随机利率下跨期多事件触发巨灾债券定价研究

巨灾债券兼具规避巨灾风险和投资功能,既是对巨灾救助体制的有力补充,又为资本市场提供了较高收益率的零贝塔债券。多事件触发巨灾债券只有当多个触发指标被同时满足时才会损失本金,投资风险小于单事件触发巨灾债券,具有更大市场潜力。结合中国的台风历史损失数据,可构建多事件触发巨灾债券的定价模型,结合Copula函数拟合的双触发指标的联合分布,在利率服从Vasicek随机利率模型的假设下完成多事件触发巨灾债券的定价过程,并得出利率的随机因素对跨期定价结果的影响。     

中国农业巨灾债券定价模型与实证研究——基于安徽省棉花产量数据

巨灾债券作为一种金融创新产品,可以有效解决保险市场巨灾风险非可保性问题,正逐步被一些国家应用于农业保险领域。本文以安徽省棉花年产量数据为样本,以该地区相对于长期平均趋势产量偏差量为损失指数,设计了一种农业巨灾债券。研究结果表明:通过样条回归与Gaussian核密度估计法,以及产量损失分布与利率风险相结合,可以初步构建中国农业巨灾债券的定价模型并付诸实践。     

巨灾风险债券定价

巨灾风险债券通过证券化将风险转移到资本市场,投资者可以利用巨灾债券与经济变量无关的特性,获取不受金融市场变量影响的高收益.本文推导了一个不完全市场框架下的基于代表性代理模型基础上的巨灾风险债券定价模型.     

随机利率条件下可转换债券定价模型的经验检验

本文运用市场的实际数据对随机利率条件下可转换债券的定价模型作了经验检验,发现在可卖空的市场条件下,对处于实值状态的可转换债券可直接获得满意的定价,对处于虚值状态的可转换债券需要考虑债券的恶意违约风险,在加入风险补偿后也可获得满意的定价。在中国不可卖空的市场条件下,这一定价模型仅对进入转换期的可转换债券的价格具有一定的预测作用。     

基于利率期限结构模型的中国可转换债券定价分析

选取上海证券交易所国债,基于样条函数模型推导出无违约利率期限结构,进行有效性检验;选取上海证券交易所可转换债券推导出违约利率期限结构,并与无违约利率期限结构对可转换债券的定价效果进行比较;针对目前国内可转换债券定价均假设利率恒定、没有考虑利率变化的现状,将无违约利率期限结构与Black-Scholes期权定价模型相结合,提出了可转债定价的一般模型,并用此模型对歌华转债进行定价实证分析,结果表明,两者均存在一定的偏差,可见利率期限结构模型对中国可转换债券定价相对有效.     

论保险公司的巨灾风险管理——再保险方法与风险证券化

随着时代发展,经济中出现各种风险损失规模也日益增大,因此保险公司在面临巨灾时可能出现承保以及偿付能力不足的情况。为了管理巨灾风险,保险公司可以使用再保险方法,但该方法仍存在一定局限。因此,使用以巨灾债券为代表的风险证券化方法成为国际保险市场的新动态。巨灾债券通过引入资本市场资金以扩大保险市场的风险容量并实现了巨灾风险的转移及扩散。而资本市场引入巨灾债券也被证明是对投资组合的优化。风险证券化的应用在我国有着广大的发展前景。     

基于两要素利率期限结构的债券期权定价

基于经验证据并为了弥补存在模型缺点,提出短期利率与短期利率均值均为随机变量的两要素的利率期限结构模型,解出折现债券的定价公式,在此基础上,进一步给出债券期权、债券期货期权、债券远期期权的定价公式.     

可违约零息债券风险综合度量Monte Carlo方法

可违约零息债券同时面临着违约风险和市场风险(利率风险)这两类主要风险.相对于传统的不同类风险独立度量方法,也不同于割裂两类风险再进行加总或通过Copula函数关联,本文在信用风险强度定价模型的基础上,同时考虑信用风险、市场风险和两类风险之间的相关关系,建立了计算可违约零息债券综合风险VaR的Monte Carlo方法,得出同一个风险计算期下反映两类风险的损失分布和同一个某置信度的损失分布的分位点,进而能求得风险综合VaR值,这样可在同一个框架下同时捕捉可违约零息债券的两类风险,这里,给出了MonteCarlo模拟方法具体技术细节,包括违约时间和基础状态向量过程的模拟.最后运用本文的风险综合度量模型对短期融资券的综合风险进行计算,得出风险综合VaR值,并与利率风险独立度量VaR值和信用风险独立度量VaR值进行比较分析.     

随机波动HJM框架下可违约债券市场波动结构的实证研究

波动结构对于可违约债券及其衍生品的定价和风险管理具有重要意义.利用AAA级企业债券价格数据,基于中国可违约债券市场构建三因子可违约随机波动HJM模型,并对其进行有限维马尔科夫仿射实现.在此基础上,从波动因子的随机波动特征、相关性结构和贡献度3个方面对中国可违约债券市场的波动结构进行系统分析.研究结果表明,样本期内中国可违约债券隐含的无风险利率和信用利差的波动率中含有显著的随机波动过程,且其数值呈现持续增大的趋势;无风险短期利率、短期信用利差和随机波动过程3个主要波动因子之间存在显著的相关关系;各波动因子的风险贡献度随时间推移而发生明显的波动.在经济向好时期,无风险短期利率的风险贡献较大;在经济趋冷时期,短期信用利差的风险贡献占优.     

基于测度变换方法的随机型创新幂式期权定价

随机型创新幂式期权以其结构简明、风险可控而受到投资者青睐。针对传统方法求解随机型期权存在的困难,提出用测度变换方法解决随机幂式期权的定价模型。受鞅定价方法的启发,推广计价单位的选取以获取相应的等价测度变换,得到随机利率情形下具有一般支付函数的测度变换公式;以此为基础选取远期债券为计价单位,并考虑债券价格波动和股价波动的相关性,可以方便地推导出随机型幂式期权定价模型。通过对模型风险特征的数值模拟分析,说明了幂型期权的优势所在。此项研究结论对金融衍生产品的发行者和投资者具有一定的理论借鉴意义。     

Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location‐Specific Premiums in the Netherlands

As flood risks grow worldwide, a well‐designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood‐loss‐sharing program involving private insurance based on location‐specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS‐based flood model and a stochastic optimization procedure with respect to location‐specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile‐related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures.     

基于Esscher变换的巨灾指数期权定价与数值模拟

巨灾指数期权是最重要的巨灾衍生工具之一,在我国有很好的发展前景。但巨灾指数期权在我国推广的一个主要技术障碍是,在信息较少的情况下,如何对巨灾指数期权进行快速的定价。本文提出了一种基于Esscher变换的巨灾指数期权定价的解析表达公式,区别于以往文献采用亚式期权或随机时间变化的方法。这个方法的优势在于能够反映巨灾指数的跳跃性、两部性（损失期和延展期）、上界性特点。同时,Esscher 变换的无套利等价性也赋予该方法坚实的理论基础,有较好的延展性,可以使用多种分布过程。首先,具体给出漂移泊松、漂移伽马和维纳过程条件下的巨灾指数期权定价公式。通过数值模拟分析结果与Black-Scholes公式结果及巨灾指数历史数据的对比,认为基于漂移伽马过程的定价结果能更好地反映巨灾指数的特点。最终,指出了巨灾指数的开发和本文提出的方法在中国具有很好的应用前景。     

Optimal Security Investments and Extreme Risk

In the aftermath of 9/11, concern over security increased dramatically in both the public and the private sector. Yet, no clear algorithm exists to inform firms on the amount and the timing of security investments to mitigate the impact of catastrophic risks. The goal of this article is to devise an optimum investment strategy for firms to mitigate exposure to catastrophic risks, focusing on how much to invest and when to invest. The latter question addresses the issue of whether postponing a risk mitigating decision is an optimal strategy or not. Accordingly, we develop and estimate both a one‐period model and a multiperiod model within the framework of extreme value theory (EVT). We calibrate these models using probability measures for catastrophic terrorism risks associated with attacks on the food sector. We then compare our findings with the purchase of catastrophic risk insurance.     

Community Resilience and Decision Theory Challenges for Catastrophic Events

Extreme and catastrophic events pose challenges for normative models of risk management decision making. They invite development of new methods and principles to complement existing normative decision and risk analysis. Because such events are rare, it is difficult to learn about them from experience. They can prompt both too little concern before the fact, and too much after. Emotionally charged and vivid outcomes promote probability neglect and distort risk perceptions. Aversion to acting on uncertain probabilities saps precautionary action; moral hazard distorts incentives to take care; imperfect learning and social adaptation (e.g., herd‐following, group‐think) complicate forecasting and coordination of individual behaviors and undermine prediction, preparation, and insurance of catastrophic events. Such difficulties raise substantial challenges for normative decision theories prescribing how catastrophe risks should be managed. This article summarizes challenges for catastrophic hazards with uncertain or unpredictable frequencies and severities, hard‐to‐envision and incompletely described decision alternatives and consequences, and individual responses that influence each other. Conceptual models and examples clarify where and why new methods are needed to complement traditional normative decision theories for individuals and groups. For example, prospective and retrospective preferences for risk management alternatives may conflict; procedures for combining individual beliefs or preferences can produce collective decisions that no one favors; and individual choices or behaviors in preparing for possible disasters may have no equilibrium. Recent ideas for building “disaster‐resilient” communities can complement traditional normative decision theories, helping to meet the practical need for better ways to manage risks of extreme and catastrophic events.     

An Insurance Model for Risk Management of Process Facilities

Most existing risk management models for process industries do not consider the effect of insurance coverage, which results in an overestimation of overall risk. A model is presented in this article to study the effect of insurance coverage of health, safety, environmental, and business risks. The effect of insurance recovery is modeled through the application of adjustment factors by considering the stochastic factors affecting insurance recovery. The insurance contract's conditions, deductibles, and policy limits are considered in developing the insurance recovery adjustment factors. Copula functions and Monte Carlo simulations are used to develop the distribution of the aggregate loss by considering the dependence among loss classes. A case study is used to demonstrate both the practical application of the proposed insurance model to improve management decisions, and the mitigating effect of insurance to minimize the residual risk.     

考虑投资者情绪的中国股市自激发效应研究

本文在极值理论中引入行为金融学，结合标值自激发点过程（MSEPP）刻画股指收益率极端值序列的集聚性、短期相依性，并将传统的超阈值模型所描述的齐次泊松过程拓展为非齐次泊松过程，探讨投资者情绪对极端收益率的冲击。运用风险偏好指数的方法，基于沪深300指数成份股合成中国投资者情绪指数（EMSI），进一步构建MSEPP-EMSI模型预测沪深300指数、上证综合指数及深圳成分指数的极端风险爆发概率，并对其进行动态ES风险测度。实证结果表明，沪深股市在短期内股指连续暴跌现象时有发生，投资者极度负面情绪会加剧股市的剧烈动荡，当考虑投资者情绪对极端风险的冲击时，MSEPP-EMSI模型能有效的提高对极端风险的概率预测精度及ES预测精度。     

基于银行监管资本的存款保险定价研究

结合存款保险定价的期权定价法和期望损失定价法,提出了利用银行破产时被保险存款的期望损失来定价存款保险的新思路,该方法的特点是存款保险定价不仅仅与银行资产的风险和收益有关,而且与银行资本持有状况和存款的参保比率有密切关系.通过理论推导得到了存款保险定价公式.运用极大似然估计方法与测算原理,实证研究了其敏感性、可行性与合理...