巨灾保险:内涵、市场失灵、政府救济与现实选择

巨灾保险问题是一个全球性的问题,是保险业正在拓展的一个新领域,也是保险理论研究的一个新课题。深刻剖析巨灾风险的特殊内涵,明确指出目前巨灾保险存在的“市场失灵”问题,并提出政府干预是解决该问题的根本方法,从而为我国巨灾保险制度建立和完善提供现实选择。     

浅析我国巨灾保险体系的构建

我国是世界上巨灾风险比较严重的国家之一，保险业应在巨灾风险管理中发挥应有的功能。起到“减震器”的作用。2008年我国南方雪灾及四川地震暴露出我国现行巨灾保险制度存在的重大缺陷——巨灾造成损失的保险赔偿远低于全球平均水平，绝大部分损失靠政府的补贴和扶持，国内防范巨灾风险的基本保险险种几乎是空白。因此，巨灾保险制度的构建迫在眉睫。     

Pricing of swaps with default risk

In this paper, I study the valuation of interest rate and currency swaps with default risk under the contingent claim analysis framework. I demonstrate that the traditional approach of pricing swap contracts as exchanges of loans underestimates the value of such contracts to the counterparty with higher credit rating and exaggerates the credit spread required to guard against default risk. Numerical simulations show that the swap rate is not sensitive to counterparty credit rating: for a ten year interest rate swap, a one hundred basis point increase in counterparty bond yield spread results in only about one basis point increase in the swap rate. (JEL G10, G12, G13)This paper is based on Chapter 2 of my Ph.D. dissertation at Yale University. I would like to thank my dissertation committee, Kenneth French, Roger Ibbotson, and Jonathan Ingersoll, Jr. (chairman), for helpful advice and guidance. I would also like to thank Keny Back, Richard Lindsey, N. R. Prabhala, Ming Huang, Marti Subrahmanyam, three anonymous referees and especially Bob Jarrow, the editor, for helpful comments and suggestions. Any errors that remain are solely mine. This paper won the 1996 Trefftzs Award for best student paper from the Western Finance Association.     

系统结构不稳定点对系统的影响

突变理论是以拓扑学为基础，研究系统在平衡状态下临界点的性态，描述由逐渐变化的力或运动而导致系统发生突变的现象。本文采用突变理论的方法，推导出了系统在平衡状态下临界点的方程。     

突变论在桥梁方案比选应用中的进一步研究

桥梁方案的比选过程中包含很多要素，既包括具有确定性、可以定量表示的客观要素；同时也包括一些具有不确定性、难以给出定量表示的主观要素。如果把两者合理科学的统一起来，就需要应用到数学方法。文中通过对突变优选理论的研究，在突变级数法的研发基础上，更进一步提出因子分析方法的理论方法研究，为突变优选理论在桥梁方案比选中的应用提出更进一步的探讨。     

Realizing stock market crashes: stochastic cusp catastrophe model of returns under time-varying volatility

This paper develops a two-step estimation methodology that allows us to apply catastrophe theory to stock market returns with time-varying volatility and to model stock market crashes. In the first step, we utilize high-frequency data to estimate daily realized volatility from returns. Then, we use stochastic cusp catastrophe theory on data normalized by the estimated volatility in the second step to study possible discontinuities in the markets. We support our methodology through simulations in which we discuss the importance of stochastic noise and volatility in a deterministic cusp catastrophe model. The methodology is empirically tested on nearly 27 years of US stock market returns covering several important recessions and crisis periods. While we find that the stock markets showed signs of bifurcation in the first half of the period, catastrophe theory was not able to confirm this behaviour in the second half. Translating the results, we find that the US stock market’s downturns were more likely to be driven by the endogenous market forces during the first half of the studied period, while during the second half of the period, exogenous forces seem to be driving the market’s instability. The results suggest that the proposed methodology provides an important shift in the application of catastrophe theory to stock markets.     

CDS as insurance: Leaky lifeboats in stormy seas

What market features of financial risk transfer exacerbate counterparty risk? To analyze this, we formulate a model which elucidates important differences between financial risk transfer and traditional insurance, using the example of Credit Default Swaps (CDS). We allow for (heterogeneous) insurer insolvency, which captures the possibility that relatively risky counterparties may exist in the market. Further, we find that stable insurers become less stable as the price of the contract decreases. The analysis includes insured parties that have heterogeneous motivations for purchasing CDS. For example, some may own the underlying asset and purchase CDS for risk management, while others buy these contracts purely for trading purposes. We show that traders will choose to contract with less stable insurers, resulting in higher counterparty risk in this market relative to that of traditional insurance; however, a regulatory policy that removes traders can, perversely, cause stable counterparties to become less stable. We conclude with two extensions of the model that consider a Central Counterparty (CCP) arrangement and the consequences of asymmetric information over insurer type.     

从国际经验看我国地震巨灾保险基金的建立

本文借鉴国际上较成熟的巨灾保险基金建立与运行的先进经验,提出了在我国构建地震巨灾保险基金的构想,即采用以政府为主导的方式,在各项政策法规的支持下,充分利用保险的市场化作用,建立多层次、多渠道分摊损失的地震巨灾保险基金.     

CLOSED FORM PRICING FORMULAS FOR DISCRETELY SAMPLED GENERALIZED VARIANCE SWAPS

Most of the existing pricing models of variance derivative products assume continuous sampling of the realized variance processes, though actual contractual specifications compute the realized variance based on sampling at discrete times. We present a general analytic approach for pricing discretely sampled generalized variance swaps under the stochastic volatility models with simultaneous jumps in the asset price and variance processes. The resulting pricing formula of the gamma swap is in closed form while those of the corridor variance swaps and conditional variance swaps take the form of one‐dimensional Fourier integrals. We also verify through analytic calculations the convergence of the asymptotic limit of the pricing formulas of the discretely sampled generalized variance swaps under vanishing sampling interval to the analytic pricing formulas of the continuously sampled counterparts. The proposed methodology can be applied to any affine model and other higher moments swaps as well. We examine the exposure to convexity (volatility of variance) and skew (correlation between the equity returns and variance process) of these discretely sampled generalized variance swaps. We explore the impact on the fair strike prices of these exotic variance swaps with respect to different sets of parameter values, like varying sampling frequencies, jump intensity, and width of the monitoring corridor.