基于极值谱风险测度的动态保证金水平设定

动态保证金是国际期货保证金制度发展的趋势,既顾及交易所的风险态度和有效地控制市场风险、又考虑到投资者的资金成本与效率是合理设定保证金水平的出发点.以交易活跃度为原则,选取1999年1月4日～2008年4月1日上海期货交易所的铜、大连商品交易所的大豆、郑州商品交易所的硬麦期货报酬率连续序列为样本,基于极值POT模型,利用谱风险测度模型对期货价格极端波动下设定动态保证金水平做实证研究,然后做回溯测试,并与用VaR和ES方法设定的保证金水平及现行静态保证金水平进行比较.研究结果表明,用极值POT模型可以很好地描述期货价格极端波动下报酬率序列的尾部特征,选择交易所风险厌恶参数为0.005计算极值谱风险动态保证金水平能对期货价格极端波动下的实时风险进行有效的控制,这为合理设定保证金水平的研究提供了新思路和新方法.     

基于EVT-POT-SV-MT模型的极值风险度量

针对金融资产收益的异常变化,采用SV-MT模型对风险资产的预期收益做风险补偿并捕捉收益序列的厚尾性、波动的异方差性等特征,将收益序列转化为标准残差序列,通过SV-MT模型与极值理论相结合拟合标准残差的尾部分布,建立了一种新的金融风险度量模型——基于EVT-POT-SV-MT的动态VaR模型.通过该模型对上证综指做实证分析,结果表明该模型能够合理有效地度量上证综指收益的风险.     

Risk Analysis of Chemical, Biological, or Radionuclear Threats: Implications for Food Security

If the food sector is attacked, the likely agents will be chemical, biological, or radionuclear (CBRN). We compiled a database of international terrorist/criminal activity involving such agents. Based on these data, we calculate the likelihood of a catastrophic event using extreme value methods. At the present, the probability of an event leading to 5,000 casualties (fatalities and injuries) is between 0.1 and 0.3. However, pronounced, nonstationary patterns within our data suggest that the "reoccurrence period" for such attacks is decreasing every year. Similarly, disturbing trends are evident in a broader data set, which is nonspecific as to the methods or means of attack. While at the present the likelihood of CBRN events is quite low, given an attack, the probability that it involves CBRN agents increases with the number of casualties. This is consistent with evidence of "heavy tails" in the distribution of casualties arising from CBRN events.     

极端风险下中国股市的反应特征研究

投资者是否理性在金融理论中至关重要。本文利用日度交易价格数据,对中国股票市场在极端风险下的反应特征进行了统计实证研究。基于2002-2013年的数据发现,中国股票市场在极端风险下会表现出一些有趣且显著的反应特征。具体表现为:1.在股市出现极端下跌情形下,市场会由于过度反应而在接下来的一个交易日内表现出显著的反转特征;而在股市出现极端上涨情形下,市场在接下来的交易日内并没有明显的反应模式;2.与熊市相比,在出现极端下跌风险下,市场在牛市中更容易出现过度反应而表现出反转特征。实证研究结果还发现,基于极端风险构建的投资交易策略要显著的优于有效市场假说的买入并持有策略。本文的实证研究结果不仅表明投资者的非理性特征,而且还表明这种非理性特征已经影响到资产的定价。     

Risk of Extreme Events in Multiobjective Decision Trees Part 2. Rare Events

Earlier work with decision trees identified nonseparability as an obstacle to minimizing the conditional expected value, a measure of the risk of extreme events, by the well-known method of averaging out and folding back. This second of two companion papers addresses the conditional expected value that is defined as the expected outcome assuming that a random variable is observed only in the upper 100 (1 −α) percent of potential outcomes, where α is a cumulative probability preselected by the decision maker. An approach is proposed to overcome the need to evaluate all policies in order to identify the optimal policy. The approach is based in part on approximating the conditional expected value by using statistics of extremes. An existing convenient approximation of the conditional expected value is shown to be separable into two constituent elements of risk and can thus be optimized, along with other objectives including the unconditional expected value of the outcome, in a multiobjective decision tree. An example of sequential decision making for remediation or environmental contamination is provided. The importance of the results for risk analyis beyond the minimization of conditional expected values is pointed out.     

Risk of Extreme Events in Multiobjective Decision Trees Part 1. Severe Events

Earlier work with decision trees identified nonseparability as an obstacle to minimizing the conditional expected value, a measure of the risk of extreme events, by the well-known method of averaging out and folding back. This first of two companion papers addresses the conditional expected value that is defined as the expected outcome assuming the exceedance of a threshold β, where β is preselected by the decision maker. An approach is proposed to overcome the need to evaluate all policies in order to identify the optimal policy. The approach is based on the insight that the conditional expected value is separable into two constituent elements of risk and can thus be optimized along with other objectives, including the unconditional expected value of the outcome, by using a multiobjective decision tree. An example of sequential decision making for improving highway capacity is given.     

Robustness of Optimal Investment Decisions in Mixed Insurance/Investment Cyber Risk Management

An integrated risk management strategy, combining insurance and security investments, where the latter contribute to reduce the insurance premium, is investigated to assess whether it can lead to reduced overall security expenses. The optimal investment for this mixed strategy is derived under three insurance policies, covering, respectively, all the losses (total coverage), just those below the limit of maximum liability (partial coverage), and those above a threshold but below the maximum liability (partial coverage with deductibles). Under certain conditions (e.g., low potential loss, or either very low or very high vulnerability), the mixed strategy reverts however to insurance alone, because investments do not provide an additional benefit. When the mixed strategy is the best choice, the dominant component in the overall security expenses is the insurance premium in most cases. Optimal investment decisions require an accurate estimate of the vulnerability, whereas larger estimation errors may be tolerated for the investment-effectiveness coefficient.     

应用复合极值理论估计动态流动性调整VaR

本文首次将一分钟内的交易差价(分内价差)的分布和日收益率的分布结合了起来进行分析,应用复合极值理论给出了动态流动性调整的VaR一种估计,同时得到动态流动性调整VaR的预测方法,最后对上海汽车股票(600104)和中国石化(600028)两只股票进行了实证分析。     

Extreme Risk Analysis of Interdependent Economic and Infrastructure Sectors

Willful attacks or natural disasters pose extreme risks to sectors of the economy. An extreme-event analysis extension is proposed for the Inoperability Input-Output Model (IIM) and the Dynamic IIM (DIIM), which are analytical methodologies for assessing the propagated consequences of initial disruptions to a set of sectors. The article discusses two major risk categories that the economy typically experiences following extreme events: (i) significant changes in consumption patterns due to lingering public fear and (ii) adjustments to the production outputs of the interdependent economic sectors that are necessary to match prevailing consumption levels during the recovery period. Probability distributions associated with changes in the consumption of directly affected sectors are generated based on trends, forecasts, and expert evidence to assess the expected losses of the economy. Analytical formulations are derived to quantify the extreme risks associated with a set of initially affected sectors. In addition, Monte Carlo simulation is used to handle the more complex calculations required for a larger set of sectors and general types of probability distributions. A two-sector example is provided at the end of the article to illustrate the proposed extreme risk model formulations.     

A Survey of Approaches for Assessing and Managing the Risk of Extremes

In this paper, we review methods for assessing and managing the risk of extreme events, where extreme events are defined to be rare, severe, and outside the normal range of experience of the system in question. First, we discuss several systematic approaches for identifying possible extreme events. We then discuss some issues related to risk assessment of extreme events, including what type of output is needed (e.g., a single probability vs. a probability distribution), and alternatives to the probabilistic approach. Next, we present a number of probabilistic methods. These include: guidelines for eliciting informative probability distributions from experts; maximum entropy distributions; extreme value theory; other approaches for constructing prior distributions (such as reference or noninformative priors); the use of modeling and decomposition to estimate the probability (or distribution) of interest; and bounding methods. Finally, we briefly discuss several approaches for managing the risk of extreme events, and conclude with recommendations and directions for future research.     

基于马尔科夫随机波动和极值理论的风险测度

针对金融资产波动的时变、聚集以及状态转换等特征,将马尔可夫转换模型和随机波动模型相结合,同时考虑波动尾部的状态分布,构建MSSV-t模型,然后将收益序列转化为标准残差序列,在此基础上,应用EVT模型对标准残差进行建模,进而构建基于MSSV-t-EVT的VaR测度模型,最后对该模型的有效性进行检验。研究发现：MSSV-t-EVT模型能够有效识别上证指数（SSCI）的波动转换特征,并且能合理地测度该指数的收益风险,尤其在高的置信水平下表现更好。研究结论表明MSSV-t-EVT模型能较为准确的刻画股市剧烈波动的事实,可用于交易风险控制和对市场异常波动的预警。     

Cross-country risk quantification of extreme wildfires in Mediterranean Europe

We estimate the country-level risk of extreme wildfires defined by burned area (BA) for Mediterranean Europe and carry out a cross-country comparison. To this end, we avail of the European Forest Fire Information System (EFFIS) geospatial data from 2006 to 2019 to perform an extreme value analysis. More specifically, we apply a point process characterization of wildfire extremes using maximum likelihood estimation. By modeling covariates, we also evaluate potential trends and correlations with commonly known factors that drive or affect wildfire occurrence, such as the Fire Weather Index as a proxy for meteorological conditions, population density, land cover type, and seasonality. We find that the highest risk of extreme wildfires is in Portugal (PT), followed by Greece (GR), Spain (ES), and Italy (IT) with a 10-year BA return level of 50'338 ha, 33'242 ha, 25'165 ha, and 8'966 ha, respectively. Coupling our results with existing estimates of the monetary impact of large wildfires suggests expected losses of 162–439 million € (PT), 81–219 million € (ES), 41–290 million € (GR), and 18–78 million € (IT) for such 10-year return period events.

SUMMARY

We model the risk of extreme wildfires for Italy, Greece, Portugal, and Spain in form of burned area return levels, compare them, and estimate expected losses.     

基于分块极大值模型的商业银行操作风险计量研究

尽管高级计量法由于具有计算精确和节约监管资本等优点而被多数商业银行所青睐,但对于采用哪一种方法来刻画低频高危的操作风险尾部数据却没有一致认识。本文根据巴塞尔委员会关于操作风险计量的原则,采用分块极大值方法和概率加权矩参数估计法,对中国商业银行1990—2009年间的操作风险数据进行了实证。从图形检验和数值检验结果来看,该模型估计的参数具有较高的拟合优度,能够较好地拟合操作风险极端值的尾部分布,为商业银行计量操作风险资本提供了较高的参考价值。     

Optimal and Acceptable Technical Facilities Involving Risks

Economic cost-benefit optimization of technical facility requires suitable "life saving cost" and/or an appropriate acceptance criterion if human life and limb are at risk. Traditionally, acceptance criteria implicit in codes of practice, standards, or regulations for well-defined fields of application are calibrated against past and present practice. This is all but satisfying. It is unclear whether present rules are already optimal. Extrapolations into new fields of application are extremely difficult. Direct cost-benefit analysis is proposed as an alternative. Based on the recently proposed "life quality index" (LQI), a rational acceptance criterion and so-called life saving cost are derived. The classical life quality index is reviewed, modified, and imbedded in modern economics theory. The results are then applied to technical facilities. The relation between optimization and the LQI-based acceptance criterion is discussed. The relevant economics literature is reviewed with respect to discount rates applicable for long-term investments into risk reduction. They should be as low as possible according to a recent mathematical result. Modern economic growth theory decomposes the output growth rate into the rate of time preference of consumption and the rate of economical growth multiplied by the elasticity of marginal utility of consumption. It is found that the rate of time preference of consumption should be a little larger than the long-term population growth rate. The public benefit rate (output growth rate) on the other hand should be smaller than the sum of the population growth rate and the long-term growth rate of a national economy, which is around 2% for most industrial countries. Accordingly, the rate of time preference of consumption is about 1%, which is also intergenerationally acceptable from an ethical point of view. Given a certain output growth rate there is a corresponding maximum financial interest rate in order to maintain nonnegativity of the objective function at the optimum. Finally, a simple demonstration example is added.     

Economic Costs of Misinforming About Risk: The EDB Scare and the Media

This study reports results of an analysis of consumer responses to news reports of grain-product contamination by the pesticide ethylene dibromide (EDB). The results demonstrate that it is possible to quantify market disruption related to the dissemination of risk information. Implications include the need for increased awareness among risk managers that public perceptions, regardless of their objective accuracy, can induce real economic costs. Such costs should be considered in designing regulatory and information policies.     

Quantile Uncertainty and Value‐at‐Risk Model Risk

This article develops a methodology for quantifying model risk in quantile risk estimates. The application of quantile estimates to risk assessment has become common practice in many disciplines, including hydrology, climate change, statistical process control, insurance and actuarial science, and the uncertainty surrounding these estimates has long been recognized. Our work is particularly important in finance, where quantile estimates (called Value‐at‐Risk) have been the cornerstone of banking risk management since the mid 1980s. A recent amendment to the Basel II Accord recommends additional market risk capital to cover all sources of “model risk” in the estimation of these quantiles. We provide a novel and elegant framework whereby quantile estimates are adjusted for model risk, relative to a benchmark which represents the state of knowledge of the authority that is responsible for model risk. A simulation experiment in which the degree of model risk is controlled illustrates how to quantify Value‐at‐Risk model risk and compute the required regulatory capital add‐on for banks. An empirical example based on real data shows how the methodology can be put into practice, using only two time series (daily Value‐at‐Risk and daily profit and loss) from a large bank. We conclude with a discussion of potential applications to nonfinancial risks.     

中国A股市场金融板块间风险相依关系及动态演化研究

将中国金融板块细分为国有大型银行、全国性股份制银行、城商行、证券、保险和信托等6个金融子板块,并以2015年中国股市异动和2019年新冠肺炎疫情为研究背景,分析在两个场景下的不同时期内,6个金融板块间的风险相依关系及其动态演化。通过计算各板块间波动指数的互信息,构建金融板块风险相依关系网络,并使用最大生成树刻画该相依关系的核心结构。研究发现,在市场处于相对平静时期,银行类金融板块与非银行类金融板块二者彼此之间的风险关联较弱,处于相对割裂状态;在市场走势波动较大时,银行类金融板块与非银行类金融板块之间的风险关联程度增强,且保险板块成为重要的中间节点;在两个场景下的异动期和疫情期,国有大型银行板块和城商行板块分别成为最大的风险节点。     

Functional Approximation and Optimal Specification of the Mechanical Risk Index

The mechanical risk index (MRI) is a numerical measure that quantifies the complexity of drilling a well. The purpose of this article is to examine the role of the component factors of the MRI and its structural and parametric assumptions. A meta-modeling methodology is applied to derive functional expressions of the MRI, and it is shown that the MRI can be approximated in terms of a linear functional. The variation between the MRI measure and its functional specification is determined empirically, and for a reasonable design space, the functional specification is shown to a good approximating representation. A drilling risk index is introduced to quantify the uncertainty in the time and cost associated with drilling a well. A general methodology is outlined to create an optimal MRI specification.     

非奇次空间动态极值估计的模型与应用

传统EVT方法是从静态的角度,研究超额数据的性质。然而,它没有同时考虑极端数据发生的时间所隐含的充分信息。本文首次在国内提出了非奇次空间动态极值理论(ITD-EVT)的概念,克服了EVT的上述缺陷,在极端数据的基础上考虑了时间因素,并引入多个解释变量,使极值分布的是三个参数为时变的,用二维泊松分布过程建立动态空间模型,是文中一大特色。把TD-EVT运用于极端情况下风险值的估计中,对金融风险管理、资产定价等问题有较大的理论和现实意义。     

Practice-Specific Risk Perceptions and Self-Reported Food Safety Practices

The relationship between risk perception and risk avoidance is typically analyzed using self-reported measures. However, in domains such as driving or food handling, the validity of responses about usual behavior is threatened because people think about the situations in which they are self-aware, such as when they encounter a hazard. Indeed, researchers have often noted a divergence between what people say about their behavior and how they actually behave. Thus, in order to draw conclusions about risk perceptions and risk avoidance from survey data, it is important to identify particular cognitive elements, such as those measured by questions about risk and safety knowledge, risk perceptions, or information search behavior, which may be effective antecedents of self-reported safety behavior. It is also important to identify and correct for potential sources of bias that may exist in the data. The authors analyze the Food and Drug Administration's 1998 Food Safety Survey to determine whether there are consistent cognitive antecedents for three types of safe food practices: preparation, eating, and cooling of foods. An assessment of measurement biases shows that endogeneity of food choices affects reports of food preparation. In addition, response bias affects reports of cooling practices as evidenced by its relation to knowledge and information search, a pattern of cognitive effects unique to cooling practices. After correcting for these biases, results show that practice-specific risk perceptions are the primary cognitive antecedents of safe food behavior, which has implications for the design of effective education messages about food safety.