Loan Loss Reserves （LLR）, Expected Loss （EL）, and Value at Risks （VaR）

This paper clarifies the distinctions between loan loss reserves （LLR）, expected loss （EL）, and loan loss provisions （LLP）. The paper also includes information on individual and collective impairment assessment of local commercial banks in Malaysia collected from their annual reports. Most banks have maintained collective assessment （CA） allowance ratio of lower than 1.2% of gross total loans.

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.

Value evaluation of ecological risk of marine chemical spills

The environmental risk of chemical spills near coastal zones threatens abundant marine ecological resources. By appraising the ecological damage value of the environmental risk of marine chemical spills, we can facilitate decision-making for the development of a coastal zone and establishment of economic policy on coastal zone management. In this study, the ecological effect of the environmental risk of a chemical spill in the Haicang chemical industrial park in Xiamen was identified, after which its impact was forecasted and its ecological damage value was assessed. The information generated in this study will be useful in future studies evaluating marine ecological compensation based on environmental risk analysis.

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.

Analysis of Conditional Value-at-Risk for Newsvendor with Holding and Backorder Cost under Market Search

We consider a distribution system with one supplier and two retailers. For the two retailers, they face different demand and are both risk averse. We study a single period model which the supplier has ample goods and the retailers order goods separately. Market search is measured as the fraction of customers who unsatisfied with their ＂local＂ retailer due to stock-out, and search for the goods at the other retailer before leaving the system. We investigate how the retailers game for order quantity in a Conditional Value-at-Risk framework and study how risk averse degree, market search level, holding cost and backorder cost influence the optimal order strategies. Furthermore, we use uniform distribution to illustrate these results and obtain Nash equilibrium of order strategies.

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.

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。

Multi scale risk measurement in electricity market:a wavelet based value at risk approach

Value at risk (VaR) is adopted to measure the risk level in the electricity market. To estimate VaR at higher accuracy and reliability, the wavelet variance decomposed approach for value at risk estimates (WVDVaR) is proposed. Empirical studies conduct in five Australian electricity markets, which evaluate the performances of both the proposed approach and the traditional ARMA-GARCH approach using the Kupiec backtesting procedure. Experimental results suggest that the proposed approach measures electricity market risks at higher accuracy and reliability than the bench mark ARMA-GARCH approach, as indicated by the higher p values during the Kupiec backtesting procedure. In addition, the new approach also provides more insight into the risk evolution process over time and helps in adjusting VaR estimates to the time horizons that best suit investor interests. The distribution of risk according to investor preferences is shown by decomposing VaR across different time horizons. This also provides important information for the appropriate aggregation of risk measures based on investor investment preferences.

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.

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.

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.

Estimation of Dynamic VaR in Chinese Stock Markets Based on Time Scale and Extreme Value Theory

The accuracy and time scale invariance of value-at-risk （VaR） measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory （EVT） is applied to model the extreme tail of standardized residual series of daily/weekly indices losses, and parametric and nonparametric methods are used to estimate parameters of the general Pareto distribution （GPD）, and dynamic VaR for indices of three stock markets in China. The accuracy and time scale invariance of risk measurement methods through back-testing approach are also examined. Results show that not all the indices accept time scale invariance; there are some differences in accuracy between different indices at various confidence levels. The most powerful dynamic VaR estimation methods are EVT-GJR-Hill at 97.5% level for weekly loss to Shanghai stock market, and EVT-GARCH-MLE （Hill） at 99.0% level for weekly loss to Taiwan and Hong Kong stock markets, respectively.

On the factors of Bitcoin’s value at risk

This study investigates the factors of Bitcoin’s tail risk,quantified by Value at Risk(VaR).Extending the conditional autoregressive VaR model proposed by Engle and Manganelli(2004),I examine 30 potential drivers of Bitcoin’s 5%and 1%VaR.For the 5%VaR,quantity variables,such as Bitcoin trading volume and monetary policy rate,were positively significant,but these effects were attenuated when new samples were added.The 5%VaR responds positively to the Internet search index and negatively to the fluctuation of returns on commodity variables and the Chinese stock market index.For the 1%VaR,variables related to the macroeconomy play a key role.The consumer sentiment index exerts a strong positive effect on the 1%VaR.I also find that the 1%VaR has positive relationships with the US economic policy uncertainty index and the fluctuation of returns on the corporate bond index.

Franchise Value Change Information of State-owned Commercial Bank and Securities Investment Risk

The investment strategy choice of state-owned commercial bank is related to its franchise value change information. This paper analyzes the franchise value change information of state-owned commercial bank. The franchise value change information shows that the franchise value of state-owned Commercial Bank is descending. Along with the descending of the franchise value, state-owned commercial bank strengthens its high risk investment motive when it chooses its investment strategy. State-owned commercial bank tends to run the high risk of investing securities because its investment variety is very sparse. Based on the theoretical principle of how to control securities investment risk, this paper proposes some countermeasures and suggestions that state-owned commercial bank strengthen the control of its securities investment risk in order to perfect its investment strategy.

Three Methods to Calculate the Financial Risk Measurement: Value- At-Risk and Expected Shortfall

This paper analyzes the relationship between the risk factor of each stock and the portfolio’s risk based on a small portfolio with four U.S.stocks,and the reason why these risk factors can be regarded as a market invariant.Then,it evaluates the properties of the convex and coherent risk indicators of the capital requirement index composed of VaR and ES,and use three methods(the historical estimation method,boudoukh’s mixed method and Monte Carlo method)to estimate the risk measurement indicators VaR and ES respectively based on the assumption of multivariate normal distribution’risk factors and multivariate student t-copula distribution’s one,finally it figures out that these three calculation results are very close.

The Value Choice for Legal Regulation of Financial Risks in Shadow Banking

It is of vital importance for shadow banking supervision to have correct targets for the curtailment of financial risks.In fact,the process of selecting legal regulation targets for shadow banking financial risks is equivalent to a process of achieving specific goals or objectives by means of legal regulation.The establishment of a regulatory system for shadow banking should consider the objective and practical needs of the sector,prioritize security as the desired value,and reasonably establish a value system for risk control.

Calendar Effects in AAPL Value-at-Risk

This study investigates calendar anomalies： day-of-the-week effect and seasonal effect in the Value-at-Risk （VaR） analysis of stock returns for AAPL during the period of 1995 through 2015. The statistical properties are examined and a comprehensive set of diagnostic checks are made on the two decades of AAPL daily stock returns. Combing the Extreme Value Approach together with a statistical analysis, it is learnt that the lowest VaR occurs on Fridays and Mondays typically. Moreover, high Q4 and Q3 VaR are observed during the test period. These results are valuable for anyone who needs evaluation and forecasts of the risk situation in AAPL. Moreover, this methodology, which is applicable to any other stocks or portfolios, is more realistic and comprehensive than the standard normal distribution based VaR model that is commonly used.

The Application of "Risk=Value*Threat*Vulnerability" in Management Information Systems

With rapid development of the technology, many organizations depend more on the information than before and they will process mountains of information everyday. Hence, an information system is designed to facilitate managing information. Security is applied to insure data confidentiality, integrality and availability. In this process, risk can not be ignored. How can we analyze the degree of the risk we can accept to provide effective protection at reasonable costs with a great return on investment？ Now many methodologies are proposed. In the paper, one method ＂Risk = Value ＊ Threat ＊ Vulnerability＂ is analyzed so that more people can have a basic understanding about the risk.

Risk Sharing Method of PPP Model for Rural Sewage Treatment - Based on Interval Fuzzy Shapley Value

Rural sewage treatment is in need of more capital investment,in which the financing model of PPP(public-private partnership)is able to encourage the investment of social capital in this sector.Risk sharing is one of the core features in the PPP model.In view that the risk loss of projects cannot be accurately estimated,this article describes the uncertainty of risk loss with fuzzy numbers and allocates the distribution of risk loss among the participants of rural sewage treatment PPP projects with interval fuzzy Shapley value to ensure a more reasonable and effective risk distribution.

基于fattailed-garch的VaR模型

利用F atta il GARCH V aR模型在不同分布的假设下建模上证综指收益时间序列,并与常用的正态分布下GARCH V aR模型进行比较,结果表明:广义误差分布及Skew ed t分布下的GARCH V aR模型适合建模上证综指收益时间序列。