Beyond Value‐at‐Risk: GlueVaR Distortion Risk Measures

We propose a new family of risk measures, called GlueVaR, within the class of distortion risk measures. Analytical closed‐form expressions are shown for the most frequently used distribution functions in financial and insurance applications. The relationship between GlueVaR, value‐at‐risk, and tail value‐at‐risk is explained. Tail subadditivity is investigated and it is shown that some GlueVaR risk measures satisfy this property. An interpretation in terms of risk attitudes is provided and a discussion is given on the applicability in nonfinancial problems such as health, safety, environmental, or catastrophic risk management.     

基于虚拟变量分位点回归模型的条件VaR估计以及杠杆效应分析

已有成果在研究杠杆效应时大多数都是基于ARCH类模型,从波动率的角度进行分析的。本文应用分位点回归模型以及含有虚拟变量的分位点回归模型分析了"已实现"波动率条件下的CVaR,并尝试从市场风险的角度对杠杆效应进行分析。最后,对中国股票市场进行了实证研究,得到了"已实现"波动率条件下的CVaR估计,并从风险的角度证实了中国股市的市场风险存在杠杆效应。     

General Probability‐Time Tradeoff and Intertemporal Risk‐Value Model

This article proposes an intertemporal risk‐value (IRV) model that integrates probability‐time tradeoff, time‐value tradeoff, and risk‐value tradeoff into one unified framework. We obtain a general probability‐time tradeoff, which yields a formal representation form to reflect the psychological distance of a decisionmaker in evaluating a temporal lottery. This intuition of probability‐time tradeoff is supported by robust empirical findings as well as by psychological theory. Through an explicit formalization of probability‐time tradeoff, an IRV model taking into account three fundamental dimensions, namely, value, probability, and time, is established. The object of evaluation in our framework is a complex lottery. We also give some insights into the structure of the IRV model using a wildcatter problem.     

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.     

An Agent‐Based Model of Evolving Community Flood Risk

Although individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent‐based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near‐miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high‐risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in‐depth behavioral and decision rules at the individual and community level.     

Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location‐Specific Premiums in the Netherlands

As flood risks grow worldwide, a well‐designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood‐loss‐sharing program involving private insurance based on location‐specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS‐based flood model and a stochastic optimization procedure with respect to location‐specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile‐related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures.     

基于CAViaR模型的沪深300股指期货隔夜风险研究

期货隔夜风险的防范历来是投资者关注的热点,本文以沪深300股指期货为研究对象,采用CAViaR模型对普通隔夜风险进行度量,同时还采用新建的CAViaR-EVT模型对极端隔夜风险进行预测,全面地分析了多头VaR和空头VaR在不同分位数的动态变化特征,最后采用Kupiec似然比检验和动态分位数检验对模型进行后测检验。实证结果表明,隔夜收益序列具有右偏、无长期记忆性和尖峰厚尾等典型特征;CAViaR模型对股指期货的普通隔夜风险具有优异的预测能力,其中AS模型的预测效果最好;加入极值理论后,CAViaR-EVT模型同样能很好地刻画极端分位数下隔夜风险的动态演化过程,且其预测结果比EVT和GARCH-EVT模型要更合理。     

Channel Coordination with a Risk‐Neutral Supplier and a Downside‐Risk‐Averse Retailer

We investigate how a supply chain involving a risk‐neutral supplier and a downside‐risk‐averse retailer can be coordinated with a supply contract. We show that the standard buy‐back or revenue‐sharing contracts may not coordinate such a channel. Using a definition of coordination of supply chains proposed earlier by the authors, we design a risk‐sharing contract that offers the desired downside protection to the retailer, provides respective reservation profits to the agents, and accomplishes channel coordination.     

Failure Probability Under Parameter Uncertainty

In many problems of risk analysis, failure is equivalent to the event of a random risk factor exceeding a given threshold. Failure probabilities can be controlled if a decisionmaker is able to set the threshold at an appropriate level. This abstract situation applies, for example, to environmental risks with infrastructure controls; to supply chain risks with inventory controls; and to insurance solvency risks with capital controls. However, uncertainty around the distribution of the risk factor implies that parameter error will be present and the measures taken to control failure probabilities may not be effective. We show that parameter uncertainty increases the probability (understood as expected frequency) of failures. For a large class of loss distributions, arising from increasing transformations of location‐scale families (including the log‐normal, Weibull, and Pareto distributions), the article shows that failure probabilities can be exactly calculated, as they are independent of the true (but unknown) parameters. Hence it is possible to obtain an explicit measure of the effect of parameter uncertainty on failure probability. Failure probability can be controlled in two different ways: (1) by reducing the nominal required failure probability, depending on the size of the available data set, and (2) by modifying of the distribution itself that is used to calculate the risk control. Approach (1) corresponds to a frequentist/regulatory view of probability, while approach (2) is consistent with a Bayesian/personalistic view. We furthermore show that the two approaches are consistent in achieving the required failure probability. Finally, we briefly discuss the effects of data pooling and its systemic risk implications.     

Improving Default Risk Prediction Using Bayesian Model Uncertainty Techniques

Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from “nominal predictions” due to “upsetting events” such as the 2008 global banking crisis.     

Communicating Low‐Probability High‐Consequence Risk,Uncertainty and Expert Confidence: Induced Seismicity of Deep Geothermal Energy and Shale Gas

Subsurface energy activities entail the risk of induced seismicity including low‐probability high‐consequence (LPHC) events. For designing respective risk communication, the scientific literature lacks empirical evidence of how the public reacts to different written risk communication formats about such LPHC events and to related uncertainty or expert confidence. This study presents findings from an online experiment (N = 590) that empirically tested the public's responses to risk communication about induced seismicity and to different technology frames, namely deep geothermal energy (DGE) and shale gas (between‐subject design). Three incrementally different formats of written risk communication were tested: (i) qualitative, (ii) qualitative and quantitative, and (iii) qualitative and quantitative with risk comparison. Respondents found the latter two the easiest to understand, the most exact, and liked them the most. Adding uncertainty and expert confidence statements made the risk communication less clear, less easy to understand and increased concern. Above all, the technology for which risks are communicated and its acceptance mattered strongly: respondents in the shale gas condition found the identical risk communication less trustworthy and more concerning than in the DGE conditions. They also liked the risk communication overall less. For practitioners in DGE or shale gas projects, the study shows that the public would appreciate efforts in describing LPHC risks with numbers and optionally risk comparisons. However, there seems to be a trade‐off between aiming for transparency by disclosing uncertainty and limited expert confidence, and thereby decreasing clarity and increasing concern in the view of the public.     

基于流动性风险的资本资产定价模型

在现有的资产定价理论基础上,研究了考虑流动性风险因素的风险资产定价问题。首先在无套利下对流动性风险进行定价,得到流动性风险的市场价格,进而给出了无风险资产和风险资产的有效前沿。再从风险构成的角度给出了流动性风险的测度和市场价格,推导出两种形式的基于流动性风险的资本资产定价模型(以相对量表示风险的LBCAPM和以绝对量表示风险的LBCAPM)并揭示了资产期望回报的形成过程。最后,介绍了定价模型的应用前景。     

Model Uncertainty and Risk Estimation for Experimental Studies of Quantal Responses

Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.     

Risk Mapping of Groundwater‐Drawdown‐Induced Land Subsidence in Heterogeneous Soils on Large Areas

Groundwater leakage into subsurface constructions can cause reduction of pore pressure and subsidence in clay deposits, even at large distances from the location of the construction. The potential cost of damage is substantial, particularly in urban areas. The large‐scale process also implies heterogeneous soil conditions that cannot be described in complete detail, which causes a need for estimating uncertainty of subsidence with probabilistic methods. In this study, the risk for subsidence is estimated by coupling two probabilistic models, a geostatistics‐based soil stratification model with a subsidence model. Statistical analyses of stratification and soil properties are inputs into the models. The results include spatially explicit probabilistic estimates of subsidence magnitude and sensitivities of included model parameters. From these, areas with significant risk for subsidence are distinguished from low‐risk areas. The efficiency and usefulness of this modeling approach as a tool for communication to stakeholders, decision support for prioritization of risk‐reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned tunnel in Stockholm.     

EGARCH-GED模型在计量中国期货市场风险价值中的应用

根据我国期货市场收益的基本特性,本文从收益的波动性与概率分布出发,建立了能准确度量时变风险价值的EGARCH-GED模型,并与基于正态分布和t分布GARCH模型的风险价值计算效果进行了比较.结果表明,基于EGARCH-GED模型的风险价值能更好地刻画我国期货市场的市场风险.另外,对我国期货市场各交易品种的风险趋势进行了比较和解析.     

基于EVA与经营风险偏好的供应链鲁棒优化模型

在由一个制造商和多个外部供应商构成的多产品、多阶段供应链中,将经济增加值（EVA）作为体现价值创造的绩效指标,并考虑决策者的经营风险偏好,采用已知概率的离散情景描述资本成本与需求的波动情况,利用鲁棒随机规划方法,建立了以价值创造为目标的供应链鲁棒优化模型。应用分析的结果表明,模型能够将供应链的绩效与风险管理结合起来,减少资本成本与需求不确定对目标值的影响,得到具有鲁棒性的最优解,而且越是风险厌恶型的决策者越会为了保持较低的经营风险而放弃较大的EVA值。同时,决策者可选择不同的权重系数侧重于解鲁棒或模型鲁棒,保证供应链运作的鲁棒性,实现价值创造的目标。     

An Empirical Agent‐Based Model to Simulate the Adoption of Water Reuse Using the Social Amplification of Risk Framework

Water reuse can serve as a sustainable alternative water source for urban areas. However, the successful implementation of large‐scale water reuse projects depends on community acceptance. Because of the negative perceptions that are traditionally associated with reclaimed water, water reuse is often not considered in the development of urban water management plans. This study develops a simulation model for understanding community opinion dynamics surrounding the issue of water reuse, and how individual perceptions evolve within that context, which can help in the planning and decision‐making process. Based on the social amplification of risk framework, our agent‐based model simulates consumer perceptions, discussion patterns, and their adoption or rejection of water reuse. The model is based on the “risk publics” model, an empirical approach that uses the concept of belief clusters to explain the adoption of new technology. Each household is represented as an agent, and parameters that define their behavior and attributes are defined from survey data. Community‐level parameters—including social groups, relationships, and communication variables, also from survey data—are encoded to simulate the social processes that influence community opinion. The model demonstrates its capabilities to simulate opinion dynamics and consumer adoption of water reuse. In addition, based on empirical data, the model is applied to investigate water reuse behavior in different regions of the United States. Importantly, our results reveal that public opinion dynamics emerge differently based on membership in opinion clusters, frequency of discussion, and the structure of social networks.     

基于VaR的现金流风险度量模型研究

与金融企业不同,非金融类企业拥有更多非经常交易且不易估值的资产,因此更关注其在将来某一时刻现金流的不确定性.把现金流在险值与金融工程技术中在险值的概念相结合,运用风险敞口模型分析和蒙特卡洛模拟等计量方法,为非金融类公司管理层、投资者和分析家提供一个简单具体而直接的现金流不确定性的评判指标.通过对在险值和现金流在险值的比较以及对现金流在险值度量模型发展脉络的分析,发现现金流在险值技术更能刻画出非金融类公司的财务风险;通过引入管理决策风险作为风险因子并改进风险敞口模型,计算样本公司的现金流在险值;进一步提出现金流在险值的应用价值和研究方向.