内容简介
This book is a second edition of the book of the same title by the first authorwhich was published in 2000. The subject of ruin probabilities and related top- ics has since then undergone a considerable development, not to say boom. This much expanded and revised second edition aims at covering a substantial part of these developments as well as the classical topics.
R,isk theory in general and ruin probabilities in particular are traditionally considered as part of insurance mathematics, and has been an active area of research from the days of Lundberg all the way up to today. One reason for writing tlus book is a feeling that the area has in recent years achieved a con-siderable mathematical maturity, which has in particular removed one of the standard criticisms of the area, namely that it can only say something about very simple models and questions. Although in insurance practice, usually sim- pler (and coarser) risk measures like Value-at-Risk are used, it is widely believed that the thinking advocated by ruin theory is still important for modern risk management. For instance, in times of market-consistent valuation principles, the role of the time diversification effect of insurance portfolios, which is one of the core elements of ruin theory, should not be forgotten. In addition, ruin the- ory has fruitful methodological links and applications to other fields of applied probability, like queueing theory and mathematical finance (pricing of barrier options, credit products etc.). Apart from these remarks, we have deliberately stayed away from discussing the practical relevance of the theory; if the formu- lations occasionally give a different impression, it is not by intention. Thus, the book is basically mathematical in its flavor.
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目录
Preface
Notation and conventions
Ⅰ Introduction
1 The risk process
2 Claim size distributions
3 The arrival process
4 A summary of main results and methods
Ⅱ Martingales and simple ruin calculations
1 Wald martingales
2 Gambler's ruin.Two-sided ruin.Brownian motion
3 Further simple martingale calculations
4 More advanced martingales
Ⅲ Further general tools and results
1 Likelihood ratios and change of measure
2 Duality with other applied probability models
3 Random walks in discrete or continuous time
4 Markov additive processes
5 The ladder height distribution
Ⅳ The compound Poisson model
1 Introduction
2 The Pollaczeck-Khinchine formula
3 Special cases of the Pollaczeck-Khinchine formula
4 Change of measure via exponential families
5 Lundberg conjugation
6 Further topics related to the adjustment coefficient
7 Various approximations for the ruin probability
8 Comparing the risks of different claim size distributions
9 Sensitivity estimates
10 Estimation of the adjustment coefficient
Ⅴ The probability of ruin within finite time
1 Exponential claims
2 The ruin probability with no initial reserve
3 Laplace transforms
4 When does ruin occur?
5 Diffusion approximations
6 Corrected diffusion approximations
7 How does ruin occur?
Ⅵ Renewal arrivals
1 Introduction
2 Exponential claims.The compound Poisson model with negative claims
3 Change of measure via exponential families
4 The duality with queueing theory
Ⅶ Risk theory in a Markovian environment
1 Model and examples
2 The ladder height distribution
3 Change of measure via exponential families
4 Comparisons with the compound Poisson model
5 The Markovian arrival process
6 Risk theory in a periodic environment
7 Dual queueing models
Ⅷ Level-dependent risk processes
1 Introduction
2 The model with constant interest
3 The local adjustment coefficient.Logarithmic asymptotics
4 The model with tax
5 Discrete-time ruin problems with stochastic investment
6 Continuous-time ruin problems with stochastic investment
Ⅸ Matrix-analytic methods
1 Definition and basic properties of phase-type distributions
2 Renewal theory
3 The compound Poisson model
4 The renewal model
5 Markov-modulated input
6 Matrix-exponential distributions
7 Reserve-dependent premiums
8 Erlangization for the finite horizon case
Ⅹ Ruin probabilities in the presence of heavy tails
1 Subexponential distributions
2 The compound Poisson model
3 The renewal model
4 Finite-horizon ruin probabilities
5 Reserve-dependent premiums
6 Tail estimation
Ⅺ Ruin probabilities for Levy processes
1 Preliminaries
2 One-sided ruin theory
3 The scale function and two-sided ruin problems
4 Further topics
5 The scale function for two-sided phase-type jumps
Ⅻ Gerber-Shiu functions
1 Introduction
2 The compound Poisson model
3 The renewal model
4 Levy risk models
ⅩⅢ Further models with dependence
1 Large deviations
2 Heavy-tailed risk models with dependent input
3 Linear models
4 Risk processes with shot-noise Cox intensities
5 Causal dependency models
6 Dependent Sparre Andersen models
7 Gaussian models.Fractional Brownian motion
8 Ordering ofruin probabilities
9 Multi-dimensional risk processes
ⅩⅣ Stochastic control
1 Introduction
2 Stochastic dynamic programming
3 The Hamilton-Jacobi-Bellman equation
ⅩⅤ Simulation methodology
1 Generalities
2 Simulation via the Pollaczeck-Khinchine formula
3 Static importance sampling via Lundberg conjugation
4 Static importance sampling for the finite horizon case
5 Dynamic importance sampling
6 Regenerative simulation
7 Sensitivity analysis
ⅩⅥ Miscellaneous topics
1 More on discrete-time risk models
2 The distribution of the aggregate claims
3 Principles for premium calculation
4 Reinsurance
Appendix
A1 Renewal theory
A2 Wiener-Hopf factorization
A3 Matrix-exponentials
A4 Some linear algebra
A5 Complements on phase-type distributions
A6 Tauberian theorems
Bibliography
Index
前言/序言
风险的边界:现代金融与保险中的不确定性管理 内容提要: 本书深入探讨了现代金融、保险及其他高风险行业中,理解、量化和管理不确定性所必需的理论基础、分析工具和实践应用。它超越了传统的风险度量模型,聚焦于在极端不利情景下系统性崩溃的可能性,以及如何构建更具韧性的操作框架。全书结构严谨,从概率论和随机过程的基本原理出发,逐步过渡到复杂的衍生品定价、信用风险建模、操作风险管理,以及宏观经济冲击下的金融稳定分析。 --- 第一部分:不确定性与随机过程的基础重述 本部分旨在为读者构建理解复杂风险现象所需的数学和统计学基石。我们不局限于经典的正态分布假设,而是将重点放在分布的“厚尾”特性和跳跃过程的建模上,这些特性在金融时间序列中表现得尤为突出。 第一章:概率论的重新审视:从精确到近似 本章首先回顾了概率论的核心概念,如随机变量、期望值和方差的定义。然而,重点迅速转向对极端事件(尾部风险)的关注。我们将探讨极值理论(Extreme Value Theory, EVT)在金融危机分析中的应用,包括 Block Maxima 和 Peaks Over Threshold 方法。详细讨论了非对称分布(如偏度和峰度)对风险估计的影响,并引入了更适合描述市场剧烈波动的非高斯模型,例如 $alpha$-稳定分布的初步概念。 第二章:随机过程的动态视角 理解风险随时间演变至关重要。本章系统介绍了描述连续时间随机现象的关键工具。布朗运动(Wiener 过程)作为最基础的连续时间模型被深入分析,探讨其路径依赖性和非可预测性。在此基础上,我们引入了复合泊松过程(Compound Poisson Process)来模拟资产价格或索赔事件的离散、随机到达,及其对累积损失的影响。更进一步,本章详细讲解了伊藤积分(Itô Calculus)的基本规则及其在描述资产价格随机微分方程(SDEs)中的核心地位,为后续的衍生品定价和动态资产负债管理奠定理论基础。 第二部分:信用、违约与系统性压力分析 金融机构面临的最大风险之一是交易对手方无法履行合约义务。本部分聚焦于对这种信用风险的量化和建模。 第三章:违约建模的进化:从结构到简约 信用风险建模的演变是本部分的核心。我们首先分析了 Merton 的结构模型,该模型将公司股权视为一种看涨期权,并探讨了该模型在实践中的局限性,特别是关于违约时间点和资产波动率的假设。随后,转向更具操作性的简约模型,重点分析了 Jarrow-Turnbull 框架,该框架利用了信用违约互换(CDS)的市场价格来校准违约强度(Intensity Rate)。强度过程的建模是关键,我们探讨了如 Vasicek 或 Hull-White 模型在描述利率和信用强度动态相关性方面的应用。 第四章:相关性、传染与多违约分析 在复杂的金融网络中,单个违约事件可能引发连锁反应。本章深入探讨了信用风险中的相关性问题。我们详细分析了经典的 Copula 函数族(如 Gaussian Copula、t-Copula),它们在构建多变量违约模型中扮演的角色,特别是用于估算投资组合层面尾部风险的聚合作用。此外,本章还引入了网络理论中的传染模型,用于模拟一个机构的失败如何通过其资产负债表连接传递至其他参与者,评估“大而不倒”的机构在系统中的潜在影响。 第三部分:保险数学与巨灾风险管理 本部分将视角转向保险业,核心关注巨额、不定期索赔的积累与管理,特别是当索赔频率和规模受外部冲击影响时。 第五章:索赔过程的积累与重塑 保险负债的估计依赖于对未来索赔到达和严重程度的准确预测。本章重新审视了破产风险模型的核心要素:索赔到达过程和单个索赔的分布。重点分析了如何利用精算学中的生存分析(Survival Analysis)技术来建模投保人生命周期中的事件发生概率。在损失严重性(Severity)方面,除了传统的伽马或韦布尔分布外,本章详细讨论了带有厚尾特征的分布(如帕累托分布)如何影响重置成本和巨灾赔付的估计。 第六章:重保险、再保险与资本配置的优化 风险管理在保险领域体现为资本的有效配置。本章探讨了再保险机制作为转移极端风险的工具。从比例再保险(Proportional Reinsurance)到超额损失再保险(Excess of Loss Reinsurance)的结构和定价被详细解析。在此基础上,我们将现代资本模型(如基于风险价值 Value-at-Risk 或预期亏空 Expected Shortfall 的度量)应用于确定最优的风险转移比例,以最小化资本成本的同时,满足监管机构设定的偿付能力要求。 第四部:衍生品定价与动态对冲的局限性 金融衍生品市场是高杠杆和不确定性的交汇点。本部分考察了经典定价模型(如 Black-Scholes-Merton)的假设在现实市场中的失效,以及如何应对这些失效。 第七章:跳跃扩散与随机波动率 经典 Black-Scholes 模型在平滑的、连续的资产价格变动假设下运行良好,但现实中市场波动往往伴随着突发性的、剧烈的价格跳跃。本章引入了包含跳跃项的扩散模型(如 Kou 模型或 Merton 的跳跃扩散模型),用以更真实地描述期权价格随时间的动态。随后,我们转向随机波动率模型(Stochastic Volatility Models),如 Heston 模型,探讨波动率本身作为一种随机过程如何影响期权微笑(Volatility Smile)的形成和演变,以及它对 Delta 对冲策略的深刻影响。 第八章:模型风险与对冲的有效边界 动态对冲(如 Delta 对冲)的有效性依赖于模型对底层资产路径的准确预测。本章的核心议题是“模型风险”——即所使用的数学模型与真实市场结构不匹配所带来的风险。我们将分析当市场波动率不是常数、利率也存在随机性时,传统对冲策略的失效点。讨论了高频交易中的滑点(Slippage)和流动性风险如何侵蚀对冲收益,以及如何在存在模型不确定性的情况下,设定对冲策略的“安全边际”。 --- 结论:面向韧性的风险框架 本书的结论部分强调,在理解了这些复杂的数学工具和模型之后,真正的挑战在于如何将这些理论转化为具有韧性的决策框架。这要求管理者超越单一风险指标的限制,采纳情景分析、压力测试和反脆弱性设计(Antifragility Design)的理念,以准备好应对那些传统模型认为“不可能”发生的极端事件。本书为从业者和研究人员提供了一个全面且深入的工具箱,以在日益复杂和相互关联的全球风险环境中做出更明智的决策。