模型危机还是应用不当造成的危机?——对《高盛风险之海市蜃楼》一文批驳VaR模型之反驳

次贷风波所引起的金融危机不但使国际银行业一度岌岌可危,而且沉重打击了世界经济。作为市场风险管理的核心,Vag模型遭到各方诟病。通过分析《高盛风险之海市蜃楼》一文,本文归纳了对Vag模型的几点认识误区,包括忽略对外部数据准确性的考核与检验,在模型应用上未能对正常市场情况与极端市场情况区别对待等。本文进而论证Vag仍是当前风险管理的首选工具,提出对收益率概率密度分布的选择将是Vag模型进一步发展的核心与关键,强调数据分析,指出量化研究应作为主观判断的前提与基础等观点。     

金融资产综合风险价值动态评估研究

为综合度量金融资产损失的市场风险与流动性风险,采用GARCH-VaR模型度量了日市场风险价值,用日内相对波动幅度调整为日LA-VaR,并利用时间延展槡T规则将它转换为变现期间的综合风险价值,构建了金融资产综合风险价值的全方位动态评估模型。通过以中国股指期货为例的实证研究证明,该模型能够有效评估金融资产综合风险价值,适用于金融资产公允价值的期末估算。     

金融危机期间巴塞尔资本协议提升银行风险管理能力的效应

在巴塞尔协议规则下,运用Va R预测模型的组合构建风险管理策略,包括保守型和积极型策略。从实践应用出发,运用沪深300指数检验这两种风险管理策略在2008—2009年全球金融危机期间的表现。在此基础上,对不同市场风险模型的Va R和每日资本要求进行测算。研究结果表明:第一,在全球金融危机期间,巴塞尔资本协议能够有效覆盖存款类金融机构可能的市场风险损失。第二,从每日资本要求测算结果来看,保守型风险策略的违反次数最小,但会导致较高的资本要求,对于希望保持在巴塞尔协议II绿色区域的银行是更好的选择。     

基于VaR的开放式股票型基金市场风险的测量与评价

通过采用半参数法计算投资组合VaR,得到相应VaR的近似置信区间,并结合成分VaR、边际VaR对投资组合vaR进行分解,结果发现,VaR作为风险管理工具同样可以有效应用于开放式股票型基金市场风险的测量与评价.     

Portfolio frontiers with restrictions to tracking error volatility and value at risk

Asset managers are often given the task of restricting their activity by keeping both the value at risk (VaR) and the tracking error volatility (TEV) under control. However, these constraints may be impossible to satisfy simultaneously because VaR is independent of the benchmark portfolio. The management of these restrictions is likely to affect portfolio performance and produces a wide variety of scenarios in the risk-return space. The aim of this paper is to analyse various interactions between portfolio frontiers when risk managers impose joint restrictions upon TEV and VaR. Specifically, we provide analytical solutions for all the intersections and we propose simple numerical methods when such solutions are not available. Finally, we introduce a new portfolio frontier.     

Varying the VaR for unconditional and conditional environments

Accurate forecasting of risk is the key to successful risk management techniques. Using the largest stock index futures from 12 European bourses, this paper presents VaR measures based on their unconditional and conditional distributions for single and multi-period settings. These measures underpinned by extreme value theory are statistically robust explicitly allowing for fat-tailed densities. Conditional tail estimates accounting for volatility clustering are obtained by adjusting the unconditional extreme value procedure with GARCH filtered returns. The conditional modelling results in iid returns allowing for the use of a simple and efficient multi-period extreme value scaling law. The paper examines the properties of these distinct conditional and unconditional trading models. The paper finds that the biases inherent in unconditional single and multi-period estimates assuming normality extend to the conditional setting.     

A new time-varying optimal copula model identifying the dependence across markets

This paper proposes a new time-varying optimal copula (TVOC) model to identify and capture the optimal dependence structure of bivariate time series at every time point. In the TVOC model, half-rotated copulas are constructed to measure the nonlinear and asymmetric negative dependence, and the distribution-free test for independence is introduced to verify the dependent relationship and reduce the computational time. The TVOC model is then employed to research the dependence structure between security and commodity markets. We find evidence that the dependence structures across different markets vary over time and that emergencies are usually the major cause of sudden changes in the dependence structure. We also show that the TVOC model captures the dynamic characteristics of the direction and intensity of the dependence as well as the dynamic characteristics of the types of dependence structure. In particular, the half-rotated copulas can accurately describe the asymmetric negative extreme dependence across different markets.     

Dynamic characteristics of the daily yen–dollar exchange rate

This paper explores various dynamic properties of daily data for the yen–dollar exchange rate. This empirical study shows that quantitative information articulated with technical trading acts as market-based indicators, thus contributing to the modelling of daily fluctuations in the exchange rate. Value-at-Risk analysis is also performed to demonstrate that allowing for data properties such as skewness is essential for representing the underlying volatility of the yen–dollar rate.     

中国股票市场风险度量的模型选择与比较研究

分别采用等权移动平均方法、指教加权移动平均方法、GARCH(1,1)方法、GARCH(1,1)-t方法和Pareto型极值分布方法计算上海和深圳股票日收益率的VaR.向后检验表明,Pareto型极值分布方法比其他方法更能准确地反映我国股市的风险.     

Market Risk of Developed and Emerging Countries During the Global Financial Crisis

This study compares the performance of the widely used risk measure, value at risk (VaR), across a large sample of developed and emerging countries. The performance of VaR is assessed using both the unconditional and conditional tests of Kupiec and Christoffersen, respectively, as well as the quadratic loss function. The results indicate that VaR performs much more poorly when measuring the risk of developed countries than of emerging ones. One possible reason might be the deeper initial impact of the global financial crisis on developed countries. The results also provide evidence of the decoupling of the market risk of emerging and developed countries during the global financial crisis.     

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。     

Dynamic Modeling of Tail Risk: Applications to China, Hong Kong and Other Asian Markets

In this paper, we study the extreme dependence between the markets in Hong Kong, Shanghai, Shenzhen, Taiwan and Singapore. The tail dependence coefficient (TDC), which measures how likely financial returns move together in extreme market conditions, is modeled dynamically using the Multivariate Generalized Autoregressive Conditional Heteroscedasticity model with the time-varying correlation matrix of Tse and Tsui (Journal of Business & Economic Statistics, 20(3):351–363, 2002). The time paths of the TDC indicate that Hong Kong stocks had the highest extreme dependence during the Asian financial crisis and their TDCs have followed an increasing trend since 2006. The results in this paper also show that the TDC pattern of Singapore with the other markets is very similar to the TDC pattern of Hong Kong with the other markets. An increasing trend in the extreme dependence between Shanghai A Share Index and Shanghai B Share Index and between the Hang Seng Index and the Hong Kong China Enterprise Index is observed from 2002 to 2007. A substantial rise in the TDC between Shenzhen A Share Index and Shenzhen B Share Index was recorded after the China market reforms in 2005. Our TDC modeling with Asian market data provides evidence that Asian markets are becoming integrated and their extreme co-movements during financial turmoil are becoming stronger.     

The disclosure of risk in financial statements

The accounting information currently issued by firms is not wholly adequate when used for decision making purposes, and within that process, for forecasting, for which additional information on risks is required. Therefore a reform of the current framework becomes necessary. Within this reform an adequate scheme and typology for the risks facing firms must be established and a set of specific risk quantification models must be designed. This paper focuses on both issues, showing all the risks that can affect firms and proposing a quantification model for each one.     

收益率曲线动态与银行经济价值的利率风险

本文在收益率曲线动态的主成分分析基础上,运用MonteCarlo模拟的主成分VaR方法,以我国五家商业银行为样本研究银行账户经济价值利率风险的计量方法,并与巴塞尔委员会标准久期法的结果进行比较。同时,对VaR模型的有效性进行了样本外的返回检验。研究发现,五家银行的经济价值面临的是利率上升的风险;非正态主成分VaR模型估计的经济价值利率风险,都要大于正态主成分VaR模型的结果,这反映了利率波动的厚尾特征,正态假设有可能低估风险。     

Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange

This paper investigates the use of tick-by-tick data for intraday market risk measurement. We propose a method to compute an Intraday Value at Risk based on irregularly spaced high-frequency data and an intraday Monte Carlo simulation. A log-ACD–ARMA–EGARCH model is used to specify the joint density of the marked point process of durations and high-frequency returns. We apply our methodology to transaction data for three stocks actively traded on the Toronto Stock Exchange. Compared to traditional techniques applied to intraday data, our methodology has two main advantages. First, our risk measure has a higher informational content as it takes into account all observations. On the total risk measure, our method allows for distinguishing the effect of random trade durations from the effect of random returns, and for analyzing the interaction between these factors. Thus, we find that the information contained in the time between transactions is relevant to risk analysis, which is consistent with predictions from asymmetric-information models in the market microstructure literature. Second, once the model has been estimated, the IVaR can be computed by any trader for any time horizon based on the same information and with no need of sampling the data and estimating the model again when the horizon changes. Backtesting results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active in the market.