and accurate pricing method that can be used in a rather wide class of Lévy‐driven models including Variance Gamma processes, Normal Inverse Gaussian processes, KoBoL processes, CGMY model, and Kuznetsov's β ‐class. Our method can be applied to double barrier options with arbitrary bounded terminal payoff functions, which, in particular, allows us to price knock‐out double barrier put/call options as well as double‐no‐touch options.     

帧中继网络的一种拥塞控制机制

本文介绍一帧中继网络的拥塞控制机制－输入速率控制机制。它采用漏桶算法调节从源节点到网络节点的数据流量。本文着重介绍了该输入速度控制机制的实现并作了性能分析。依据帧中继子网的拓扩结构，进行理论分析和计算机模拟，给出了不同参数下的各项性能曲线。     

配股融资股票定价模型的实证研究——基于2000年到2005年上市公司经验数据

上市公司为了达到配股的政策要求,往往在配股前进行一系列的盈余管理,从而使公司的净资产收益率满足证监会所要求的配股条件。公司配股的盈余管理往往采用政策诱增式自愿性会计政策变更,因而研究政策性诱增式会计变更所引起的信息噪音对公司配股定价的影响就具有重要的理论和现实意义。实证结果表明:公司的配股定价受到自愿性政策对当期净利润增减变化调整的影响,从而也验证了配股行为的盈余管理多为政策诱增式会计政策变更。     

The Network Industry,Monopoly Regulation,and Social Optimum

This paper examines pricing policy, quality levels, consumer surplus and social welfare for the monopoly and non‐monopoly case. It is shown that given certain realistic assumptions, the network industry under unregulated monopoly would yield more social welfare than in the case of several producers in the industry, and would therefore be socially preferred.     

OPTIMAL MULTIPLE STOPPING AND VALUATION OF SWING OPTIONS

The connection between optimal stopping of random systems and the theory of the Snell envelop is well understood, and its application to the pricing of American contingent claims is well known. Motivated by the pricing of swing contracts (whose recall components can be viewed as contingent claims with multiple exercises of American type) we investigate the mathematical generalization of these results to the case of possible multiple stopping. We prove existence of the multiple exercise policies in a fairly general set-up. We then concentrate on the Black–Scholes model for which we give a constructive solution in the perpetual case, and an approximation procedure in the finite horizon case. The last two sections of the paper are devoted to numerical results. We illustrate the theoretical results of the perpetual case, and in the finite horizon case, we introduce numerical approximation algorithms based on ideas of the Malliavin calculus.     

Pricing Internet access in the presence of user loyalty

Socio-economic aspects of future communication networks such as pricing models for network providers, network neutrality, and Quality of Experience (QoE) are becoming more and more important as the convergence of networks is in progress. All the above areas share a common interest: the deeper understanding of user behavior. In this paper, as a first step towards a more realistic user model, we investigate customer loyalty and its impact on the pricing competition of Internet Service Providers (ISPs) who sell Internet access to end-users. The main contribution of this paper is twofold. First, we analyze the impact of user loyalty with game-theoretical means motivated by the Bertrand game. We show how loyalty introduces a new equilibrium in a repeated game setting resulting in the cooperation of ISPs. Furthermore, we investigate the case of a differentiated customer population by introducing dual reservation values, and show how it leads to new, pure strategy Nash equilibria indicating that ISPs should make the most out of their respective loyal user base. Second, we construct two novel models for customer loyalty incorporating two important aspects of the users’ purchasing decisions: price sensitivity and inherent uncertainty. We evaluate the impact of user loyalty through these models by extensive simulations in a number of relevant scenarios. In particular, we show how the higher level of loyalty in the user population leads to larger profits for ISPs. We argue that our findings can motivate network researchers to incorporate a finer-grained user behavior model in their investigations on pricing models of network services and other socio-economic issues.     

A direct solution method for pricing options in regime‐switching models

Pricing financial or real options with arbitrary payoffs in regime‐switching models is an important problem in finance. Mathematically, it is to solve, under certain standard assumptions, a general form of optimal stopping problems in regime‐switching models. In this article, we reduce an optimal stopping problem with an arbitrary value function in a two‐regime environment to a pair of optimal stopping problems without regime switching. We then propose a method for finding optimal stopping rules using the techniques available for nonswitching problems. In contrast to other methods, our systematic solution procedure is more direct as we first obtain the explicit form of the value functions. In the end, we discuss an option pricing problem, which may not be dealt with by the conventional methods, demonstrating the simplicity of our approach.     

Ascending Combinatorial Auctions with Risk Averse Bidders

Ascending combinatorial auctions are being used in an increasing number of spectrum sales worldwide, as well as in other multi-item markets in procurement and logistics. Much research has focused on pricing and payment rules in such ascending auctions. However, recent game-theoretical research has shown that such auctions can even lead to inefficient perfect Bayesian equilibria with risk-neutral bidders. There is a fundamental free-rider problem without a simple solution, raising the question whether ascending combinatorial auctions can be expected to be efficient in the field. Risk aversion is arguably a significant driver of bidding behavior in high-stakes auctions. We analyze the impact of risk aversion on equilibrium bidding strategies and efficiency in a threshold problem with one global and several local bidders. Due to the underlying free-rider problem, the impact of risk-aversion on equilibrium bidding strategies of local bidders is not obvious. We characterize the necessary and sufficient conditions for the perfect Bayesian equilibria of the ascending auction mechanism to have the local bidders to drop at the reserve price. Interestingly, in spite of the free-riding opportunities of local bidders, risk-aversion reduces the scope of the non-bidding equilibrium. The results help explain the high efficiency of ascending combinatorial auctions observed in the lab.     

Balancing Profitability and Customer Welfare in a Supermarket Chain

We investigate the impact of price discrimination by a large Chicago supermarket chain. First we measure the impact of the chain's current zone-pricing policy on shelf prices, variable profits and consumer welfare across its stores. Using the chain's database to simulate a finer store-specific micro-pricing policy, we study the implications of this policy on profits and welfare. We show how a store-pricing policy that is constrained to offer consumers at least as much surplus as a uniform chain wide pricing policy still enables the retailer to generate substantial incremental profits.To ensure our pricing problem exhibits a well-defined optimum, we use the parsimonious, mixed-logit demand function that allows for flexible substitution patterns across brands and also retains a link to consumer theory. We discuss the issue of price endogeneity when estimating the demand parameters with weekly store-level data. Standard instrumental variables techniques used to account for such endogeneity also seem to increase the magnitudes of own-price elasticities thereby offsetting the problem encountered by previous researchers of predicted prices from a demand model exceeding those in the actual data.     

Compete in Price or Service?—A Study of Personalized Pricing and Money Back Guarantees

Retailers use both pricing and service strategies to respond to intensified competition. Here we develop a duopoly model to investigate the impact of the increasingly popular personalized pricing strategy (PPS) and the widely used Money Back Guarantee (MBG) customer returns policy. We consider two retailers who differ in customer satisfaction rates. Each retailer chooses a pricing strategy, PPS or uniform pricing, and a product return strategy, MBG or ‘no returns.’ We show that both PPS and MBG are dominant strategies, but their impact on retailers’ prices and profits are different; while PPS intensifies price competition and may lead to a prisoner’s dilemma in which both retailers may lose profit, MBG mitigates price competition and may result in a Pareto improvement in both retailers’ profits. Both PPS and MBG increase the size of the overall market, but not the total duopoly profit. The total customer surplus and social welfare may increase under either strategy. In addition, we obtain some interesting observations as to how our results may change if the product quality/customer satisfaction rate is endogenously chosen in the duopoly. Some of our findings are in contrast to related results reported in the literature.