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.     

Convergence analysis of a monotonic penalty method for American option pricing

This paper is devoted to study the convergence analysis of a monotonic penalty method for pricing American options. A monotonic penalty method is first proposed to solve the complementarity problem arising from the valuation of American options, which produces a nonlinear degenerated parabolic PDE with Black-Scholes operator. Based on the variational theory, the solvability and convergence properties of this penalty approach are established in a proper infinite dimensional space. Moreover, the convergence rate of the combination of two power penalty functions is obtained.     

New Brownian bridge construction in quasi-Monte Carlo methods for computational finance

Quasi-Monte Carlo (QMC) methods have been playing an important role for high-dimensional problems in computational finance. Several techniques, such as the Brownian bridge (BB) and the principal component analysis, are often used in QMC as possible ways to improve the performance of QMC. This paper proposes a new BB construction, which enjoys some interesting properties that appear useful in QMC methods. The basic idea is to choose the new step of a Brownian path in a certain criterion such that it maximizes the variance explained by the new variable while holding all previously chosen steps fixed. It turns out that using this new construction, the first few variables are more “important” (in the sense of explained variance) than those in the ordinary BB construction, while the cost of the generation is still linear in dimension. We present empirical studies of the proposed algorithm for pricing high-dimensional Asian options and American options, and demonstrate the usefulness of the new BB.     

Implementing quasi-Monte Carlo simulations with linear transformations

The curse of dimensionality limits the accuracy of the quasi-Monte Carlo (QMC) method in high-dimensional problems. Imai and Tan (Proceedings of the 2002 winter simulation conference, 2002; Monte Carlo and Quasi-Monte Carlo Methods 2002, pp 275?C292, Springer, Berlin, 2004; J Comput Finance 10(2):129?C155, 2007) have proposed a dimension reduction technique, named linear transformation (LT), aiming to improve the efficiency of the QMC method. We investigate this approach in detail and make it more convenient. We implement a faster QR decomposition that considerably reduces the computational burden. The efficacy of our algorithm is illustrated by considering two high-dimensional option pricing problems: Asian basket options in the Black?CScholes (BS) model and Asian options in the Cox?CIngersoll?CRoss (CIR) model. We employ a QMC generator only for the components selected by the LT construction and use Latin hypercube sampling (LHS) for all the others. Finally, we compare our results to those obtained by different random number generators and standard algorithms; subsequently, we benchmark our computational times against those presented in Imai and Tan (J Comput Finance 10(2):129?C155, 2007).     

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.     

An irregular grid approach for pricing high-dimensional American options

We propose and test a new method for pricing American options in a high-dimensional setting. The method is centered around the approximation of the associated complementarity problem on an irregular grid. We approximate the partial differential operator on this grid by appealing to the SDE representation of the underlying process and computing the root of the transition probability matrix of an approximating Markov chain. Experimental results in five-dimensions are presented for four different payoff functions.     

基于近似对冲跳跃风险的美式看跌期权定价及数值解法研究

考虑了基于近似对冲跳跃风险的美式看跌期权定价问题。首先,运用近似对冲跳跃风险、广义It 公式及无套利原理,得到了跳-扩散过程下的期权定价模型及期权价格所满足的偏微分方程。然后建立了美式看跌期权定价模型的隐式差分近似格式,并且证明了该差分格式具有的相容性、适定性、稳定性和收敛性。最后,数值实验表明,用本文方法为跳-扩散模型中的美式期权定价是可行的和有效的。     

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.     

The discontinuous Galerkin method for discretely observed Asian options

Asian options represent an important subclass of the path-dependent contracts that are identified by payoff depending on the average of the underlying asset prices over the prespecified period of option lifetime. Commonly, this average is observed at discrete dates, and also, early exercise features can be admitted. As a result, analytical pricing formulae are not always available. Therefore, some form of a numerical approximation is essential for efficient option valuation. In this paper, we study a PDE model for pricing discretely observed arithmetic Asian options with fixed as well as floating strike for both European and American exercise features. The pricing equation for such options is similar to the Black-Scholes equation with 1 underlying asset, and the corresponding average appears only in the jump conditions across the sampling dates. The objective of the paper is to present the comprehensive methodological concept that forms and improves the valuation process. We employ a robust numerical procedure based on the discontinuous Galerkin approach arising from the piecewise polynomial generally discontinuous approximations. This technique enables a simple treatment of discrete sampling by incorporation of jump conditions at each monitoring date. Moreover, an American early exercise constraint is directly handled as an additional nonlinear source term in the pricing equation. The proposed solving procedure is accompanied by an empirical study with practical results compared to reference values.     

Option pricing with mean reversion and stochastic volatility

Many underlying assets of option contracts, such as currencies, commodities, energy, temperature and even some stocks, exhibit both mean reversion and stochastic volatility. This paper investigates the valuation of options when the underlying asset follows a mean-reverting lognormal process with stochastic volatility. A closed-form solution is derived for European options by means of Fourier transform. The proposed model allows the option pricing formula to capture both the term structure of futures prices and the market implied volatility smile within a unified framework. A bivariate trinomial lattice approach is introduced to value path-dependent options with the proposed model. Numerical examples using European options, American options and barrier options demonstrate the use of the model and the quality of the numerical scheme.     

Differential quadrature method for pricing American options

In this article, differential quadrature method (DQM), a highly accurate and efficient numerical method for solving nonlinear problems, is used to overcome the difficulty in determining the optimal exercise boundary of American option. The following three parts of the problem in pricing American options are solved. The first part is how to treat the uncertainty of the early exercise boundary, or free boundary in the language of the PDE treatment of the American option, because American options can be exercised before the date of expiration. The second part is how to solve the nonlinear problem, because the problem of pricing American options is nonlinear. And the third part is how to treat the initial value condition with the singularity and the boundary conditions in the DQM. Numerical results for the free boundary of American option obtained by both DQM and finite difference method (FDM) are given and from which it can be seen the computational efficiency is greatly improved by DQM. © 2002 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 18: 711–725, 2002; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/num.10028.     

Applying a Power Penalty Method to Numerically Pricing American Bond Options

In this paper, we aim to develop a numerical scheme to price American options on a zero-coupon bond based on a power penalty approach. This pricing problem is formulated as a variational inequality problem (VI) or a complementarity problem (CP). We apply a fitted finite volume discretization in space along with an implicit scheme in time, to the variational inequality problem, and obtain a discretized linear complementarity problem (LCP). We then develop a power penalty approach to solve the LCP by solving a system of nonlinear equations. The unique solvability and convergence of the penalized problem are established. Finally, we carry out numerical experiments to examine the convergence of the power penalty method and to testify the efficiency and effectiveness of our numerical scheme.     

Computational Methods for Pricing American Put Options

This work develops computational methods for pricing American put options under a Markov-switching diffusion market model. Two methods are suggested in this paper. The first method is a stochastic approximation approach. It can handle option pricing in a finite horizon, which is particularly useful in practice and provides a systematic approach. It does not require calibration of the system parameters nor estimation of the states of the switching process. Asymptotic results of the recursive algorithms are developed. The second method is based on a selling rule for the liquidation of a stock for perpetual options. Numerical results using stochastic approximation and Monte Carlo simulation are reported. Comparisons of different methods are made. This research was supported in part by the National Science Foundation and in part by the Wayne State University Research Enhancement Program.     

Pricing American options with uncertain volatility through stochastic linear complementarity models

We consider the problem of pricing American options with uncertain volatility and propose two deterministic formulations based on the expected value method and the expected residual minimization method for a stochastic complementarity problem. We give sufficient conditions that ensure the existence of a solution of those deterministic formulations. Furthermore we show numerical results and discuss the usefulness of the proposed approach.     

An efficient algorithm for Bermudan barrier option pricing

An efficient option pricing method based on Fourier-cosine expansions was presented by Fang and Oosterlee for European options in 2008,and later,this method was also used by them to price early-exercis...     

A mathematical modeling for the lookback option with jump-diffusion using binomial tree method

The binomial tree method (BTM), first proposed by Cox et al. (1979) [4] in diffusion models and extended by Amin (1993) [9] to jump-diffusion models, is one of the most popular approaches to pricing options. In this paper, we present a binomial tree method for lookback options in jump-diffusion models and show its equivalence to certain explicit difference scheme. We also prove the existence and convergence of the optimal exercise boundary in the binomial tree approximation to American lookback options and give the terminal value of the genuine exercise boundary. Further, numerical simulations are performed to illustrate the theoretical results.     

Efficient L-stable method for parabolic problems with application to pricing American options under stochastic volatility

Efficient L-stable numerical method for semilinear parabolic problems with nonsmooth initial data is proposed and implemented to solve Heston’s stochastic volatility model based PDE for pricing American options under stochastic volatility. The proposed new method is also used to solve two asset American options pricing problem. Cox and Matthews [S.M. Cox, P.C. Matthews, Exponential time differencing for stiff systems, Journal of Computational Physics 176 (2002) 430-455] developed a class of exponential time differencing Runge-Kutta schemes (ETDRK) for nonlinear parabolic problems. Kassam and Trefethen [A.K. Kassam, L.N. Trefethen, Fourth-order time stepping for stiff PDEs, SIAM Journal on Scientific Computing 26 (4) (2005) 1214-1233] showed that while computing certain functions involved in the Cox-Matthews schemes, severe cancelation errors can occur which affect the accuracy and stability of the schemes. Kassam and Trefethen proposed complex contour integration technique to implement these schemes in a way that avoids these cancelation errors. But this approach creates new difficulties in choosing and evaluating the contour integrals for larger problems. We modify the ETDRK schemes using positivity preserving Padé approximations of the matrix exponential functions and construct computationally efficient parallel version using splitting technique. As a result of this approach it is required only to solve several backward Euler linear problems in serial or parallel.     

Unifying pricing formula for several stochastic volatility models with jumps

In this paper, we introduce a unifying approach to option pricing under continuous‐time stochastic volatility models with jumps. For European style options, a new semi‐closed pricing formula is derived using the generalized complex Fourier transform of the corresponding partial integro‐differential equation. This approach is successfully applied to models with different volatility diffusion and jump processes. We also discuss how to price options with different payoff functions in a similar way. In particular, we focus on a log‐normal and a log‐uniform jump diffusion stochastic volatility model, originally introduced by Bates and Yan and Hanson, respectively. The comparison of existing and newly proposed option pricing formulas with respect to time efficiency and precision is discussed. We also derive a representation of an option price under a new approximative fractional jump diffusion model that differs from the aforementioned models, especially for the out‐of‐the money contracts. Copyright © 2017 John Wiley & Sons, Ltd.     

高维空间中数据的相似性度量

高维空间中数据之间的相似性度量是目前数据挖掘、信息处理与检索等领域所面临的一个重要问题.文章在总结分析了高维数据的特点以及现有的一些度量方法的基础上,提出了一种新的度量方式,该方法在对高维数据进行相似性度量之前,首先对原始数据空间进行网格划分.文章的最后对其有效性作了定量分析,实验证明,该方式是行之有效的.     

Convergence of the binomial tree method for Asian options in jump-diffusion models

The binomial tree methods (BTM), first proposed by Cox, Ross and Rubinstein [J. Cox, S. Ross, M. Rubinstein, Option pricing: A simplified approach, J. Finan. Econ. 7 (1979) 229-264] in diffusion models and extended by Amin [K.I. Amin, Jump diffusion option valuation in discrete time, J. Finance 48 (1993) 1833-1863] to jump-diffusion models, is one of the most popular approaches to pricing options. In this paper, we present a binomial tree method for Asian options in jump-diffusion models and show its equivalence to certain explicit difference scheme. Employing numerical analysis and the notion of viscosity solution, we prove the uniform convergence of the binomial tree method for European-style and American-style Asian options.