期权定价问题的数值方法

本文研究以股票为标的资产的美式看跌期权定价问题的数值方法,即有限元方法。通过将所考虑的问题转化为等价的变分不等式,并利用积分恒等式与超逼近分析技术,得到了半离散有限元方法的最优L~2-模与L~∞-模的误差估计。     

求解美式回望期权的有限元方法

期权作为一种金融衍生产品,在欧美国家一直很受欢迎.由于其规避风险的特性,期权也吸引了中国投资者的兴趣.基于市场的需求,2015年初,上海证券交易所推出了中国首批期权产品,期权定价问题的研究热潮正席卷全球.本文研究的美式回望期权,是一种路径相关的期权,其支付函数不仅依赖于标的资产的现值,也依赖其历史最值.分析回望期权的特点,不难发现:1)这类期权空间变量的变化范围为二维无界不规则区域,难以应用数值方法直接求解;2)最佳实施边界未知,使得该问题变得高度非线性.本文的主要工作就是解决这两个困难,得到回望期权和最佳实施边界的数值逼近结果.现有的处理问题1)的有效方法是采用标准变量替换、计价单位变换以及Landau变换将定价模型化为一个[0,1]区间上的非线性抛物问题,本文也将沿用这些技巧处理问题1).进一步,采用有限元方法离散简化后的定价模型,并论证了数值解的非负性,提出了利用Newton法求解离散化的非线性系统.最后,通过数值模拟,验证了本文所提算法的高效性和准确性.     

自适应隐式有限差分方法求解美式期权定价问题

本文针对美式期权的定价问题设计了基于有限差分方法的预估-校正数值算法.该算法采用显式离散格式先对自由边界条件进行预估,再对经过变量替换后的关于期权价格的偏微分方程采用隐式格式离散,并用Fourier方法分析了此离散格式的稳定性.接下来,引入基于Richardson外推法的后验误差指示子.这个后验误差指示子能够在给定的误差阈值范围内,针对期权价格和自由边界找到合适的网格划分.最后,通过设计多组数值实验并与Fazio^[1]采用显式离散格式算得的数值结果相比较,验证了所提算法的有效性,稳定性和收敛性.     

美式期权定价中非局部问题的有限元方法

在本文中 ,我们关心的是美式期权的有限元方法 .首先 ,根据 [4 ]我们对所讨论的问题引进一个新奇的实用的方法 ,它涉及到对原问题重新形成准确的数学公式 ,使得数值解的计算可以在非常小的区域上进行 ,从而该算法计算速度快精度高 .进而 ,我们利用超逼近分析技术得到了有限元解关于 L2 -模的最优估计 .     

CEV过程下回望期权定价的高精度收敛差分算法

主要研究了CEV过程下一类回望期权的定价的数值解法问题.首先对期权价格所满足的微分方程中的空间变量进行半离散化处理,得到了具体的半离散化差分格式,然后证明了该差分格式具有稳定性和收敛性.数值试验表明本文算法是一个稳定收敛的算法.     

随机波动率与双指数跳扩散组合模型的美式期权定价

在股价满足Cox-Ingersoll-Ross(CIR)随机波动率与Kou的双指数跳扩散组合模型下,利用随机分析方法讨论了美式看跌期权函数及最佳实施边界的性质.应用一阶线性近似实施边界获得了期权价格的拟解析式和实施边界满足的非线性方程.进一步,应用梯形法离散处理方程式内积分表达式,建立了期权最佳实施边界和价格的数值算法.最后分别给出了常数波动率或CIR随机波动率的数值实例.     

有红利美式看跌期权定价树图模型的自适应性改进

本文基于支付固定红利美式看跌期权的三叉树图定价模型,对其进行了自适应性改进,从而解决了树图模型所存在的因为时间离散、状态不连续而产生的"非线性误差"问题.最后给出了实证分析,并与二叉树图和三叉树图进行了比较,结果表明进行自适应性改进后可以得到更加精确、有效的数值解.     

美式看跌期权定价的一种混合数值方法

本文对美式看跌期权的定价提供了一种新的混合数值方法,即快速傅里叶变换法加龙格-库塔法.首先将美式看跌期权价格所满足的Black-Scholes微分方程定解问题转化为一个标准的抛物型初、边值问题,然后通过傅里叶变换,使之转换为一个不带股价变量的常微分方程初值问题,再利用龙格-库塔法对其进行数值求解.数值实验表明,本文算法是一种快速的高精度的算法.     

投影三角分解法定价带随机波动率的美式期权

考虑数值求解Heston随机波动率美式期权定价问题,通过在空间方向采用中心差分格式离散二维偏微分算子,在时间方向利用隐式交替方向格式,将美式期权定价问题转化成求解每个时间层上的若干个线性互补问题.针对一般美式期权定价模型离散得到的线性互补问题,构造出投影三角分解法进行求解,并在理论上给出算法的收敛条件.数值实验表明,所构造的数值方法对于求解美式期权定价问题是有效的,并且优于经典的投影超松弛迭代法和算子分裂方法.     

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.     

美式债券期权定价问题的有限元方法

The aim of this paper is to investigate the finite element methods for pricing the American put option on bonds. Based on a new variational inequality equation for the option pricing problems, both semidiscrete and fully discretized finite element approximation schemes are established. It is proved that the finite element methods are stable and convergent under L2 and H^1 norms.     

Sequential Monte Carlo Methods for Option Pricing

In this article, we provide a review and development of sequential Monte Carlo (SMC) methods for option pricing. SMC are a class of Monte Carlo-based algorithms, that are designed to approximate expectations w.r.t a sequence of related probability measures. These approaches have been used successfully for a wide class of applications in engineering, statistics, physics, and operations research. SMC methods are highly suited to many option pricing problems and sensitivity/Greek calculations due to the nature of the sequential simulation. However, it is seldom the case that such ideas are explicitly used in the option pricing literature. This article provides an up-to-date review of SMC methods, which are appropriate for option pricing. In addition, it is illustrated how a number of existing approaches for option pricing can be enhanced via SMC. Specifically, when pricing the arithmetic Asian option w.r.t a complex stochastic volatility model, it is shown that SMC methods provide additional strategies to improve estimation.     

High-order computational methods for option valuation under multifactor models

Many of the different numerical techniques in the partial differential equations framework for solving option pricing problems have employed only standard second-order discretization schemes. A higher-order discretization has the advantage of producing low size matrix systems for computing sufficiently accurate option prices and this paper proposes new computational schemes yielding high-order convergence rates for the solution of multi-factor option problems. These new schemes employ Galerkin finite element discretizations with quadratic basis functions for the approximation of the spatial derivatives in the pricing equations for stochastic volatility and two-asset option problems and time integration of the resulting semi-discrete systems requires the computation of a single matrix exponential. The computations indicate that this combination of high-order finite elements and exponential time integration leads to efficient algorithms for multi-factor problems. Highly accurate European prices are obtained with relatively coarse meshes and high-order convergence rates are also observed for options with the American early exercise feature. Various numerical examples are provided for illustrating the accuracy of the option prices for Heston’s and Bates stochastic volatility models and for two-asset problems under Merton’s jump-diffusion model.     

Unstructured meshing for two asset barrier options

Discretely observed barriers introduce discontinuities in the solution of two asset option pricing partial differential equations (PDEs) at barrier observation dates. Consequently, an accurate solution of the pricing PDE requires a fine mesh spacing near the barriers. Non-rectangular barriers pose difficulties for finite difference methods using structured meshes. It is shown that the finite element method (FEM) with standard unstructured meshing techniques can lead to significant efficiency gains over structured meshes with a comparable number of vertices. The greater accuracy achieved with unstructured meshes is shown to more than compensate for a greater solve time due to an increase in sparse matrix condition number. Results are presented for a variety of barrier shapes, including rectangles, ellipses, and rotations of these shapes. It is claimed that ellipses best represent constant (risk neutral) probability regions of underlying asset price-point movement, and are thus natural two-dimensional barrier shapes.     

Convergence of a fitted finite volume method for the penalized Black–Scholes equation governing European and American Option pricing

In this paper we present an analysis of a numerical method for a degenerate partial differential equation, called the Black–Scholes equation, governing American and European option pricing. The method is based on a fitted finite volume spatial discretization and an implicit time stepping technique. The analysis is performed within the framework of the vertical method of lines, where the spatial discretization is formulated as a Petrov–Galerkin finite element method with each basis function of the trial space being determined by a set of two-point boundary value problems. We establish the stability and an error bound for the solutions of the fully discretized system. Numerical results are presented to validate the theoretical results.     

An approximate moving boundary method for American option pricing

We present a method to solve the free-boundary problem that arises in the pricing of classical American options. Such free-boundary problems arise when one attempts to solve optimal-stopping problems set in continuous time. American option pricing is one of the most popular optimal-stopping problems considered in literature. The method presented in this paper primarily shows how one can leverage on a one factor approximation and the moving boundary approach to construct a solution mechanism. The result is an algorithm that has superior runtimes-accuracy balance to other computational methods that are available to solve the free-boundary problems. Exhaustive comparisons to other pricing methods are provided. We also discuss a variant of the proposed algorithm that allows for the computation of only one option price rather than the entire price function, when the requirement is such.     

Valuing Asian options using the finite element method and duality techniques

The main objective of this paper is to develop an adaptive finite element method for computation of the values, and different sensitivity measures, of the Asian option with both fixed and floating strike. The pricing is based on Black–Scholes PDE-model and a method developed by Ve?e? where the resulting PDEs are of parabolic type in one spatial dimension and can be applied to both continuous and discrete Asian options. We propose using an adaptive finite element method which is based on a posteriori estimates of the error in desired quantities, which we derive using duality techniques. The a posteriori error estimates are tested and verified, and are used to calculate optimal meshes for each type of option. The use of adapted meshes gives superior accuracy and performance with less degrees of freedom than using uniform meshes. The suggested adaptive finite element method is stable, gives fast and accurate results, and can be applied to other types of options as well.     

Compact finite difference method for American option pricing

A compact finite difference method is designed to obtain quick and accurate solutions to partial differential equation problems. The problem of pricing an American option can be cast as a partial differential equation. Using the compact finite difference method this problem can be recast as an ordinary differential equation initial value problem. The complicating factor for American options is the existence of an optimal exercise boundary which is jointly determined with the value of the option. In this article we develop three ways of combining compact finite difference methods for American option price on a single asset with methods for dealing with this optimal exercise boundary. Compact finite difference method one uses the implicit condition that solutions of the transformed partial differential equation be nonnegative to detect the optimal exercise value. This method is very fast and accurate even when the spatial step size h   is large (h?0.1)$(h?0.1)$. Compact difference method two must solve an algebraic nonlinear equation obtained by Pantazopoulos (1998) at every time step. This method can obtain second order accuracy for space x and requires a moderate amount of time comparable with that required by the Crank Nicolson projected successive over relaxation method. Compact finite difference method three refines the free boundary value by a method developed by Barone-Adesi and Lugano [The saga of the American put, 2003], and this method can obtain high accuracy for space x. The last two of these three methods are convergent, moreover all the three methods work for both short term and long term options. Through comparison with existing popular methods by numerical experiments, our work shows that compact finite difference methods provide an exciting new tool for American option pricing.     

A new predictor-corrector scheme for valuing American puts

In this paper, we present a new numerical scheme, based on the finite difference method, to solve American put option pricing problems. Upon applying a Landau transform or the so-called front-fixing technique [19] to the Black-Scholes partial differential equation, a predictor-corrector finite difference scheme is proposed to numerically solve the nonlinear differential system. Through the comparison with Zhu’s analytical solution [35], we shall demonstrate that the numerical results obtained from the new scheme converge well to the exact optimal exercise boundary and option values. The results of our numerical examples suggest that this approach can be used as an accurate and efficient method even for pricing other types of financial derivative with American-style exercise.