黄金市场波动特征与风险度量

本文应用GARCH族模型刻画了伦敦黄金市场收益率波动特征,在此基础上计算出黄金市场VaR值用以度量风险并进行了回测检验。研究结果表明,伦敦黄金市场受外部冲击时收益率波动存在非对称效应,好消息带来的波动相对更大,并且高风险并不意味着高收益。给定95%置信水平,在正态分布假定下,TARCH(1,1)模型的VaR值精确度最高,可有效度量伦敦黄金市场波动风险。实证分析为黄金市场参与者管理风险提供了参考。     

国内外黄金市场风险传染的实证研究

以国际黄金市场上具有代表性的伦敦黄金现货价格和我国上海黄金交易所的黄金现货价格作为研究对象,利用多元GARCH模型对国内外黄金市场的风险传染机制进行了实证研究。结果表明：伦敦黄金市场对我国黄金市场存在单方向的均值溢出效应;从波动溢出效应来看,伦敦黄金市场对我国黄金市场的风险传染强于我国黄金市场对伦敦黄金市场的风险传染,存在着风险传染的非对称性。     

GARCH模型下的Var计算

在险价值(Valueat Risk)是一种由J.P.Morgen提出并不断完善的卓有成效的风险度量技术,GARCH模型可以很好地模拟并度量VaR值。文章讨论了基于不同分布假定下的GARCH模型在上海股市和深圳股市的VaR值,结果表明T分布和GED分布能更好地表现股市收益率的厚尾性,且深圳股市相比上海股市具有更大的风险性。     

GARCH模型下的Var计算

在险价值(Valueat Risk)是一种由J.P.Morgen提出并不断完善的卓有成效的风险度量技术,GARCH模型可以很好地模拟并度量VaR值。文章讨论了基于不同分布假定下的GARCH模型在上海股市和深圳股市的VaR值,结果表明T分布和GED分布能更好地表现股市收益率的厚尾性,且深圳股市相比上海股市具有更大的风险性。     

货币错配风险的特征与组合评估

提出一种基于VaR模型失败率的VaR风险综合值的权数构造方法,并利用它测度货币错配VaR风险上下限值和分析风险变动特征。同时AR(m)—ARCH类模型VaR组合风险测度表明:组合模型的VaR值比单一模型更合理和可靠;具有债权型特征的我国货币错配风险主要来自于外币资产增长快于外币负债的增长;货币错配风险区间的波动宽度与外币资产与外币负债增长之比值呈正相关关系;在本币升值和贬值之间权衡得失时,本币升值比贬值更有利于债权型货币错配风险的弱化。     

基于VaR的我国上证股市风险的实证研究

VaR方法的引入使金融资产的风险分析得以量化,其实质是波动率的计算。本文正是基于VaR方法,通过计算不同模型在不同分布下的VaR值,对上海股票市场风险进行比较分析。分析结果表明,GED分布下各模型对风险高估或低估的现象均有所缓解。TARCH模型和EGARCH模型比GARCH模型更能反映我国股市波动情况且前两种模型间并没有绝对的优劣之分,都能较好地反映收益率的风险特性。     

股票型基金风险实证研究

开放式基金和封闭式基金是两种不同运作方式的基金。本文基于VaR方法对开放式和封闭式的股票型基金进行风险测算和比较。由于基金的日收益率非正态的"尖峰厚尾"的分布特征,运用GARCH模型计算出了基金的VaR值。实证结果发现,开放式和封闭式基金的VaR值是存在差异的。     

股票型基金风险实证研究

开放式基金和封闭式基金是两种不同运作方式的基金。本文基于VaR方法对开放式和封闭式的股票型基金进行风险测算和比较。由于基金的日收益率非正态的"尖峰厚尾"的分布特征,运用GARCH模型计算出了基金的VaR值。实证结果发现,开放式和封闭式基金的VaR值是存在差异的。     

开放式基金的VaR值测算与评估——基于GARCH模型的实证分析

本文在对VaR值理论进行分析的基础上,选取了我国证券市场中偏重于股票投资的10支开放式基金,利用GARCH模型以及历史模拟法进行了VaR值比较研究,通过实证发现我国开放式基金的日收益率分布也呈现出非正态性和“尖峰宽尾”的特征,并且现阶段各支基金的风险与收益并未出现较为显著的对应关系。     

基于VaR我国阳光私募基金市场风险研究

VaR方法称为风险价值模型,VaR模型测量风险简洁明了,统一了风险计量标准,VaR度量金融风险受到普遍关注。本文以阳光私募基金的风险度量作为研究对象,基于VaR方法,通过计算不同模型的不同分布值,论证GARCH模型能较好解决残差异方差问题以及分析阳光私募基金市场存在的投资风险问题。     

论银行市场风险的资本计提——兼评内部模型法的适用性

市场风险及其监管资本要求的计量历来为业界和监管当局所关注。近期,次贷危机爆发导致的市场动荡使得全球银行业和监管当局开始重新审视其市场风险管理和监管资本要求。文章结合国际银行业和监管机构计量市场风险及其监管资本要求的当前做法,针对我国银行业的实际情况,重点探索了内部模型法在我国银行业的适用性,尤其是从方法论、特殊风险计量、验证等角度探讨了内部模型法的主要工具——风险价值体系在我国银行业计量市场风险及其监管资本要求的适用性,并从方法论和应用层面提出了相应的政策建议。     

Market capitalization and Value-at-Risk

The potential of economic variables for financial risk measurement is an open field for research. This article studies the role of market capitalization in the estimation of Value-at-Risk (VaR). We test the performance of different VaR methodologies for portfolios with different market capitalization. We perform the analysis considering separately financial crisis periods and non-crisis periods. We find that VaR methods perform differently for portfolios with different market capitalization. For portfolios with stocks of different sizes we obtain better VaR estimates when taking market capitalization into account. We also find that it is important to consider crisis and non-crisis periods separately when estimating VaR across different sizes. This study provides evidence that market fundamentals are relevant for risk measurement.     

Capital allocation for credit portfolios with kernel estimators

Determining the contributions of sub-portfolios or single exposures to portfolio-wide economic capital for credit risk is an important risk measurement task. Often, economic capital is measured as the Value-at-Risk (VaR) of the portfolio loss distribution. For many of the credit portfolio risk models used in practice, the VaR contributions then have to be estimated from Monte Carlo samples. In the context of a partly continuous loss distribution (i.e. continuous except for a positive point mass on zero), we investigate how to combine kernel estimation methods with importance sampling to achieve more efficient (i.e. less volatile) estimation of VaR contributions.     

Alternativen zu Value at Risk

The industry standard for the measurement of the downside risk of financial positions, Value at Risk (VaR), has serious deficiencies. In the current article we systematically discuss properties of risk measures and alternatives to VaR. It is demonstrated that utility-based shortfall risk (UBSR) possesses many desirable properties.     

An Analytical Evaluation Method of the Operational Risk Using Fast Wavelet Expansion Techniques

A financial institution that adopts an advanced measurement approach (AMA) as a method of computing operational risk capital has to measure 99.9 % value-at-risk (VaR) as the amount of an operational risk. The most popular method to satisfy the AMA standards requires the evaluation of aggregate (compound) loss distribution, which is called the loss distribution approach (LDA). The Monte Carlo (MC) method is a well known method for calculating VaR under the LDA. However, when using the MC method to calculate VaR, the statistical error of VaR for the fat-tailed distribution increases and the computation time increases in proportion to the expected value of frequency distribution. Since the MC method has these problems, this paper presents a new methodology to compute VaR under the LDA using fast wavelet expansion techniques. The key features of our algorithm are follows: (1) Scale transformation technique for loss distributions, (2) Double exponential transformation for oscillatory integrals, (3) Finite series expansion of the wavelet scaling coefficients, (4) Wynn’s epsilon algorithm to accelerate the convergence of those series, (5) Efficient cubic spline interpolation method to calculate the moment generating function. We illustrate the effectiveness of our algorithms through numerical examples.     

On the performance of the minimum VaR portfolio

Alexander and Baptista [2002. Economic implications of using a mean-value-at-risk (VaR) model for portfolio selection: A comparison with mean–variance analysis. Journal of Economic Dynamics and Control 26: 1159–93] develop the concept of mean-VaR efficiency for portfolios and demonstrate its very close connection with mean–variance efficiency. In particular, they identify the minimum VaR portfolio as a special type of mean–variance efficient portfolio. Our empirical analysis finds that, for commonly used VaR breach probabilities, minimum VaR portfolios yield ex post returns that conform well with the specified VaR breach probabilities and with return/risk expectations. These results provide a considerable extension of evidence supporting the empirical validity and tractability of the mean-VaR efficiency concept.     

Gibbs posterior inference on value-at-risk

ABSTRACT

Accurate estimation of value-at-risk (VaR) and assessment of associated uncertainty is crucial for both insurers and regulators, particularly in Europe. Existing approaches link data and VaR indirectly by first linking data to the parameter of a probability model, and then expressing VaR as a function of that parameter. This indirect approach exposes the insurer to model misspecification bias or estimation inefficiency, depending on whether the parameter is finite- or infinite-dimensional. In this paper, we link data and VaR directly via what we call a discrepancy function, and this leads naturally to a Gibbs posterior distribution for VaR that does not suffer from the aforementioned biases and inefficiencies. Asymptotic consistency and root-n concentration rate of the Gibbs posterior are established, and simulations highlight its superior finite-sample performance compared to other approaches.     

Value-at-risk versus expected shortfall: A practical perspective

Value-at-Risk (VaR) has become a standard risk measure for financial risk management. However, many authors claim that there are several conceptual problems with VaR. Among these problems, an important one is that VaR disregards any loss beyond the VaR level. We call this problem the “tail risk”. In this paper, we illustrate how the tail risk of VaR can cause serious problems in certain cases, cases in which expected shortfall can serve more aptly in its place. We discuss two cases: concentrated credit portfolio and foreign exchange rates under market stress. We show that expected shortfall requires a larger sample size than VaR to provide the same level of accuracy.     

Systemic risk measurement: Multivariate GARCH estimation of CoVaR

We modify Adrian and Brunnermeier’s (2011) CoVaR, the VaR of the financial system conditional on an institution being in financial distress. We change the definition of financial distress from an institution being exactly at its VaR to being at most at its VaR. This change allows us to consider more severe distress events, to backtest CoVaR, and to improve its consistency (monotonicity) with respect to the dependence parameter. We define the systemic risk contribution of an institution as the change from its CoVaR in its benchmark state (defined as a one-standard deviation event) to its CoVaR under financial distress. We estimate the systemic risk contributions of four financial industry groups consisting of a large number of institutions for the sample period June 2000 to February 2008 and the 12 months prior to the beginning of the crisis. We also investigate the link between institutions’ contributions to systemic risk and their characteristics.     

我国商业银行市场风险计量及波动性研究

本文结合巴塞尔委员会和我国监管当局对市场风险计量的规定,选取上市商业银行为样本,研究了我国上市商业银行风险计量的主要方式和特点,并分析了上市商业银行的市场波动性和系统性风险。分析主要结论为:(1)市场风险计量是市场风险管理的重要环节,风险计量模型的演变是从简单的风险计量发展到综合的风险收益计量的过程;(2)我国商业银行市场风险计量方式正在不断提高,风险价值分析和经济资本配置等方式正在逐步实施;(3)上市商业银行自身市场波动性较小,对于稳定资本市场起到了积极作用。