美式期权定价问题的数值方法

本文研究美式股票看跌期权定价问题的数值方法。通过将问题转化为等价的变分不等式方程,分别建立了半离散和全离散有限元逼近格式。并给出了有限元解的收敛性和稳定性分析。数值实验表明本文算法是一个高效和收敛的算法。     

CARA效用函数下美式期权的定价

考虑当期权持有者的效用为CARA效用函数U(x)=-e<'-λx>时的关式期权定价问题.运用最优停止理论得到其在有限离散时间金融市场模型下的最佳实施期,并给出相应美式期权的定价公式.     

混合高斯模型下带红利的永久美式期权定价

本文建立混合高斯模型下支付连续红利的永久美式期权定价模型.利用自融资策略和分数伊藤公式,得到永久美式期权价值所满足的偏微分方程.其次,由永久美式期权的实施条件与看涨-看跌期权的对称关系,获得看涨与看跌期权的定价公式与最佳实施边界.最后,利用平安银行的日收盘价对标的资产进行实证分析,结果表明:用混合高斯模型模拟出的股票价格与真实股票价格比较接近,能够反映股票的整体走势.     

有限体积法定价美式期权

讨论美式期权定价的有限体积法.采用投影超松弛迭代法求解隐式欧拉和CrankNicolson有限体积格式离散Black-Scholes偏微分方程得到的线性互补问题.数值实验结果表明,两种有限体积格式都是有效的,而Crank-Nicolson格式的数值效果要优于隐式欧拉格式.     

离散模型下的美式期权定价

本文考虑离散时间金融市场模型中由效用函数U(x)所产生的报酬序列(U1 (Srn))n的最优停止问题.其中U(x)是由股票价格产生的效用.     

分数布朗运动的美式障碍期权定价

假定标的股票服从分数布朗运动,应用二次近似法和偏微分方程方法求出了美式下降敲出看涨、看跌障碍期权价格近似解以及最佳实施边界.最后,通过显式差分法比较近似解的准确性,并分析Hurst参数对期权价格和最佳实施边界S*的影响.     

美式期权定价的数值方法

介绍了定价美式期权的几种常见数值方法.对最近几年的主要研究成果做了简单的介绍和比较,并给出了数值算例.特别回顾了美式期权定价的蒙特卡罗模拟加速方法.     

随机波动率的美式期权定价

本文利用随机波动率状态有限Markov链,通过有限差分方法计算美式期权的价值.这种方法既避免了建立复杂的随机波动率模型,又较大程度地改进了常数波动率的计算结果,获得与真实结果比较接近数值解,推广了二项式概率树模型.     

美式债券期权定价熵模型

基于熵定价理论,结合美式期权解析近似求解的G eske-Johnson方法,构建了美式债券期权定价熵模型,给出了标的资产为零息票债券和息票债券的美式期权估值的解析近似计算公式,并展示了具体的算法步骤.     

基于跳-分形模型的美式看涨期权定价

假设股票变化过程服从跳一分形布朗运动,根据风险中性定价原理对股票发生跳跃次数的收益求条件期望现值推导出M次离散支付红利的美式看涨期权解析定价方程,并使用外推加速法求出当M趋于无穷时方程的二重、三重正态积分多项式表达,依此计算连续支付红利美式看涨期权价值.数值模拟表明通常仅需二重正态积分多项式能产生精确价值,而在极实值状态下则需三重正态积分多项式才能满足,结合两种多项式可以编出有效数字程序评价支付红利的美式看涨期权.     

GARCH模型中美式亚式期权价值的蒙特卡罗模拟算法

我们运用 Longstaff和 Schwartz最近提出的用蒙特卡罗模拟法计算美式期权的方法在 GARCH模型中求解美式亚式期权 ,我们的结果表明和其它数值方法相比 ,这个方法不仅有相当的精确度 ,而且使用简便并具有更广泛的适用性 ,对于 GARCH模型中运用格点法难以求解的浮动执行价格的美式亚式期权同样可以得到稳定解 .     

Ant colony method to control variance reduction techniques in the Monte Carlo simulation of clinical electron linear accelerators of use in cancer therapy

The Monte Carlo simulation of clinical electron linear accelerators requires large computation times to achieve the level of uncertainty required for radiotherapy. In this context, variance reduction techniques play a fundamental role in the reduction of this computational time. Here we describe the use of the ant colony method to control the application of two variance reduction techniques: Splitting and Russian roulette. The approach can be applied to any accelerator in a straightforward way and permits the increasing of the efficiency of the simulation by a factor larger than 50.     

Importance Sampling for Backward SDEs

In this article, we explain how the importance sampling technique can be generalized from simulating expectations to computing the initial value of backward stochastic differential equations (SDEs) with Lipschitz continuous driver. By means of a measure transformation we introduce a variance reduced version of the forward approximation scheme by Bender and Denk [4 Bender , C. , and Denk , R. 2007 . A forward scheme for backward SDEs . Stochastic Processes and their Applications 117 ( 12 ): 1793 – 1812 . [Google Scholar]] for simulating backward SDEs. A fully implementable algorithm using the least-squares Monte Carlo approach is developed and its convergence is proved. The success of the generalized importance sampling is illustrated by numerical examples in the context of Asian option pricing under different interest rates for borrowing and lending.     

Efficiently pricing barrier options in a Markov-switching framework

An efficient Monte Carlo simulation for the pricing of barrier options in a Markov-switching model is presented. Compared to a brute-force approach, relying on the simulation of discretized trajectories, the presented algorithm simulates the underlying stock price process only at state changes and at maturity. Given these pieces of information, option prices are evaluated using the probability of Brownian bridges not to fall below some threshold level. It is illustrated how two methods of variance reduction, control variates and antithetic variates, further improve the algorithm. In a small case study, the algorithm is applied to the pricing of options with the EuroStoxx 50 as underlying.     

Fast Hamiltonian Monte Carlo Using GPU Computing

In recent years, the Hamiltonian Monte Carlo (HMC) algorithm has been found to work more efficiently compared to other popular Markov chain Monte Carlo (MCMC) methods (such as random walk Metropolis–Hastings) in generating samples from a high-dimensional probability distribution. HMC has proven more efficient in terms of mixing rates and effective sample size than previous MCMC techniques, but still may not be sufficiently fast for particularly large problems. The use of GPUs promises to push HMC even further greatly increasing the utility of the algorithm. By expressing the computationally intensive portions of HMC (the evaluations of the probability kernel and its gradient) in terms of linear or element-wise operations, HMC can be made highly amenable to the use of graphics processing units (GPUs). A multinomial regression example demonstrates the promise of GPU-based HMC sampling. Using GPU-based memory objects to perform the entire HMC simulation, most of the latency penalties associated with transferring data from main to GPU memory can be avoided. Thus, the proposed computational framework may appear conceptually very simple, but has the potential to be applied to a wide class of hierarchical models relying on HMC sampling. Models whose posterior density and corresponding gradients can be reduced to linear or element-wise operations are amenable to significant speed ups through the use of GPUs. Analyses of datasets that were previously intractable for fully Bayesian approaches due to the prohibitively high computational cost are now feasible using the proposed framework.     

An improved averaged two-replication procedure with Latin hypercube sampling

The averaged two-replication procedure assesses the quality of a candidate solution to a stochastic program by forming point and confidence interval estimators on its optimality gap. We present an improved averaged two-replication procedure that uses Latin hypercube sampling to form confidence intervals of optimality gap. This new procedure produces tighter and less variable interval widths by reducing the sampling error by $\sqrt{2}$. Despite having tighter intervals, it improves an earlier procedure’s asymptotic coverage probability bound from ${(1?\alpha )}^2$ to $(1?\alpha )$.     

基于藤Copula的多资产交换期权模拟定价

传统的多维Copula是用单个参数来度量多变量之间的相依关系,这限制了该类Copula在描述多变量之间相依结构.为了解决这一问题,提出了一种使用藤构造三维Copula的算法,用蒙特卡罗方法分别模拟传统的单参数三维Copula和藤构造的三维Copula,并给三资产的交换期权定价,发现藤构造的Copula在定价上与单参数多维Copula存在明显的差别,使用藤构造的Copula在描述相依结构时有较大弹性.     

A computationally efficient state-space partitioning approach to pricing high-dimensional American options via dimension reduction

This paper studies the problem of pricing high-dimensional American options. We propose a method based on the state-space partitioning algorithm developed by Jin et al. (2007) and a dimension-reduction approach introduced by Li and Wu (2006). By applying the approach in the present paper, the computational efficiency of pricing high-dimensional American options is significantly improved, compared to the extant approaches in the literature, without sacrificing the estimation precision. Various numerical examples are provided to illustrate the accuracy and efficiency of the proposed method. Pseudcode for an implementation of the proposed approach is also included.     

Smoothed Monte Carlo estimators for the time-in-the-red in risk processes

We consider a modified version of the de Finetti model in insurance risk theory in which, when surpluses become negative the company has the possibility of borrowing, and thus continue its operation. For this model we examine the problem of estimating the time-in-the red over a finite horizon via simulation. We propose a smoothed estimator based on a conditioning argument which is very simple to implement as well as particularly efficient, especially when the claim distribution is heavy tailed. We establish unbiasedness for this estimator and show that its variance is lower than the naïve estimator based on counts. Finally we present a number of simulation results showing that the smoothed estimator has variance which is often significantly lower than that of the naïve Monte-Carlo estimator.     

Revisit of stochastic mesh method for pricing American options

From an importance sampling viewpoint, Broadie and Glasserman [M. Broadie, P. Glasserman, A stochastic mesh method for pricing high-dimensional American options, Journal of Computational Finance 7 (4) (2004) 35–72] proposed a stochastic mesh method to price American options. In this paper, we revisit the method from a conditioning viewpoint, and derive some new weights.