基于潜伏因子模型分析的新兴市场国家金融传染效应研究

构建了基于资本资产定价模型为基础的潜伏因子模型对金融危机传染效应进行分析,将引起市场收益率波动的因素分解为"共同因子","特质因子"和"传染因子",同时采用迭代累计平方和算法内生性地对金融危机演化的不同阶段进行了时间上的划分.以2008年全球金融危机期间4个主要新兴市场国家的股票市场为对象进行了实证研究,结果表明这些国家均遭受到了不同程度的传染,其中中国和巴西受到的传染较弱,而印度和俄罗斯受到的传染较强.     

Fitness model for tiered structure in the interbank market

Tiered structure is observed in a range of countries' banking systems. In that case, relatively few first‐tier banks are not only interconnected, but are connected with second‐tier banks, whereas second‐tier banks are almost exclusively connected with first‐tier banks. This study uses the theory of complex networks to quantitatively characterize the formation of tiered structure in banking systems. The interbank market network model constructed in this article reproduces tiered structure and various statistical properties, namely, a small‐world property and a disassortative mixing property as well as a reciprocal property. This network modeling of the interbank market could be an efficient way to understand the bank behavior in the interbank market. © 2012 Wiley Periodicals, Inc. Complexity, 2012     

Pricing/inventory decisions and profit shares in a non-integrated marketing channel for a single-period product

We consider the situation in which the manufacturer of a single-period product first sets the unit wholesale price and then the retailer responds with an order size. We present mostly analytical results on the effects of the problem's environmental parameters (such as shortage cost and demand uncertainty) on the optimal decisions (ie, the unit wholesale price and retailer's order size) and on the expected profits of the manufacturer and of the retailer. Some of these effects are counter-intuitive and/or contradict related results published recently for similar models. The most important finding is that demand uncertainty is always harmful to the manufacturer but is very often beneficial to the retailer. This means that when the manufacturer can set the wholesale price, the manufacturer should be much more supportive (or even aggressive) than previously advised towards activities such as market surveys and ‘Quick Response’ that reduce the retailer's market uncertainty; in contrast, the retailer need not be as enthusiastic about these activities.     

An enhanced two-quantile Wilks methodology for engineering uncertainty analysis

Complex computational engineering uncertainty analyses have become more prevalent. When input parameters of such engineering models are uncertain, the output metric's uncertainty distribution is of an unknown parametric form. Since Wilks' method, named after the seminal paper by SS Wilks in 1941 entitled “Determination of sample sizes for setting tolerance limits”, is a nonparametric statistical procedure, it has received renewed interest, in particular in nuclear and chemical safety engineering. Unfortunately, the prevailing Wilks' method applied relies on arbitrary specification of order statistics' ranks with undue influence on the sample size recommendations that follow. Herein, a novel modification of Wilks' method involving two quantiles is proposed resolving that arbitrary rank selection. Together with a confidence level to be exceeded, these quantiles uniquely determine the parameters of an order statistics' beta distribution which drive the selection of symmetric tolerance limits. The modified procedure is demonstrated in two illustrative engineering uncertainty analysis examples drawn from the nuclear and chemical engineering domains.     

On the parametric uncertainty quantification of the Rothermel's rate of spread model

Parametric uncertainty quantification of the Rothermel's fire spread model is presented using the Polynomial Chaos expansion method under a Non-Intrusive Spectral Projection (NISP) approach. Several Rothermel's model input parameters have been considered random with an associated prescribed probability density function. Two different vegetation fire scenarios are considered and NISP method results and performance are compared with four other stochastic methodologies: Sensitivity Derivative Enhance Sampling; two Monte Carlo techniques; and Global Sensitivity Analysis. The stochastic analysis includes a sensitivity analysis study to quantify the direct influence of each random parameter on the solution. The NISP approach achieved performance three orders of magnitude faster than the traditional Monte Carlo method. The NISP capability to perform uncertainty quantification associated with fast convergence makes it well suited to be applied for stochastic prediction of fire spread.     

Uncertainty Principles for the Segal-Bargmann Transform

We recall some properties of the Segal-Bargmann transform; and we establish for this transform qualitative uncertainty principles: local uncertainty principle, Heisenberg uncertainty principle, Donoho-Stark''s uncertainty principle and Matolcsi-Sz\"ucs uncertainty principle.     

Demand uncertainty and supplier's returns policies for a multi-store style-good retailer

As demand uncertainty grows in the marketplace, a critical issue today in most purchase contract negotiations between an independent retailer of a style-good and its supplier is the provision of a returns policy, i.e., a commitment by the supplier to buy back unsold inventory of the good at the end of its selling season. Management science research on the strategic role and optimal design of returns policies has grown in recent years but so far offers little treatment of how exactly the retailer's optimal order quantity decisions are affected by demand uncertainty and how a supplier's returns policy can influence these decisions. Employing the traditional “newsboy problem” modeling framework, the authors investigate these issues considering a supplier who faces a retailer with two or more store outlets with normally distributed and possibly correlated demands. To facilitate their analyses, the authors employ a methodology based on special error function representations of the highly nonlinear objective functions of the retailer and supplier. Utilizing this approach, the authors are able to provide explicit insights into how: (a) the buyer's total order quantity decision is affected by the variability in demand; (b) buyback prices in combination with wholesale prices can influence the buyer's order quantity response to demand uncertainty; (c) demand uncertainty moderates the effects of the buyback and wholesale prices; (d) supplier's optimal combination of actions are affected by demand variability; (e) retailer's and supplier's expected profits behave in response to changes in the supplier's actions under different levels of demand variability.     

Characterizing the elements of Earth's radiative budget: Applying uncertainty quantification to the CESM

Understanding and characterizing sources of uncertainty in climate modeling is an important task. Because of the ever increasing sophistication and resolution of climate modeling it is increasingly important to develop uncertainty quantification methods that minimize the computational cost that occurs when these methods are added to climate modeling. This research explores the application of sparse stochastic collocation with polynomial edge detection to characterize portions of the probability space associated with the Earth's radiative budget in the Community Earth System Model (CESM). Specifically, we develop surrogate models with error estimates for a range of acceptable input parameters that predict statistical values of the Earth's radiative budget as derived from the CESM simulation. We extend these results in resolution from T31 to T42 and in parameter space increasing the degrees of freedom from two to three.     

The Application of Discriminant Analysis to the Prediction of Sales Forecast Uncertainty in New Product Situations

Managers' estimates of sales forecast uncertainty for a new product are critical to an effective risk analysis. This research utilizes discriminant analysis to analyze sales forecast errors in past new-product ventures. Using the past ventures, a linear discriminant function is established from relevant market variables by which future ventures may be allocated to one of two populations depending on the probabilities that their sales forecast uncertainty will be low or high.     

A Formal,Decision-Theoretic Approach to Flexibility and Robustness

Traditional decision-making techniques only deal with a limited type of uncertainty: that which can be foreseen sufficiently to be expressed as a number of alternate moves between which nature will choose. A plan is formulated which specifies how the decision-maker should respond to nature's moves. Such a plan makes no allowance for uncertainty which could not be foreseen. Unforeseeable uncertainty can only be dealt with if the decision-maker's response to nature's moves is not fixed in advance but is itself uncertain. Flexibility is then defined as the entropy of that uncertainty. It is a measure of both the number of alternative sequences of moves which are open to the decision-maker and his attitude to them. Robustness is a way of trading off flexibility against expected value as estimated under foreseeable uncertainty. The cost of flexibility may be estimated and controlled.     

HOW RAPID SHOULD EMISSION REDUCTION BE? A GAME‐THEORETIC APPROACH

Abstract In this paper, we search for multistage realization of international environmental agreements. To analyze countries' incentives and the results of their interactions, we mathematically represent players' strategic preferences and apply a game‐theoretic approach to make predictions about their outcomes. The initial decision on emissions reduction is determined by the Stackelberg equilibrium concept. We generalize Barrett's static “emission” model to a dynamic framework and answer the question “how rapid should the emission reduction be?” It appears that sharper abatement is desirable in the early term, which is similar to the conclusion of the Stern review. Numerical example demonstrates that abatement dynamics of the coalition and the free‐rider differ when discounting of the future payoffs increases. We show that without incentives from external organizations or governments, such pollution reduction path can actually lead to a decline in the agreement's membership size.     

Uncertainty principles for images defined on the square

This paper discusses uncertainty principles of images defined on the square, or, equivalently, uncertainty principles of signals on the 2‐torus. Means and variances of time and frequency for signals on the 2‐torus are defined. A set of phase and amplitude derivatives are introduced. Based on the derivatives, we obtain three comparable lower bounds of the product of variances of time and frequency, of which the largest lower bound corresponds to the strongest uncertainty principles known for periodic signals. Examples, including simulations, are provided to illustrate the obtained results. To the authors' knowledge, it is in the present paper, and for the first time, that uncertainty principles on the torus are studied. Copyright © 2016 John Wiley & Sons, Ltd.     

Dunkl Multiplier Operators on a Class of Reproducing Kernel Hilbert Spaces

We study some class of Dunkl multiplier operators; and we establish for them the Heisenberg-Pauli-Weyl uncertainty principle and the Donoho-Stark''s uncertainty principle. For these operators we give also an application of the theory of reproducing kernels to the Tikhonov regularization on the Sobolev-Dunkl spaces.     

A Mean Variance Approach to the Optimal Machine Maintenance and Replacement Problem

This paper considers a simultaneous maintenance and replacement problem under uncertainty. The effects of maintenance and deterioration are assumed to have a probabilistic effect (of the Markovian type) on a machine's salvage value. This leads to the definition of a non-stationary stochastic process of the machine's salvage value whose mean and variance evolutions are found. These evolutions together with an expected discounted profit functional (as that given by Thompson) allows us to apply the tools of optimal control theory to determine a "certainty-equivalent" maintenance program and the optimum replacement date of the machine. A discussion of the uncertain effects of deterioration and maintenance and managers attitudes towards risk is included as well.     

Uncertainty principles associated with the offset linear canonical transform

As a time‐shifted and frequency‐modulated version of the linear canonical transform (LCT), the offset linear canonical transform (OLCT) provides a more general framework of most existing linear integral transforms in signal processing and optics. To study simultaneous localization of a signal and its OLCT, the classical Heisenberg's uncertainty principle has been recently generalized for the OLCT. In this paper, we complement it by presenting another two uncertainty principles, ie, Donoho‐Stark's uncertainty principle and Amrein‐Berthier‐Benedicks's uncertainty principle, for the OLCT. Moreover, we generalize the short‐time LCT to the short‐time OLCT. We likewise present Lieb's uncertainty principle for the short‐time OLCT and give a lower bound for its essential support.     

Managing inventory and service levels in a safety stock‐based inventory routing system with stochastic retailer demands

The inherent uncertainty in supply chain systems compels managers to be more perceptive to the stochastic nature of the systems' major parameters, such as suppliers' reliability, retailers' demands, and facility production capacities. To deal with the uncertainty inherent to the parameters of the stochastic supply chain optimization problems and to determine optimal or close to optimal policies, many approximate deterministic equivalent models are proposed. In this paper, we consider the stochastic periodic inventory routing problem modeled as chance‐constrained optimization problem. We then propose a safety stock‐based deterministic optimization model to determine near‐optimal solutions to this chance‐constrained optimization problem. We investigate the issue of adequately setting safety stocks at the supplier's warehouse and at the retailers so that the promised service levels to the retailers are guaranteed, while distribution costs as well as inventory throughout the system are optimized. The proposed deterministic models strive to optimize the safety stock levels in line with the planned service levels at the retailers. Different safety stock models are investigated and analyzed, and the results are illustrated on two comprehensively worked out cases. We conclude this analysis with some insights on how safety stocks are to be determined, allocated, and coordinated in stochastic periodic inventory routing problem. Copyright © 2017 John Wiley & Sons, Ltd.     

PRODUCTION COST UNCERTAINTY AND INDUSTRY EQUILIBRIUM FOR NON-RENEWABLE RESOURCE EXTRACTION

In this paper we examine production cost uncertainty in a non-renewable resource industry model. A rational expectations, m-firms industry equilibrium is characterized, and the effects of production cost uncertainty on industry rent and firms' profits are examined.     

Decision making under uncertainty comprising complete ignorance and probability

This paper investigates a model of decision making under uncertainty comprising opposite epistemic states of complete ignorance and probability. In the first part, a new utility theory under complete ignorance is developed that combines Hurwicz–Arrow's theory of decision under ignorance with Anscombe–Aumann's idea of reversibility and monotonicity used to characterize subjective probability. The main result is a representation theorem for preference under ignorance by a particular one-parameter function – the τ-anchor utility function. In the second part, we study decision making under uncertainty comprising an ignorant variable and a probabilistic variable. We show that even if the variables are independent, they are not reversible in Anscombe–Aumann's sense. This insight leads to the development of a new proposal for decision under uncertainty represented by a preference relation that satisfies the weak order and monotonicity assumptions but rejects the reversibility assumption. A distinctive feature of the new proposal is that the certainty equivalent of a mapping from the state space of uncertain variables to the prize space depends on the order in which the variables are revealed. Explicit modeling of the order of variables explains some of the puzzles in multiple-prior model and the models for decision making with Dempster–Shafer belief function.     

Bounds on the value of information in uncertain decision problems II†

In most stochastic decision problems one has the opportunity to collect information that would partially or totally eliminate the inherent uncertainty. One wishes to compare the cost and value of such information in terms of the decision maker's preferences to determine an optimal information gathering plan. The calculation of the value of information generally involves oneor more stochastic recourse problems as well as one or more expected value distribution problems. The difficulty and costs of obtaining solutions to these problems has led to a focus on the development of upper and lower bounds on the various subproblems that yield bounds on the value of information. In this paper we discuss published and new bounds for static problems with linear and concave preference functions for partial and perfect information. We also provide numerical examples utilizing simple production and investment problems that illustrate the calculations involved in the computation of the various bounds and provide a setting for a comparison of the bounds that yields some tentative guidelines for their use. The bounds compared are the Jensen's Inequality bound,the Conditional Jensen's Inequality bound and the Generalized Jensen and Edmundson-Madansky bounds.     

Risk sharing in the supplier relations for the Taiwanese automotive industry

This study attempts to develop a supplier's risk sharing contract to gain an understanding of risk sharing for the automotive industry in Taiwan. The existing research has examined revenue-sharing contracts between retailers and manufacturers. However, the study of suppliers’ risk-sharing contracts between manufacturers and suppliers is neglected. This paper first employs a double moral hazard framework to obtain an optimal contract, and then uses the derived model to establish research hypotheses. The empirical analysis shows that manufacturers offer suppliers a type of supplier's risk sharing contract while maintaining long-term relationships with suppliers. The results also support the hypotheses that manufacturers absorb more risk when the suppliers are more uncertainty, more risk aversion and lower moral hazard, and suggest that manufacturers would be willing to absorb more risk as they deepen their involvement in the technological development of suppliers.