浅析认股权三种计量方法的比较

认股权作为一种长期的激励机制,近年来在我国开始得到采用,作为配套会计制度之一的认股权计量问题也日益得到人们的关注。认股权目前常用的三种计量方法有公允价值法、内在价值法和最小价值法。文章主要介绍了这三种计量方法,并分别对其异同进行了比较,以讨论每种计量方法的使用范围。     

浅析环境成本计量的方法——以金桥火力发电厂为例

本文对环境成本计量方法进行了探讨,介绍预防性支出法、市场价值法、机会成本法和人力资本法四种计量方法,并以金桥火力发电厂作为研究案例,分别用上述计量方法对案例的预防成本、运行成本和补偿成本进行计量,用以验证上述计量方法在火力发电行业的适用性。最后,本文指出我国环境成本计量方法的缺陷并提出相应的改进对策,希望能够为我国环境成本计量的理论和实践提供一定的借鉴。     

受险价值管理——国际金融业风险管理的新规范

70年代中期以前,银行业关注的主要风险是信用风险。但近20年来,随着资本市场管理放松和银行业务的全球化,市场风险管理引起国际金融业越来越多的重视。 一、受险价值管理的起源与发展1.银行业对受险价值管理的开发受险价值可以作为一个风险指标,也可以作为一种风险管理方法。受险价值作为风险指     

基于EVA的企业人力资本价值计量

一、人力资本价值计量方法分析现有的人力资本价值计量方法基本上可分为货币性计量方法和非货币性计量方法。其一,人力资本价值货币计量方法分析。目前,对人力资本采用货币为计量尺度的计量模型有很多,但主要可分为不完全的价值计量方法和完全价值计量方法两种。(1)不完全价值计量方法主     

投资性房地产计量模式比较及影响分析

本文介绍了新准则中投资性房地产的两种计量模式，并对两种计量模式的会计处理进行了详细对比，分析了两种计量模式对企业的影响，同时就企业采用公允价值计量模式可能遇到的问题进行了探讨。     

公允价值在投资性房地产中的应用

本文首先阐述了投资性房地产的种类和作用;其次对历史成本模式和公允价值计量模式两种不同计量方法对企业的影响进行深入剖析,并根据统计,分析我国现阶段上市公司的公允价值的执行情况,并指出公允价值计量存在的问题;最后,对公允价值计量的前景进行了展望.     

人力资源价值计量方法探讨

文章首先对目前比较流行的人力资源价值计量方法和模式进行了比较分析,并在此基础上,引入了一种较为理想的人力资源价值计量模式,即人力资源按贡献比率分离与计量模型,并将其特点进行了归纳总结。     

论公允价值计量的发展前景

金融危机爆发后,各国致力于公允价值计量准则的修改与完善。而今,全球公允价值计量准则趋于统一。对公允价值计量进行综述,首先介绍公允价值计量在国际会计准则、美国会计准则、中国会计准则中的发展历程;其次,就公允价值在发展过程中产生的争论及观点进行论述;最后,对公允价值计量的发展趋势及未来应用提出展望。     

新会计准则下会计计量模式比较研究

自2007会计年度开始在上市公司中实施的新会计准则摒弃了原会计准则中单一的历史成本计量模式,而采取包括公允价值在内的多种计量模式。然而两种会计计量模式之间并不是对立的关系,公允价值计量模式不可能完全取代历史成本计量模式。本文在对两种会计计量模式进行对比分析的基础上,对历史成本计量模式和公允价值计量模式的优缺点及适用性进行了初步的探讨。     

公允价值计量属性及其选择问题

公允价值是一种单一计量属性还是一种复合计量属性,国内外尚存在争议。为此,文章从公允价值的定义和公允价值与其它计量属性的关系进行分析,认为公允价值是一种复合计量属性。在此基础上进一步分析了公允价值计量属性选择的影响因素,总结了美国颁布的公允价值计量准则的主要观点,分析了该准则对我国公允价值准则建设的借鉴意义。     

VaR、ES与一致性风险测度

基于分位数的VaR(风险价值)不具有一致性,可能误导投资组合优化和风险管理,ES(预期短缺)测度克服了这一缺点。谱测度和失真风险测度更具一般性,考虑了投资者风险厌恶对风险测度的影响,其中VaR和ES均为其特例。从实用性看,ES仍是业界普遍采用的方法。     

SWARCH模型下的VaR估计

本文将状态转换下的ARCH模型（SWARCH）引入到估计金融资产VaR中，以上证股票指数为例进行实证分析，并与传统GARCH（1，1）模型中正态分布、t分布、GED分布估计的结果进行了比较，实证显示含有状态转换的VaR具有较好的估计效果。     

Value at risk models for volatile emerging markets equity portfolios

This paper investigates the issue of market risk quantification for emerging and developed market equity portfolios. A very wide spectrum of popular and widely used in practice Value at Risk (VaR) models are evaluated and compared with Extreme Value Theory (EVT) and adaptive filtered models, during normal, crises, and post-crises periods. The results are interesting and indicate that despite the documented differences between emerging and developed markets, the most successful VaR models are common for both asset classes. Furthermore, in the case of the (fatter tailed) emerging market equity portfolios, most VaR models turn out to yield conservative risk forecasts, in contrast to developed market equity portfolios, where most models underestimate the realized VaR. VaR estimation during periods of financial turmoil seems to be a difficult task, particularly in the case of emerging markets and especially for the higher loss quantiles. VaR models seem to be affected less by crises periods in the case of developed markets. The performance of the parametric (non-parametric) VaR models improves (deteriorates) during post-crises periods due to the inclusion of extreme events in the estimation sample.     

Impact of value-at-risk models on market stability

Financial institutions around the world use value-at-risk (VaR) models to manage their market risk and calculate their capital requirements under Basel Accords. VaR models, as any other risk management system, are meant to keep financial institutions out of trouble by, among other things, guiding investment decisions within established risk limits so that the viability of a business is not put unduly at risk in a sharp market downturn. However, some researchers have warned that the widespread use of VaR models creates negative externalities in financial markets, as it can feed market instability and result in what has been called endogenous risk, that is, risk caused and amplified by the system itself, rather than being the result of an exogenous shock. This paper aims at analyzing the potential of VaR systems to amplify market disturbances with an agent-based model of fundamentalist and technical traders which manage their risk with a simple VaR model and must reduce their positions when the risk of their portfolio goes above a given threshold. We analyse the impact of the widespread use of VaR systems on different financial instability indicators and confirm that VaR models may induce a particular price dynamics that rises market volatility. These dynamics, which we have called `VaR cycles’, take place when a sufficient number of traders reach their VaR limit and are forced to simultaneously reduce their portfolio; the reductions cause a sudden price movement, raise volatility and force even more traders to liquidate part of their positions. The model shows that market is more prone to suffer VaR cycles when investors use a short-term horizon to calculate asset volatility or a not-too-extreme value for their risk threshold.     

Non-linear volatility dynamics and risk management of precious metals

In this paper, we investigate the value-at-risk predictions of four major precious metals (gold, silver, platinum, and palladium) with non-linear long memory volatility models, namely FIGARCH, FIAPARCH and HYGARCH, under normal and Student-t innovations’ distributions. For these analyses, we consider both long and short trading positions. Overall, our results reveal that long memory volatility models under Student-t distribution perform well in forecasting a one-day-ahead VaR for both long and short positions. In addition, we find that FIAPARCH model with Student-t distribution, which jointly captures long memory and asymmetry, as well as fat-tails, outperforms other models in VaR forecasting. Our results have potential implications for portfolio managers, producers, and policy makers.     

Downside risk management and VaR-based optimal portfolios for precious metals,oil and stocks

Value-at-Risk (VaR) is used to analyze the market downside risk associated with investments in six key individual assets including four precious metals, oil and the S&P 500 index, and three diversified portfolios. Using combinations of these assets, three optimal portfolios and their efficient frontiers within a VaR framework are constructed and the returns and downside risks for these portfolios are also analyzed. One-day-ahead VaR forecasts are computed with nine risk models including calibrated RiskMetrics, asymmetric GARCH type models, the filtered Historical Simulation approach, methodologies from statistics of extremes and a risk management strategy involving combinations of models. These risk models are evaluated and compared based on the unconditional coverage, independence and conditional coverage criteria. The economic importance of the results is also highlighted by assessing the daily capital charges under the Basel Accord rule. The best approaches for estimating the VaR for the individual assets under study and for the three VaR-based optimal portfolios and efficient frontiers are discussed. The VaR-based performance measure ranks the most diversified optimal portfolio (Portfolio #2) as the most efficient and the pure precious metals (Portfolio #1) as the least efficient.     

VaR模型及其在金融风险管理中的应用

风险价值(简称VaR)是目前国际金融风险管理领域广泛使用的工具,也是度量金融风险的一种新的技术标准。本文着重介绍了VaR的概念、计算及其应用,并指出VaR模型作为衡量金融市场风险的标准在我国的应用前景。     

An approximate long-memory range-based approach for value at risk estimation

This paper proposes new approximate long-memory VaR models that incorporate intra-day price ranges. These models use lagged intra-day range with the feature of considering different range components calculated over different time horizons. We also investigate the impact of the market overnight return on the VaR forecasts, which has not yet been considered with the range in VaR estimation. Model estimation is performed using linear quantile regression. An empirical analysis is conducted on 18 market indices. In spite of the simplicity of the proposed methods, the empirical results show that they successfully capture the main features of the financial returns and are competitive with established benchmark methods. The empirical results also show that several of the proposed range-based VaR models, utilizing both the intra-day range and the overnight returns, are able to outperform GARCH-based methods and CAViaR models.     

VaR约束下的投资组合决策模型分析

VaR方法是目前国际上风险管理的主流方法之一。文章将VaR应用到Markwitz均值-方差模型中,构建和动态调整了投资者的最优投资组合,并分析了该组合模型的特性,提出了应用模型中应注意的问题。     

基于极值分布理论的VaR与ES度量

本文应用极值分布理论对金融收益序列的尾部进行估计,计算收益序列的在险价值VaR和预期不足ES来度量市场风险。通过伪最大似然估计方法估计的GARCH模型对收益数据进行拟合,应用极值理论中的GPD对新息分布的尾部建模,得到了基于尾部估计产生收益序列的VaR和ES值。采用上证指数日对数收益数据为样本,得到了度量条件极值和无条件极值下VaR和ES的结果。实证研究表明:在置信水平很高(如99%)的条件下,采用极值方法度量风险值效果更好。而置信水平在95%下,其他方法和极值方法结合效果会很好。用ES度量风险能够使我们了解不利情况发生时风险的可能情况。