THE TRADE DISCOUNT DECISION: A MARKOV CHAIN APPROACH*

Economic theory indicates that higher returns are required from investments that have higher risk. Two major reasons for offering trade discounts are to stimulate sales and to speed up cash receipts. Both sales increases and the earlier receipt of cash impact the risk/return characteristics of a firm. This study uses a Markov chain model to demonstrate how alternative trade credit policies impact the risk and required returns of a firm. The amount of risk and return per credit sale is calculated, and the coefficient of variation is used as a risk/return measure per credit sale. The study concludes that trade discounts can have a favorable impact on the risk/return characteristics of a firm, even in the absence of increased sales volume. Other discount decision factors which are either directly or indirectly determined in this paper include: (1) the average number of periods for which an account is outstanding, (2) the probability of collection and bad-debt losses over the average account period, (3) the average speed of payment, and (4) the average amount of cash tied up in accounts receivable.     

A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models

Multistage clonal expansion (MSCE) models of carcinogenesis are continuous‐time Markov process models often used to relate cancer incidence to biological mechanism. Identifiability analysis determines what model parameter combinations can, theoretically, be estimated from given data. We use a systematic approach, based on differential algebra methods traditionally used for deterministic ordinary differential equation (ODE) models, to determine identifiable combinations for a generalized subclass of MSCE models with any number of preinitation stages and one clonal expansion. Additionally, we determine the identifiable combinations of the generalized MSCE model with up to four clonal expansion stages, and conjecture the results for any number of clonal expansion stages. The results improve upon previous work in a number of ways and provide a framework to find the identifiable combinations for further variations on the MSCE models. Finally, our approach, which takes advantage of the Kolmogorov backward equations for the probability generating functions of the Markov process, demonstrates that identifiability methods used in engineering and mathematics for systems of ODEs can be applied to continuous‐time Markov processes.     

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。     

Landslide Risk Models for Decision Making

This contribution presents a quantitative procedure for landslide risk analysis and zoning considering hazard, exposure (or value of elements at risk), and vulnerability. The method provides the means to obtain landslide risk models (expressing expected damage due to landslides on material elements and economic activities in monetary terms, according to different scenarios and periods) useful to identify areas where mitigation efforts will be most cost effective. It allows identifying priority areas for the implementation of actions to reduce vulnerability (elements) or hazard (processes). The procedure proposed can also be used as a preventive tool, through its application to strategic environmental impact analysis (SEIA) of land-use plans. The underlying hypothesis is that reliable predictions about hazard and risk can be made using models based on a detailed analysis of past landslide occurrences in connection with conditioning factors and data on past damage. The results show that the approach proposed and the hypothesis formulated are essentially correct, providing estimates of the order of magnitude of expected losses for a given time period. Uncertainties, strengths, and shortcomings of the procedure and results obtained are discussed and potential lines of research to improve the models are indicated. Finally, comments and suggestions are provided to generalize this type of analysis.     

Foot-and-Mouth Disease Entrance Assessment Model Through Air Passenger Violations

This article aims to construct a risk model for the prediction of foot-and-mouth disease (FMD) entrance caused by passengers who illegally carry meat products of cloven-hoofed animals through international airports into a country. The risk that meat contaminated with the FMD virus is formulated as the probabilities of FMD factor events (the prevalence of FMD), the commodity factor event (the transportation, storage, and distribution (TSD) factor event), and the passenger event. Data used for analysis were records of illegal meat product carriers from areas A and B intercepted at an international airport in Taiwan. A risk model was proposed to simulate the probability distributions in disease prevalence, probability of FMD virus existing in the meat products after meat processing, and estimation of survival of virus and time period for TSD. The probability of the passenger event was hypothesized with the odds of intercepted passengers and estimated via logistic regression. The results showed that the odds of passengers being intercepted by beagles were higher than those intercepted by Customs. By conducting Monte Carlo simulations, the probability of FMD virus risk caused by FMD factors from area A was 149 times lower than that from area B. The probability of FMD virus risk caused by the passenger event from area A was four times lower than the corresponding probability from area B. The model provides a contribution to FMD prevention and can be a reference for developing models of other diseases.     

A Model of Consumers' Risk Perceptions Toward Recombinant Bovine Growth Hormone (rbGH): The Impact of Risk Characteristics

This study estimates the effect risk characteristics, described as outrage factors by Hadden, have on consumers' risk perceptions toward the food-related biotechnology, recombinant bovine growth hormone (rbGH). The outrage factors applicable to milk from rbGH treated herds are involuntary risk exposure, unfamiliarity with the product's production process, unnatural product characteristics, lack of trust in regulator's ability to protect consumers in the marketplace, and consumers' inability to distinguish milk from rbGH treated herds compared to milk from untreated herds. An empirical analysis of data from a national survey of household food shoppers reveals that outrage factors mediate risk perceptions. The results support the inclusion of outrage factors into the risk perception model for the rbGH product, as they add significantly to the explanatory power of the model and therefore reduce bias compared to a simpler model of attitudinal and demographic factors. The study indicates that outrage factors which have a significant impact on risk perceptions are the lack of trust in the FDA as a food-related information source, and perceiving no consumer benefits from farmers' use of rbGH. Communication strategies to reduce consumer risk perceptions therefore could utilize agencies perceived as more trustworthy and emphasize the benefits of rbGH use to consumers.     

SCIENCE-BASED PRODUCT DEVELOPMENT: AN EMPIRICAL STUDY OF THE MAINFRAME COMPUTER INDUSTRY

This paper reports on the results of an empirical study of product development in a complex and novel environment. The work is based on field investigations of recent product development projects performed by all leading mainframe computer producers. The projects focused on the development of complex products based on advanced technologies and probed deeply into their science base. The results show striking differences in development lead time and research and development productivity between different projects. The analysis relates these performance differences to the process for the integration of new technology. Organizations that emphasize the accumulation of system-level knowledge of product and production process and its use in technology evaluation and selection are associated with high productivity and short development lead times. This appears to have a greater impact on development performance in this novel environment than more traditional factors, such as processes for effective crossfunctional integration and for overlapping problem solving.     

Characterization of Historical Methane Occurrence Frequencies from U.S. Underground Natural Gas Storage Facilities with Implications for Risk Management,Operations, and Regulatory Policy

Defining a baseline for the frequency of occurrences at underground natural gas storage facilities is critical to maintaining safe operation and to the development of appropriate risk management plans and regulatory approaches. Currently used frequency-estimation methods are reviewed and broadened in this article to include critical factors of cause, severity, and uncertainty that contribute to risk. A Bayesian probabilistic analysis characterizes the aleatoric historical occurrence frequencies given imperfect sampling. Frequencies for the three main storage facility types in the United States (depleted oil-and-gas field storage, aquifer storage, solution-mined salt cavern storage) are generally on the order of 3 to 9 × 10^–2 occurrences, of all causes (surface, well integrity, subsurface integrity) and severities (nuisance, serious, catastrophic), per facility-year. Loss of well integrity is associated with many, but not all, occurrences either within the subsurface or from there up to the surface. The probability of one serious or catastrophic leakage occurrence to the ground surface within the next 10 years, assuming constant number of facilities, is approximately 0.1–0.3% for any facility type. Storage operators and industry regulators can use occurrence frequencies, their associated probabilities and uncertainties, and forecasts of severity magnitudes to better prioritize resources, establish a baseline against which progress toward achieving a reduction target could be measured, and develop more effective mitigation/monitoring/reduction programs in a risk management plan.     

基于Logistic生长方程—马尔可夫模型的港口投资风险测度

伴随着我国经济从高速增长逐渐向经济新常态转变,港口的发展也从高速增长阶段逐渐趋近平稳,随之而来的港口风险问题日益突出。由于港口的发展同中国经济发展相似,呈现非常明显的非线性发展特征,这样采用传统的单一模型很难刻画这一现实问题。为此,本文首次构造了Logistic生长方程—马尔可夫组合模型,通过Logistic生长方程生成马尔可夫模型所需要的状态参数,在此基础上构造风险转移概率矩阵,有效的解决了港口投资风险的测度问题。本文选取1985至2014年的样本数据进行验证,结果表明：2016年后港口投资风险会突显出来,并将持续较长一段时期,表明我国港口正处在转型升级的关键时期,需加快从外延式扩张向内涵式发展转变,从粗放式到精细化管理转变。     

Causation Analysis of Risk Coupling of Gas Explosion Accident in Chinese Underground Coal Mines

The coal mine production industry is a complex sociotechnical system with interactive relationships among several risk factors. Currently, causation analysis of gas explosion accidents is mainly focused on the aspects of human error and equipment fault, while neglecting the interactive relationships among risk factors. A new method is proposed through risk coupling. First, the meaning of risk coupling of a gas explosion is defined, and types of risk coupling are classified. Next, the coupled relationship and coupled effects among risk factors are explored through combining the interpretative structural modeling (ISM) and the NK model. Twenty‐eight representative risk factors and 16 coupled types of risk factors are obtained through analysis of 332 gas explosion accidents in coal mines in China. Through the application of the combined ISM–NK model, an eight‐level hierarchical model of risk coupling of a gas explosion accident is established, and the coupled degrees of different types of risk coupling are assessed. The hierarchical model reveals that two of the 28 risk factors, such as state policies, laws, and regulations, are the root risk factors for gas explosions; nine of the 28 risk factors, such as flame from blasting, electric spark, and local gas accumulation, are direct causes of gas explosions; whereas 17 of the risk factors, such as three‐violation actions, ventilation system, and safety management, are indirect ones. A quantitative analysis of the NK model shows that the probability of gas explosion increases with the increasing number of risk factors. Compared with subjective risk factors, objective risk factors have a higher probability of causing gas explosion because of risk coupling.     

A Risk Analysis Model in Concurrent Engineering Product Development

Concurrent engineering has been widely accepted as a viable strategy for companies to reduce time to market and achieve overall cost savings. This article analyzes various risks and challenges in product development under the concurrent engineering environment. A three‐dimensional early warning approach for product development risk management is proposed by integrating graphical evaluation and review technique (GERT) and failure modes and effects analysis (FMEA). Simulation models are created to solve our proposed concurrent engineering product development risk management model. Solutions lead to identification of key risk controlling points. This article demonstrates the value of our approach to risk analysis as a means to monitor various risks typical in the manufacturing sector. This article has three main contributions. First, we establish a conceptual framework to classify various risks in concurrent engineering (CE) product development (PD). Second, we propose use of existing quantitative approaches for PD risk analysis purposes: GERT, FMEA, and product database management (PDM). Based on quantitative tools, we create our approach for risk management of CE PD and discuss solutions of the models. Third, we demonstrate the value of applying our approach using data from a typical Chinese motor company.     

Shortcuts in complex engineering systems: a principal-agent approach to risk management

In this article, we examine the effects of shortcuts in the development of engineered systems through a principal-agent model. We find that occurrences of illicit shortcuts are closely related to the incentive structure and to the level of effort that the agent is willing to expend from the beginning of the project to remain on schedule. Using a probabilistic risk analysis to determine the risks of system failure from these shortcuts, we show how a principal can choose optimal settings (payments, penalties, and inspections) that can deter an agent from cutting corners and maximize the principal's value through increased agent effort. We analyze the problem for an agent with limited liability. We consider first the case where he is risk neutral; we then include the case where he is risk averse.     

Assessing the export trade risk of bluetongue virus serotypes 4 and 8 in France

Bluetongue (BT) causes an economic loss of $3 billion every year in the world. After two serious occurrences of BT (bluetongue virus [BTV] occurrence in 2006 and 2015), France has been controlling for decades, but it has not been eradicated. As the largest live cattle export market in the world, France is also one of the major exporters of breeding animals and genetic materials in the world. The biosafety of its exported cattle and products has always been a concern. The scenario tree quantitative model was used to analyze the risk of BTV release from French exported live cattle and bovine semen. The results showed that with the increase in vaccination coverage rates, the risk decreased. If the vaccine coverage is 0%, the areas with the highest average risk probability of BTV-4 and BTV-8 release from exported live cattle were Haute-Savoie and Puy-de-Dôme, and the risk was 2.96 × 10^–4 and 4.25 × 10^–4, respectively. When the vaccine coverage was 90%, the risk probability of BTV-4 and BTV-8 release from exported live cattle was 2.96 × 10^–5 and 4.24 × 10^–5, respectively. The average probability of BTV-8 release from bovine semen was 1.09 × 10^–10. Sensitivity analysis showed that the probability of false negative polymerase chain reaction (PCR) test and the probability of BT infection in the bull breeding station had an impact on the model. The identification of high-risk areas and the discovery of key control measures provide a reference for decision makers to assess the risk of French exports of live cattle and bovine semen.     

Probabilistic Framework for the Estimation of the Adult and Child Toxicokinetic Intraspecies Uncertainty Factors

Human health risk assessments use point values to develop risk estimates and thus impart a deterministic character to risk, which, by definition, is a probability phenomenon. The risk estimates are calculated based on individuals and then, using uncertainty factors (UFs), are extrapolated to the population that is characterized by variability. Regulatory agencies have recommended the quantification of the impact of variability in risk assessments through the application of probabilistic methods. In the present study, a framework that deals with the quantitative analysis of uncertainty (U) and variability (V) in target tissue dose in the population was developed by applying probabilistic analysis to physiologically-based toxicokinetic models. The mechanistic parameters that determine kinetics were described with probability density functions (PDFs). Since each PDF depicts the frequency of occurrence of all expected values of each parameter in the population, the combined effects of multiple sources of U/V were accounted for in the estimated distribution of tissue dose in the population, and a unified (adult and child) intraspecies toxicokinetic uncertainty factor UFH-TK was determined. The results show that the proposed framework accounts effectively for U/V in population toxicokinetics. The ratio of the 95th percentile to the 50th percentile of the annual average concentration of the chemical at the target tissue organ (i.e., the UFH-TK) varies with age. The ratio is equivalent to a unified intraspecies toxicokinetic UF, and it is one of the UFs by which the NOAEL can be divided to obtain the RfC/RfD. The 10-fold intraspecies UF is intended to account for uncertainty and variability in toxicokinetics (3.2x) and toxicodynamics (3.2x). This article deals exclusively with toxicokinetic component of UF. The framework provides an alternative to the default methodology and is advantageous in that the evaluation of toxicokinetic variability is based on the distribution of the effective target tissue dose, rather than applied dose. It allows for the replacement of the default adult and children intraspecies UF with toxicokinetic data-derived values and provides accurate chemical-specific estimates for their magnitude. It shows that proper application of probability and toxicokinetic theories can reduce uncertainties when establishing exposure limits for specific compounds and provide better assurance that established limits are adequately protective. It contributes to the development of a probabilistic noncancer risk assessment framework and will ultimately lead to the unification of cancer and noncancer risk assessment methodologies.     

基于时变跳跃次数的基准利率风险测算研究

各国央行包括美联储的利率调整和变动就是基准利率风险.基准利率的变化势必要导致金融资产定价的变动和风险溢价.本文通过自回归模型AR测算随时间变化的利率跳跃次数,确定基准利率跳跃的概率,利用伽马分布和正态分布分别测算基准利率跳跃的时间与幅度,根据利率跳跃的概率、时间和幅度确定基准利率跳跃风险溢价,建立基于时变跳跃次数的基准利率跳跃风险溢价测算模型,并利用中国上海证券交易所国债7天回购利率数据进行实证研究.本文创新与特色:1)通过自回归模型AR测算时变的利率跳跃次数,测算利率发生跳跃的概率,确定基准利率跳跃风险溢价,揭示跳跃次数的动态变化规律,反映历史利率跳跃行为对未来利率跳跃行为的影响,改变现有研究以常数跳跃次数测算利率跳跃概率、无法真实反映利率跳跃的频繁程度,导致利率跳跃概率及利率跳跃风险溢价测算不准的弊端.2)研究表明,现有研究的常数跳跃次数仅仅是本文跳跃次数测算模型在参数ρ、γ等于0时的特例.3)通过利率跳跃的概率、时间和幅度确定基准利率的跳跃风险溢价,解决基准利率跳跃风险补偿的测算问题.     

带有马尔可夫竞争因子的多产品报童模型

在竞争的环境下考虑多产品报童问题。为了刻画多产品间的竞争关系，本文利用马尔可夫链构建多产品的需求转移模型，并根据模型的转移概率矩阵提出马氏竞争因子和综合竞争力等核心概念，进而结合卷积公式和雅可比行列式，将竞争因子的概念纳入经典报童模型的框架，构建了带有马氏竞争因子的多产品报童模型，同时在该模型的理论基础上给出了新的决策机制。模型的数据实验表明：竞争因子对多产品库存系统的最优订购策略具有重要的影响，关于产品的综合竞争力为单调递增函数；将竞争因子纳入多产品库存系统的决策机制，可进一步拓展报童模型在实际应用领域中的适应性以及提高库存系统的优化与控制的效用。     

Probabilistic Analysis of Dam Accidents Worldwide: Risk Assessment for Dams of Different Purposes in OECD and Non-OECD Countries with Focus on Time Trend Analysis

This study presents probabilistic analysis of dam accidents worldwide in the period 1911–2016. The accidents are classified by the dam purpose and by the country cluster, where they occurred, distinguishing between the countries of the Organization for Economic Cooperation and Development (OECD) and nonmember countries (non-OECD without China). A Bayesian hierarchical approach is used to model distributions of frequency and severity for accidents. This approach treats accident data as a multilevel system with subsets sharing specific characteristics. To model accident probabilities for a particular dam characteristic, this approach samples data from the entire data set, borrowing the strength across data set and enabling to model distributions even for subsets with scarce data. The modelled frequencies and severities are combined in frequency-consequence curves, showing that accidents for all dam purposes are more frequent in non-OECD (without China) and their maximum consequences are larger than in OECD countries. Multipurpose dams also have higher frequencies and maximum consequences than single-purpose dams. In addition, the developed methodology explicitly models time dependence to identify trends in accident frequencies over the analyzed period. Downward trends are found for almost all dam purposes confirming that technological development and implementation of safety measures are likely to have a positive impact on dam safety. The results of the analysis provide insights for dam risk management and decision-making processes by identifying key risk factors related to country groups and dam purposes as well as changes over time.     

On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment

A central part of probabilistic public health risk assessment is the selection of probability distributions for the uncertain input variables. In this paper, we apply the first-order reliability method (FORM)^(1–3) as a probabilistic tool to assess the effect of probability distributions of the input random variables on the probability that risk exceeds a threshold level (termed the probability of failure) and on the relevant probabilistic sensitivities. The analysis was applied to a case study given by Thompson et al. ⁽⁴⁾ on cancer risk caused by the ingestion of benzene contaminated soil. Normal, lognormal, and uniform distributions were used in the analysis. The results show that the selection of a probability distribution function for the uncertain variables in this case study had a moderate impact on the probability that values would fall above a given threshold risk when the threshold risk is at the 50th percentile of the original distribution given by Thompson et al. ⁽⁴⁾ The impact was much greater when the threshold risk level was at the 95th percentile. The impact on uncertainty sensitivity, however, showed a reversed trend, where the impact was more appreciable for the 50th percentile of the original distribution of risk given by Thompson et al. ⁴ than for the 95th percentile. Nevertheless, the choice of distribution shape did not alter the order of probabilistic sensitivity of the basic uncertain variables.