Market Risk Evaluation on Single Futures Contract:SV-CVaR Model and Its Application on Cu00 Data

A new stochastic volatility（SV）method to estimate the conditional value at risk（CVaR）is put forward.Firstly,it makes use of SV model to forecast the volatility of return.Secondly,the Markov chain Monte Carlo（MCMC）simulation and Gibbs sampling have been used to estimate the parameters in the SV model.Thirdly,in this model,CVaR calculation is immediate.In this way,the SV-CVaR model overcomes the drawbacks of the generalized autoregressive conditional heteroscedasticity value at risk（GARCH-VaR）model.Empirical study suggests that this model is better than GARCH-VaR model in this field.

Conditional Value-at-Risk for Random Immediate Reward Variables in Markov Decision Processes

We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional value-at-risk for random immediate reward variables in Markov decision processes, under whose risk measure criteria the risk-optimal policies are characterized by the optimality equations for the discounted or average case. As an application, the inventory models are considered.

Risk measurement of international oil and gas projects based on the Value at Risk method

International oil and gas projects feature high capital-intensity, high risks and contract diversity. Therefore, in order to help decision makers make more reasonable decisions under uncertainty, it is necessary to measure the risks of international oil and gas projects. For this purpose, this paper constructs a probabilistic model that is based on the traditional economic evaluation model, and introduces value at risk(VaR) which is a valuable risk measure tool in finance, and applies Va R to measure the risks of royalty contracts, production share contracts and service contracts of an international oil and gas project. Besides, this paper compares the influences of different risk factors on the net present value(NPV) of the project by using the simulation results. The results indicate:(1) risks have great impacts on the project's NPV, therefore, if risks are overlooked, the decision may be wrong.(2) A simulation method is applied to simulate the stochastic distribution of risk factors in the probabilistic model. Therefore, the probability is related to the project's NPV, overcoming the inherent limitation of the traditional economic evaluation method.(3) VaR is a straightforward risk measure tool, and can be applied to evaluate the risks of international oil and gas projects. It is helpful for decision making.

Continuous-Time Models for Firm Valuation and Their Collateral Effect on Risk-Neutral Probabilities and No-Arbitraging Principle

Extensions of Merton’s model(EMM)considering the firm’s payments and generating new types of firm value distribution are suggested.In the open log-value/time space,these distributions evolve from initially normal to negatively skewed ones,and their means are concave-down functions of time.When payments are set to zero or proportional to the firm value,EMM turns into the Geometric Brownian model(GBM).We show that risk-neutral probabilities(RNPs)and the no-arbitraging principle(NAP)follow from GBM.When firm’s payments are considered,RNPs and NAP hold for the entire market for short times only,but for long-term investments,RNPs and NAP just temporarily hold for individual stocks as far as mean year returns of the firms issuing those stocks remain constant,and fail when the mean year returns decline.The developed method is applied to firm valuation to derive continuous-time equations for the firm present value and project NPV.

基于经验似然方法的Value-at-Risk估计

VaR(Value at Risk)是一种利用统计知识度量金融风险的方法,合理地确定GARCH模型是VaR计算的关键。针对这个问题,利用经验似然方法来估计VaR。模拟分析表明,经验似然方法比已有的方法简洁有效。

STUDY ON THE INTERRELATION OF EFFICIENT PORTFOLIOS AND THEIR FRONTIER UNDER t DISTRIBUTION AND VARIOUS RISK MEASURES

In order to study the effect of different risk measures on the efficient portfolios （fron- tier） while properly describing the characteristic of return distributions in the stock market, it is assumed in this paper that the joint return distribution of risky assets obeys the multivariate t-distribution. Under the mean-risk analysis framework, the interrelationship of efficient portfolios （frontier） based on risk measures such as variance, value at risk （VaR）, and expected shortfall （ES） is analyzed and compared. It is proved that, when there is no riskless asset in the market, the efficient frontier under VaR or ES is a subset of the mean-variance （MV） efficient frontier, and the efficient portfolios under VaR or ES are also MV efficient; when there exists a riskless asset in the market, a portfolio is MV efficient if and only if it is a VaR or ES efficient portfolio. The obtained results generalize relevant conclusions about investment theory, and can better guide investors to make their investment decision.

中国碳市场、能源市场和高排放行业间的溢出效应研究——基于TVP-VAR-DY模型

在以低碳发展应对全国气候变化的共识下,继电力市场后,将其他高排放行业纳入全国碳市场势在必行。探究2021年至2023年期间全国碳市场、能源市场和高排放行业市场间的风险溢出效应。通过采用TVP-VAR-DY模型,使动态溢出效应的测度更加平滑。研究发现,全国碳市场、能源市场与高排放行业市场存在时变的双向不对称溢出效应,特别是在极端风险事件下,市场之间的波动溢出效应显著增强。电力等高排放行业市场主要受碳市场波动的影响,而新能源行业风险主要影响原油期货市场。重大社会经济事件可导致能源市场在短期内成为风险传递的主导因素,但随着时间推移,高排放行业会转为风险输出者。最后,提出了关于中国碳市场建设、能源市场风险防范和高排放行业能源结构优化等方面的建议。

A Value-at-Risk Based Approach for PMU Placement in Distribution Systems

With the application of phasor measurement units(PMU)in the distribution system,it is expected that the performance of the distribution system state estimation can be improved obviously with the PMU measurements into consideration.How to appropriately place the PMUs in the distribution is therefore become an important issue due to the economical consideration.According to the concept of efficient frontier,a value-at-risk based approach is proposed to make optimal placement of PMU taking account of the uncertainty of measure errors,statistical characteristics of the pseudo measurements,and reliability of the measurement instrument.The reasonability and feasibility of the proposed model is illustrated with 12-node system and IEEE-33 node system.Simulation results indicated that uncertainties of measurement error and instrument fault result in more PMU to be installed,and measurement uncertainty is the main affect factor unless the fault rate of PMU is quite high.

地缘政治风险对中国农产品期货收益率的影响研究:基于TVP-VAR-SV模型的分析

本文使用TVP-VAR-SV模型,探讨中美地缘政治风险对中国农产品期货收益率的时变影响。研究结果表明:在短期内,地缘政治风险对农产品期货收益率的影响较为明显,而在中长期内,农产品期货收益率波动相对较小。不同农产品期货对地缘政治风险冲击反应不同,大豆、豆粕收益率受到地缘政治风险冲击波动最激烈,其他六种农产品期货则呈现出周期性特点。克里米亚事件、中美贸易摩擦和全球新冠疫情,三个不同时期的极端事件对农产品期货市场具有负向影响,不同期货品种受到的冲击存在异质性与滞后性。

Empirical Analysis of Value-at-Risk Estimation Methods Using Extreme Value Theory

This paper investigates methods of value-at-risk (VaR) estimation using extreme value theory (EVT). It compares two different estimation methods, 'two-step subsample bootstrap' based on moment estimation and maximum likelihood estimation (MLE), according to their theoretical bases and computation procedures. Then, the estimation results are analyzed together with those of normal method and empirical method. The empirical research of foreign exchange data shows that the EVT methods have good characters in estimating VaR under extreme conditions and 'two-step subsample bootstrap' method is preferable to MLE.

俄乌冲突对欧盟碳排放权交易市场波动及风险影响研究——基于GARCH-VaR视角

俄乌冲突的爆发对欧盟(EU)碳排放交易体系造成了剧烈冲击,测度俄乌冲突对碳排放交易价格波动和风险水平的冲击具有重要意义。通过设置俄乌冲突前和俄乌冲突后两个情景,基于欧盟碳排放配额期货(EUA)日度数据,采用GARCH-VaR模型来探究俄乌冲突对欧盟碳市场价格波动和市场风险影响,研究证实GARCH-VaR模型可以测度俄乌冲突前后欧盟碳排放权收益率序列的波动特征以及度量市场风险水平,俄乌冲突增加了欧盟碳交易市场的波动及风险水平。

金融杠杆对我国商业银行系统性风险的异质性影响——基于MS-VAR模型的实证研究

银行业作为我国金融业的主体,其杠杆率变化直接影响金融体系的稳定性,而金融杠杆对不同类型商业银行的系统性风险水平也存在较大差异。研究通过分析金融杠杆影响商业银行系统性风险的内在机理,构建金融杠杆与国有商业银行、股份制商业银行和城市商业银行三类银行业系统性风险的指标,并基于MS-VAR模型研究不同状态下金融杠杆对商业银行系统性风险的异质性影响。结果表明,MSIH(3)-VAR(4)模型能够较好地刻画金融杠杆率对不同所有制类型商业银行系统性风险的非线性影响机制。在不同状态下,金融杠杆对商业银行系统性风险的影响程度有显著差异,较经济上行期和过渡期,在经济下行期降低金融杠杆对抑制银行系统性风险的效果更为显著;同时,降低金融杠杆对抑制国有商业银行系统性风险的效果最好,对抑制城市商业银行系统性风险次之,对股份制商业银行系统性风险的抑制效果最弱。

Importance of Generalized Logistic Distribution in Extreme Value Modeling

We consider a problem from stock market modeling, precisely, choice of adequate distribution of modeling extremal behavior of stock market data. Generalized extreme value (GEV) distribution and generalized Pareto (GP) distribution are the classical distributions for this problem. However, from 2004, [1] and many other researchers have been empirically showing that generalized logistic (GL) distribution is a better model than GEV and GP distributions in modeling extreme movement of stock market data. In this paper, we show that these results are not accidental. We prove the theoretical importance of GL distribution in extreme value modeling. For proving this, we introduce a general multivariate limit theorem and deduce some important multivariate theorems in probability as special cases. By using the theorem, we derive a limit theorem in extreme value theory, where GL distribution plays central role instead of GEV distribution. The proof of this result is parallel to the proof of classical extremal types theorem, in the sense that, it possess important characteristic in classical extreme value theory, for e.g. distributional property, stability, convergence and multivariate extension etc.

Operational risk assessment of third-party payment platforms:a case study of China

Operational risk events have severely impacted the development of third-party payment(TPP)platforms,and have even led to a discussion on the operational risk capital charge settlement by relevant international regulators.However,prior studies have mostly focused on qualitative mechanism analysis,and have rarely examined quantitative risk assessment based on actual operational risk events.Therefore,this study attempts to assess the operational risk on TPP platforms in China by constructing a systematic framework incorporating database construction and risk modeling.First,the operational risk database that covers 202 events between Q1,2014,and Q2,2020 is constructed.Then,specific causes are clarified,and the characteristics are analyzed from both the trend and loss severity perspectives.Finally,the piecewise-defined severity distribution based-Loss Distribution Approach(PSD-LDA)with double truncation is utilized to assess the operational risk.Two main conclusions are drawn from the empirical analysis.First,legal risk and external fraud risk are the two main causes of operational risk.Second,the yearly Value at Risk and Expected Shortfall are 724.46 million yuan and 1081.98 million yuan under the 99.9%significance level,respectively.Our results are beneficial for both TPP platform operators and regulators in managing and controlling operational risk.

Calculation of Expected Shortfall for Measuring Risk and Its Applications

Expected shortfall(ES) is a new method to measure market risk. In this paper, an example was presented to illustrate that the ES is coherent but value-at-risk(VaR) is not coherent. Three formulas for calculating the ES based on historical simulation method, normal method and GARCH method were derived. Further, a numerical experiment on optimizing portfolio using ES was provided.

Common Management Process Model of New TQM Based on the Situation Analysis

In the previous study, we suggested the concept of new TQM based on the consideration of basic concept of Quality Control. Also, in the previous study, we suggested the target domains and entities of product and process based on the TQM Matrix and view point of Three Dimensional Unification Value Models for managing quality of organization systems. Furthermore, in the previous study, we suggest the Common Management Process of organizations. Based on the above suggestion, in this paper, we would like to propose the Common Management Process Model of Total Quality Management based on the consideration of situation analysis and more precise definition of TQM Matrix and Three Dimensional Unification Value Model of “Product and Process”. Improvement of quality and efficiency of organization management can be expected by the integration of conventional different management such as quality assurance, quality improvement, risk management, investment individually from the view point of common management process.

Modeling Cyber Loss Severity Using a Spliced Regression Distribution with Mixture Components

Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the whole range of the losses using a standard loss distribution. We tackle this modeling problem by proposing a three-component spliced regression model that can simultaneously model zeros, moderate and large losses and consider heterogeneous effects in mixture components. To apply our proposed model to Privacy Right Clearinghouse (PRC) data breach chronology, we segment geographical groups using unsupervised cluster analysis, and utilize a covariate-dependent probability to model zero losses, finite mixture distributions for moderate body and an extreme value distribution for large losses capturing the heavy-tailed nature of the loss data. Parameters and coefficients are estimated using the Expectation-Maximization (EM) algorithm. Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.

基于MS-VAR模型的自由贸易试验区金融风险管理创新效应研究

自由贸易试验区是根据本国(地区)法律法规在本国(地区)境内设立的区域性经济特区,对一国的经济发展具有很强的积极贡献。本文聚焦自由贸易试验区的金融建设,将金融风险划分为银行风险、财政风险、通货膨胀风险,借鉴国际上有代表性的自由贸易试验区的金融风险管理创新,基于MS-VAR模型研究分析我国最具有代表性的上海自由贸易试验区的金融风险及管控问题。研究结果显示,上海自由贸易试验区的金融风险主要处于“中金融风险”区制,在风险转换的过程中存在“棘轮效应”,相比银行风险和通货膨胀风险,财政风险能够更加显著地影响上海自由贸易试验区的金融稳定性。最后,提出采取渐进式开放路径、推进金融风险防范、做好各金融机构内部管理的对策建议。

我国首批公募基础设施REITs的风险评估——基于GARCH-VaR模型的实证研究

当前我国基础设施企业的融资结构正处于巨大变革之中,原有融资渠道有限且创新不足。公募基础设施REITs的出现和发展,在拓宽国内基建公司的投融资渠道的同时也为中小投资人提供了集安全性和盈利性于一体的国际金融市场新投资方式。文章应用GARCH-VaR模型分析了我国首批公募基础设施REITs收益率波动特征,在此基础上计算出首批此类金融产品的VaR值用以度量风险。研究结果表明:T分布下的GARCH-VaR模型的估计有效性最佳;在我国首批9只公募基础设施REITs中,中航首钢绿能REIT风险测度最高,博时蛇口产业园REIT风险测度最低;在四大资产类别中,收费公路类REITs风险测度最高,产业园区类REITs风险测度最低。结合国内外经验及实证结果,文章建议将GARCH-VaR模型纳入我国公募基础设施REITs风险管理体系,并完善其相关制度建设,以防范化解我国金融风险。