风险值（VaR）度量方法新进展（下）

认真作好风险度量和管理工作，保持金融机构的稳健经营，是现代经济运行的基石，近年来，为了更好地衡量资产损失的风险，人们提出了风险值（Value at Risk)的概念，目前，风险值（VaR）已经成为风险管理目标的同义词。本文讨论的是风险值度量方法的新进展，具体包括三部分内容：第一部分是对风险管理概念VaR的讨论，指出国内一些文章对VaR概念的一些不恰当理解和应用；第二部分是完善VaR度量方法的分类标准及名称，介绍了风险度值量方法的新进展，并给出了VaR度量方法的实施程序；第三部分是对风险值度量方法研究的展望。     

权证隐含波动率在标的证券波动率预测中的作用研究

资产收益的波动是投资者投资决策的主要依据.本文选取了葛州和长虹等七只权证作为样本.首先应用单位根检验,验证各样本历史波动率和隐含波动率序列的平稳性,在此基础上检验各样本两种波动率序列的协整关系.最后,对隐含波动率所包含的额外信息进行探讨.结果表明,已实现波动率和隐含波动率基本上呈现单位根状态,并且两者之问基本不存在协整关系,权证的隐含波动率确实拥有额外的信息.投资者在实际运作中,可以加入隐含波动率来提高对实际波动率预测的准确性.     

度量金融市场风险的VaR方法探讨

近年来，国际金融市场所面临的主要风险人信用风险转向了市场风险，对市场风险的正大角度量构成了市场风险管理的基础，本文主要介绍广泛应用于度量市场风险的ＶａＲ（Ｖａｌｕｅ－ａｔ－Ｒｉｓｋ）方法，讨论计算ＶａＲ的参数法和非参数法及其计算步骤，探讨ＶａＲ的具体应用。     

期权定价模型中的波动率分析

布莱克-斯科尔斯的期权定价模型是一个对经济理论、金融实践产生巨大影响的模型。该模型需要输入的参数中唯一无法在市场中直接观察到的重要变量是基础资产的波动率。基于历史数据来计量的历史波动率有严重缺陷,于是人们根据期权的市场价格,利用Black-Scholes定价模型倒推出隐含波动率。隐含波动率反映投资者对未来市场的共同预期;对于避险者的套期保值业务来说,这是进行风险管理的一项重要指标。然而,隐含波动率在使用过程中也存在着＂波动率微笑＂、＂波动率偏斜＂及＂波动率期限结构＂等现象。究其根源,皆源自于Black-Scholes模型所依据的某些假设条件与实际情况不相符合。     

基于实现极差和实现波动率的中国金融市场风险测度研究

目前比较流行的金融市场风险价值研究一般采用日收益数据,并基于GARCH类模型进行估计和预测。本文利用沪深股指日内高频数据,分别通过ARFIMA模型和CARR模型对实现波动率和较新的实现极差建模,计算风险价值。通过对VaR的似然比和动态分位数等回测检验,实证分析了各种模型的VaR预测能力。结果显示,使用日内高频数据的实现波动率和实现极差模型的预测能力强于采用日数据的各种GARCH类模型。     

基于股指波动率的股市压力指数构建

金融危机的爆发,使得对系统性风险的有效监测成为当前宏观审慎管理部门迫切需要解决的问题.目前对系统性风险的监测主要有两类,一类是利用宏观经济数据进行分析监测,一类是利用金融市场与实体经济之间的关系,通过监测金融市场间接监测系统性风险.运用IMF及部分央行的理论方法,利用GARCH模型估计我国股票市场的波动性,辅之以股票市场涨跌情况,构建我国股票市场的市场压力指数,并进行简单的检验,市场压力指数较为灵敏、有效.     

VaR在铜企业期现货头寸风险管理中的应用

本文把风险管理中VaR方法引入到铜企业的头寸管理中来,通过VaR的方差-协方差计算方法得出随着价格变动头寸应该变动的理论数量。为铜企业的头寸风险管理提出了具有参考价值的建议。     

波动率风险及风险价格——来自中国A股市场的证据

本文应用Fama-Macbeth估计方法,以1997年2月至2009年6月中国A股股票为样本,考察股票市场波动率风险及其风险价格的特征。研究表明:波动率风险是一个显著的横截面定价因子,其风险价格为负,该结论不受流动性及市场偏度因子、待检资产改变、波动率模型设定的影响;在资产定价模型中引入波动率风险因子有利于解释规模效应和账面市值比效应异象。波动率的风险因子可以涵盖部分宏观经济变量的定价信息,规模因子是波动率风险因子的代理变量。     

沪深300波动率指数的编制方法

推出股票价格波动率指数旨在衡量市场的波动性水平,当今最有名的波动率指数是美国芝加哥期权交易所推出的波动率指数,该指数对投资者以及市场都具有深刻意义。本文欲构造反映我国A股市场全貌的波动率指数,但由于我国市场上并无期权交易,因此本文无法效仿国外编制波动率指数的隐含波动率法,而选用历史波动率法来进行波动率预测,通过对沪深300指数的分析,本文发现通过garch模型来预测波动率,进而编制波动率指数是可行的,从而提出我国应研究推出波动率指数的政策建议。     

商品市场不可生成波动率的实证研究

Trolle(2008)指出,商品市场风险分为可以由期货对冲的风险和不可以由期货而由期权来对冲的风险,对应地也就将市场的波动划分为可生成的波动和不可生成的波动。在检验不可生成的波动的存在性时,依据USV(Unspanned Stochastic Volatility,可生成随机波动率)模型,以Trolle(2008)设定的研究框架来进行实证分析,通过COMEX黄金、NYMEX原油以及使用不同于文献记录的市场风险代理变量表示方式计算的上海期货交易所阴极铜的建模结果,发现国内外市场均存在不可生成的波动,USV特征的存在性也为中国市场推出期权提供了理论支持。     

Forecasting Intraday Volatility and Value-at-Risk with High-Frequency Data

In this paper, we develop modeling tools to forecast Value-at-Risk and volatility with investment horizons of less than one day. We quantify the market risk based on the study at a 30-min time horizon using modified GARCH models. The evaluation of intraday market risk can be useful to market participants (day traders and market makers) involved in frequent trading. As expected, the volatility features a significant intraday seasonality, which motivates us to include the intraday seasonal indexes in the GARCH models. We also incorporate realized variance (RV) and time-varying degrees of freedom in the GARCH models to capture more intraday information on the volatile market. The intrinsic tail risk index is introduced to assist with understanding the inherent risk level in each trading time interval. The proposed models are evaluated based on their forecasting performance of one-period-ahead volatility and Intraday Value-at-Risk (IVaR) with application to the 30 constituent stocks. We find that models with seasonal indexes generally outperform those without; RV can improve the out-of-sample forecasts of IVaR; student GARCH models with time-varying degrees of freedom perform best at 0.5 and 1 % IVaR, while normal GARCH models excel for 2.5 and 5 % IVaR. The results show that RV and seasonal indexes are useful to forecasting intraday volatility and Intraday VaR.     

Risk Management for Linear and Non-Linear Assets: A Bootstrap Method with Importance Resampling to Evaluate Value-at-Risk

Many empirical studies suggest that the distribution of risk factors has heavy tails. One always assumes that the underlying risk factors follow a multivariate normal distribution that is a assumption in conflict with empirical evidence. We consider a multivariate t distribution for capturing the heavy tails and a quadratic function of the changes is generally used in the risk factor for a non-linear asset. Although Monte Carlo analysis is by far the most powerful method to evaluate a portfolio Value-at-Risk (VaR), a major drawback of this method is that it is computationally demanding. In this paper, we first transform the assets into the risk on the returns by using a quadratic approximation for the portfolio. Second, we model the return’s risk factors by using a multivariate normal as well as a multivariate t distribution. Then we provide a bootstrap algorithm with importance resampling and develop the Laplace method to improve the efficiency of simulation, to estimate the portfolio loss probability and evaluate the portfolio VaR. It is a very powerful tool that propose importance sampling to reduce the number of random number generators in the bootstrap setting. In the simulation study and sensitivity analysis of the bootstrap method, we observe that the estimate for the quantile and tail probability with importance resampling is more efficient than the naive Monte Carlo method. We also note that the estimates of the quantile and the tail probability are not sensitive to the estimated parameters for the multivariate normal and the multivariate t distribution. The research of Shih-Kuei Lin was partially supported by the National Science Council under grants NSC 93-2146-H-259-023. The research of Cheng-Der Fuh was partially supported by the National Science Council under grants NSC 94-2118-M-001-028.     

An Examination of Alternative Approaches to Risk Management and Insurance Research

Much current risk management and insurance research follows a pattern prescribed by the science paradigm. This article discusses some well-recognized problems associated with the science paradigm, and then presents several alternatives that can supplement the science paradigm, thereby broadening and deepening the scope of risk management and insurance research and education.     

Revenue Volatility and Fiscal Risks: An Application of Value-at-Risk Techniques to Hong Kong's Fiscal Policy

Revenue volatility poses challenges for fiscal policy makers. It can create risks to service provision, require borrowing, or entail sudden tax changes. This paper investigates the use of value-at-risk techniques to measure the fiscal risks caused by volatility as well as the sensitivity of measured risks to policies that may limit volatility. The revenue of Hong Kong's Special Administrative Region (SAR) is among the most volatile in Asia, and thus is a natural case for applying these techniques. Reflecting its revenue volatility, Hong Kong's SAR has traditionally held high fiscal savings (reserves), and the value of the self-insurance these savings provide is also discussed.     

Predicting Volatility in the Foreign Exchange Market

Measures of volatility implied in option prices are widely believed to be the best available volatility forecasts. In this article, we examine the information content and predictive power of implied standard deviations (ISDs) derived from Chicago Mercantile Exchange options on foreign currency futures. The article finds that statistical time-series models, even when given the advantage of “ex post” parameter estimates, are outperformed by ISDs. ISDs, however, also appear to be biased volatility forecasts. Using simulations to investigate the robustness of these results, the article finds that measurement errors and statistical problems can substantially distort inferences. Even accounting for these, however, ISDs appear to be too variable relative to future volatility.     

Systematic Risk and Revenue Volatility

We introduce the degree of economic leverage (DEL) as an extension of the existing method of decomposing beta and assess its incremental explanatory power through empirical testing. The DEL is defined as the percentage change in the firm's sales resulting from a unit percentage change attributable to an exogenous economic disturbance. The exogenous economic disturbance employed is the ratio of long‐term T‐bond rates to short‐term T‐bill rates. The evidence supports the DEL's role in explaining systematic risk at both the industry and portfolio levels. However, we find mixed results at the firm level.     

The Effect of VaR Based Risk Management on Asset Prices and the Volatility Smile

Value-at-risk (VaR) has become the standard criterion for assessing risk in the financial industry. Given the widespread usage of VaR, it becomes increasingly important to study the effects of VaR based risk management on the prices of stocks and options. We solve a continuous-time asset pricing model, based on Lucas (1978) and Basak and Shapiro (2001), to investigate these effects. We find that the presence of risk managers tends to reduce market volatility, as intended. However, in some cases VaR risk management undesirably raises the probability of extreme losses. Finally, we demonstrate that option prices in an economy with VaR risk managers display a volatility smile.     

基于VaR与多尺度分析的保险业风险值估计

风险值(Value-at-Risk,VaR)是现今衡量风险的标准。本文利用风险值(VaR)方法来为风险基础资本估计风险,使其能够准确地呈现保险人本身所面临风险的状况,并利于监督机关建立适当的监督预警措施,来保障全体保险人权益并维持金融秩序的稳定。考虑到多尺度变换对估计报酬率风险型态模型无需作假设的优点,且小波变换是一种重要的多尺度分析工具,本文引入小波变换来对非线性的保险数据序列中提取频率域的高频信息,利用多尺度分解的系数得到模型参数,从而实现更加准确的风险值估计。     

Risk management in the energy markets and Value-at-Risk modelling: a hybrid approach

This paper proposes a set of Value-at-Risk (VaR) models appropriate to capture the dynamics of energy prices and subsequently quantify energy price risk by calculating VaR and expected shortfall measures. Amongst the competing VaR methodologies evaluated in this paper, besides the commonly used benchmark models, a Monte Carlo (MC) simulation approach and a hybrid MC with historical simulation approach, both assuming various processes for the underlying spot prices, are also being employed. All VaR models are empirically tested on eight spot energy commodities that trade futures contracts on the New York Mercantile Exchange (NYMEX) and the constructed Spot Energy Index. A two-stage evaluation and selection process is applied, combining statistical and economic measures, to choose amongst the competing VaR models. Finally, both long and short trading positions are considered as it is of utmost importance for energy traders and risk managers to be able to capture efficiently the characteristics of both tails of the distributions.