中国股票市场和房地产市场的极值相关性与计量模型研究

本文研究的是股市和房地产市场的相关性,分别从整体相关性和极端相关性两方面入手。在进行实证研究时所选取的代表两个市场的指标分别是股票市场价格指数和房地产市场价格指数。     

中国股市与世界主要股票市场联动性的实证研究

一、引言 由于全球股市的波动,不仅影响和蔓延全球经济活动的各个方面,而且对整个国际金融领域的影响更为剧烈,特别是货币市场.目前国际货币市场危机隐患无处不在,随时有可能导致全球性金融危机.如果全球股市在2008年反复振荡,将使全球经济前景不确定性进一步增大,国际金融市场也有可能因此风雨飘摇.     

Conditional tail behaviour and Value at Risk

In this paper we study the tail behaviour of eight major market indexes stratifying data according to the violation of a high threshold on the previous day. The distributional differences found can be exploited to improve VaR calculations in several settings, giving rise to what we call ‘MCVaR’. We compare the performance of MCVaR with unconditioned VaR calculation methods and with GARCH VaR by means of several back-testing techniques that take into account not only the number of violations but also their magnitude and clustering.     

基于风险价值法和压力测试法的企业风险管理方法研究

本文提出风险价值法和压力测试法的企业风险管理方法,克服了传统方法只给出风险相对严重程度的不足。建立风险量化评估、预警和控制体系,采用优化组合方法,实施一体化风险管理,规避重大风险事件的发生。     

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.     

基于时间序列极值理论的跳跃风险研究

金融资产的跳跃行为作为对极端事件的刻画,为研究极端事件风险提供了良好工具。基于时间序列下的极值理论,在放松独立同分布假设下,构造了金融资产收益率序列尾部中跳跃动态特征的极值模型。通过对上证综指大跨度、高频度的实证研究,剖析了投资者结构、投资者行为与收益尾部分布之间的相互作用机制,进一步对金融资产收益尾部的跳跃风险进行了有效测度。结果表明,极端跳跃风险的分布特征在频率与尾部方向上呈现很强的不对称状态。     

全球股票市场系统性风险传递网络研究

本文采用系统性风险度量新指标LASSO-ΔCoVaR,构建全样本时期及各极端时期全球股票市场系统性风险传递网络,考察全球股票市场系统性风险传递水平及结构特征,并着重对极端状态下的风险传递进行分析。研究发现:第一,无论风险输入水平还是风险输出水平,不同股市的动态变化趋势大体一致,但波动幅度迥然不同,且单个股市风险输出水平的波动幅度远大于风险输入水平;第二,成熟经济体经济基本面恶化往往会增强其股市的系统性风险贡献,而新兴经济体则不同;第三,法国、荷兰、中国香港、德国和英国股市的风险溢出水平较高,同其他股市间的风险传递途径较多,是系统性风险传递网络中的核心节点;第四,我国股市与全球股市间的风险关联较弱,但我国股市潜在风险来源面广,同区域股市及金砖国家股市在我国股市与全球股市间的风险传递发挥重要作用。     

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。     

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.     

操作风险损失的广义帕累托分布参数估计及其应用

极值理论表明大于某一阀值的样本服从广义帕累托分布,该结论在金融风险计量和保险精算中有着广泛的应用。然而,由于其参数没有可接受的估计方法,致使其应用受到限制。论文在推导出广义帕累托分布的条件矩的基础上,研究了基于操作风险损失的广义帕累托分布的参数估计问题。并且基于我国商业银行1994~2008年的操作风险损失数据对经济资本配置进行了算例分析。     

典型事实、极值理论与金融市场动态风险测度研究

本文在金融市场典型事实约束下,运用ARFIMA模型对金融市场条件收益率建模,运用GARCH、GJR、FIGARCH、APARCH、FIAPARCH等5种模型对金融波动率进行建模,进而运用极值理论(EVT)对标准收益的极端尾部风险建模来测度各股市的动态风险,并用返回测试(Back-testing)方法检验模型的适应性。实证结果表明,总的来说,FIAPARCH-EVT模型对各个市场具有较强的适应性,风险测度能力较为优越。进一步,本文在ARFIMA-FIAPARCH模型下,假定标准收益分别服从正态分布(N)、学生t分布(st)、有偏学生t分布(skst)、广义误差分布(GED)共4种分布,对各股市的动态风险测度的准确性进行检验,并和EVT方法的测度结果进行对比分析。结果表明,EVT方法风险测度能力优于其他方法,有偏学生t分布假设下的风险测度模型虽然略逊于EVT方法,但也不失为一种较好的方法;ARFIMA-FI-APARCH-EVT不仅在中国大陆沪深股市表现最为可靠,而且在其他市场也表现出同样的可靠性。     

Learning about Risk: Some Lessons from Insurance

This paper argues that in the fundamental subject of financialrisk analysis, some valuable lessons may be drawn from insurance.The probability of ruin, defined as a first passage time, carriesa dynamic element whose absence in Value at Risk is one liability,among others. Extreme value theory, which has been successfullyapplied to insurance shortly after it was introduced in probability,may offer a coherent framework for analyzing the extreme movessuch as the ones observed in recent foreign exchange and financialcrises. Lastly, we show that the genuine hazards generated byglobal capital markets and illustrated by the events of summer1998, generate a market incompleteness that existing modelsof defaultable bonds do not fully address. In contrast, thelong experience of risk premium analysis in the insurance andreinsurance industry, as well as the existence of historicaldata on natural disasters, render the valuation of catastrophebonds less perilous than that of defaultable bonds.