基于混合Copula的CDO定价模型

将广受欢迎的,用于CDO定价的大样本同质投资组合近似方法做了推广,其中涉及到的分布是高斯分布和Variance Gamma分布的混合,即G-VG混合分布.提出了厚尾的G-VG混合Copula模型和带有随机相关性的混合模型.这些模型可以有效的模拟CDO定价中的"相关性微笑"问题.在这些G-VG混合Copula模型中,得到了损失分布函数和期望分券层损失的解析表达式.并且用实际数据做了实证分析,把新模型和高斯模型的结果做了比较.实证表明,新模型的结果不仅与市场报价更贴近,而且为相关性结构带来了更多的灵活性.     

担保债权凭证定价——Copula函数的非参数估计与应用

CDO是最近十年来规模增长最快的信用衍生产品之一.2005年国家开发银行和建设银行两只资产支持证券的发行开启了我国CDO市场的先河,因此,深入分析CDO的合理定价不仅是当前金融研究中一个备受关注的问题也是促进CDO市场在我国健康发展的要求.对CDO定价要求模型化违约相关关系,普遍使用的方法是指定一个Copula函数来描述这种相关关系,然后使用市场数据来估计其中的参数.这种参数分析法方法不可避免的会导致巨大的模型风险.使用非参数方法可以从市场数据中推导出一个合理的Copula函数,然后利用Monte Carlo方法计算CDO分券的合理价差.这不仅可以避免因市场数据存在“厚尾”现象而错误估计Pearson相关系数带来的定价误差,还可以利用估计出来的Copula函数很方便地计算Kendall＇sτ、Spearman＇sρ等依赖性测度的值.     

从合成担保债务契约市场报价反求期望损失

研究通过CDO市场报价反求、校验期望损失的方法.在CDO风险中性定价的基础上建立介绍了通过市场报价反求期望损失的两个模型.比较了两种模型的优缺点.然后讨论了定价公式中不同的参数对保费的影响,并给出了模型的两个应用:求标的资产的违约分布以及计算非标准层的定价.     

金融危机与一般均衡下的CDO定价

传统的CDO根据无套利原理,将信用风险的保险费和违约后的回收金额两个现金流进行复制得出定价,注重金融市场局部均衡.然而无套利均衡定价的思路只针对存在套利机会的资产市场的局部均衡,使得该均衡与基础资产的联系不强.而一般均衡分析,可以引入实体经济的因素,有利于防止CDO定价的泡沫风险.因此文章在CDO定价中引入实体经济要素,证明一般均衡下CDO定价相比无套利定价有更丰富更敏感的风险刻画能力.实证结果发现,一般均衡定价相当于无套利定价加上修正项,且在高风险时期两者价差高于低风险时期,这是由于无套利定价忽略了实体经济的风险.因此CDO产品的无套利定价很可能存在着泡沫而导致资源配置扭曲.最后,文章认为CDO可以预防定价风险,用于解决地方政府债务问题,并提出相关的风险控制建议.     

多参数Archimedean Copula模型下的CDO定价

利用高维Archimedean Copula模型对合成CDO进行定价,在传统简单Archimedean Copula的基础上,基于三种不同的方式,引入多个参数,从而解决作为市场基准的Gaussian Copula模型下存在相关性微笑的问题.对于特殊的大样本同质资产组合,违约损失分布可以直接从违约概率得到.而对于一般性的资产组合,可以得到损失的特征函数,从而通过快速Fourier变换,计算出违约的分布.最后,给出了数值计算结果.     

独立非同分布二元高斯三角阵最大值的渐近性及相关统计推断

考虑二元独立非同分布高斯随机向量三角阵列最大值分布的渐近性及相关统计推断.此高斯三角阵的第n列的第i个向量服从二元高斯分布,其相关系数为i/n的函数并单调连续.首先建立了此高斯三角阵最大值分布的一阶和二阶渐近展开式.其次,分析相关系数参数估计及估计量的渐近性质.最后,通过随机模拟说明了相关系数之参数估计的有效性,并将该二元非同分布三角阵列模型应用于实际数据,得到了满意的结果.     

若干矩阵Γ分布的相关分布

本文研究了与矩阵Γ分布相关的若干分布的密度函数,利用矩阵Γ分布的特征函数和它的Bartlett分解等方法,获得了与矩阵Γ分布相关的几个分布的密度函数解析表达式,它们包括Γ分布随机矩阵的子矩阵、行列式、迹和特征根的分布密度,进一步还得到了相关系数矩阵的分布密度函数形式.     

双跳跃仿射扩散模型的美式看跌期权定价北大核心CSCD

美式期权是一类具有提前实施权利的奇异型合约.2000年Duffie等人提出了一类双跳跃仿射扩散模型,假定标的资产及其波动率过程具有相关的共同跳跃,且波动率过程的跳跃大小服从指数分布.文章扩展了该模型,允许波动率过程的跳跃大小服从伽玛分布,并在具有跳跃风险的随机利率环境下研究美式看跌期权的定价.应用Bermudan期权和Richardson插值加速方法给出了美式看跌期权价格计算的解析近似公式.用数值计算实例,以最小二乘蒙特卡罗模拟法检验文章结果的准确性和有效性.最后,分析了常利率与随机利率情形下波动率过程中的相关系数对期权价格的影响.结果表明,相关系数对美式期权价格的作用是反向的.文章结果可以应用于利率与信用衍生品的定价研究.     

双跳跃仿射扩散模型的美式看跌期权定价

美式期权是一类具有提前实施权利的奇异型合约.2000年Duffie等人提出了一类双跳跃仿射扩散模型,假定标的资产及其波动率过程具有相关的共同跳跃,且波动率过程的跳跃大小服从指数分布.文章扩展了该模型,允许波动率过程的跳跃大小服从伽玛分布,并在具有跳跃风险的随机利率环境下研究美式看跌期权的定价.应用Bermudan期权和Richardson插值加速方法给出了美式看跌期权价格计算的解析近似公式.用数值计算实例,以最小二乘蒙特卡罗模拟法检验文章结果的准确性和有效性.最后,分析了常利率与随机利率情形下波动率过程中的相关系数对期权价格的影响.结果表明,相关系数对美式期权价格的作用是反向的.文章结果可以应用于利率与信用衍生品的定价研究.     

混合高斯Heston资产定价模型及统计模拟分析

经典Heston模型没有考虑资产的长相依性,金融实践证明其不能很好的刻画资产的真实情况.本文建立了混合高斯Heston资产定价模型,利用股票数据进行实证分析.首先,得到了混合高斯Heston模型满足的随机偏微分方程,并讨论了解的存在性和唯一性以及p阶矩性质.其次,对模型中未知参数进行估计和敏感性分析,通过3只股票的实际数据对Heston模型、混合高斯Heston模型满足的价格路径与真实路径做了对比.研究表明:混合高斯Heston模型比经典的Heston模型更能够刻画资产标的价格.     

Stationary and multi-self-similar random fields with stochastic volatility

This paper introduces stationary and multi-self-similar random fields which account for stochastic volatility and have type G marginal law. The stationary random fields are constructed using volatility modulated mixed moving average (MA) fields and their probabilistic properties are discussed. Also, two methods for parameterizing the weight functions in the MA representation are presented: one method is based on Fourier techniques and aims at reproducing a given correlation structure, the other method is based on ideas from stochastic partial differential equations. Moreover, using a generalized Lamperti transform we construct volatility modulated multi-self-similar random fields which have type G distribution.     

非线性发病率随机流行病模型的动力学行为

研究了一类具有非线性发病率的随机SEIR传染病模型的绝灭性及平稳分布问题,通过构造合适的Lyapunov函数及控制噪声强度,在适当的条件下,得到模型的全局解存在唯一、指数稳定,且解具有平稳分布及遍历性.利用线性化及Fourier变换,证明了解渐近服从四维正态分布,并给出均值及方差矩阵的表达式.数值模拟验证了我们所得的主要结果.     

Inferring Boolean functions via higher-order correlations

Both the Walsh transform and a modified Pearson correlation coefficient can be used to infer the structure of a Boolean network from time series data. Unlike the correlation coefficient, the Walsh transform is also able to represent higher-order correlations. These correlations of several combined input variables with one output variable give additional information about the dependency between variables, but are also more sensitive to noise. Furthermore computational complexity increases exponentially with the order. We first show that the Walsh transform of order 1 and the modified Pearson correlation coefficient are equivalent for the reconstruction of Boolean functions. Secondly, we also investigate under which conditions (noise, number of samples, function classes) higher-order correlations can contribute to an improvement of the reconstruction process. We present the merits, as well as the limitations, of higher-order correlations for the inference of Boolean networks.     

Two asset-barrier option under stochastic volatility

Financial products which depend on hitting times for two underlying assets have become very popular in the last decade. Three common examples are double-digital barrier options, two-asset barrier spread options and double lookback options. Analytical expressions for the joint distribution of the endpoints and the maximum and/or minimum values of two assets are essential in order to obtain quasi-closed form solutions for the price of these derivatives. Earlier authors derived quasi-closed form pricing expressions in the context of constant volatility and correlation. More recently solutions were provided in the presence of a common stochastic volatility factor but with restricted correlations due to the use of a method of images. In this article, we generalize this finding by allowing any value for the correlation. In this context, we derive closed-form expressions for some two-asset barrier options.     

Solving the random diffusion model in an infinite medium: A mean square approach

This paper deals with the construction of an analytic-numerical mean square solution of the random diffusion model in an infinite medium. The well-known Fourier transform method, which is used to solve this problem in the deterministic case, is extended to the random framework. Mean square operational rules to the Fourier transform of a stochastic process are developed and stated. The main statistical moments of the stochastic process solution are also computed. Finally, some illustrative numerical examples are included.     

Pricing Options under Two-Factor Markov-Modulated Stochastic Volatility Models

In this paper, we investigate the valuation of European-style call options under an extended two-factor Markov-modulated stochastic volatility model, where the first stochastic volatility component is driven by a mean-reversion square-root process and the second stochastic volatility component is modulated by a continuous-time, finite-state Markov chain. The inverse Fourier transform is adopted to obtain analytical pricing formulae. Numerical examples are given to illustrate the discretization of the pricing formulae and the implementation of our model.     

Analysis of non-linear stochastic oscillations by the averaging method

The solution of a quasiconservative non-linear oscillatory system is considered, the right-hand sides of which are proportional to a small parameter. Fundamental relations for solving the problem are obtained by changing to “slow” variables and a combination of the stochastic averaging method and the theory of Markov processes. An efficient numerical algorithm is developed based on the fast Fourier transform that enables the output parameter distribution density and the amplitudes of the oscillations to be obtained. Application of the theory to solve the Duffing–van der Pol equation for an additive and multiplicative stochastic action is considered.     

On discrete Fourier transform,ambiguity, and Hamming-autocorrelation of pseudorandom sequences

We estimate discrete Fourier transform, ambiguity, and Hamming-auto-correlation of \(m\) -ary sequences in terms of their (periodic) correlation measure of order 4. Roughly speaking, we show that every pseudorandom sequence, that is, any sequence with small correlation measure up to a sufficiently large order, cannot have a large discrete Fourier transform, ambiguity, or Hamming-autocorrelation. Conversely, there are sequences, for example the two-prime generator, with large correlation measure of order 4 but small discrete Fourier transform, ambiguity, autocorrelation, and Hamming-autocorrelation.     

Tempered generalized functions and Hermite expansions

In this work we introduce an algebra of tempered generalized functions. The tempered distributions are embedded in this algebra via their Hermite expansions. The Fourier transform is naturally extended to this algebra in such a way that the usual relations involving multiplication, convolution and differentiation are valid. Furthermore, we give a generalized Itô formula in this context and some applications to stochastic analysis.     

A dimension and variance reduction Monte-Carlo method for option pricing under jump-diffusion models

We develop a highly efficient MC method for computing plain vanilla European option prices and hedging parameters under a very general jump-diffusion option pricing model which includes stochastic variance and multi-factor Gaussian interest short rate(s). The focus of our MC approach is variance reduction via dimension reduction. More specifically, the option price is expressed as an expectation of a unique solution to a conditional Partial Integro-Differential Equation (PIDE), which is then solved using a Fourier transform technique. Important features of our approach are (1) the analytical tractability of the conditional PIDE is fully determined by that of the Black–Scholes–Merton model augmented with the same jump component as in our model, and (2) the variances associated with all the interest rate factors are completely removed when evaluating the expectation via iterated conditioning applied to only the Brownian motion associated with the variance factor. For certain cases when numerical methods are either needed or preferred, we propose a discrete fast Fourier transform method to numerically solve the conditional PIDE efficiently. Our method can also effectively compute hedging parameters. Numerical results show that the proposed method is highly efficient.