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2002, WORLD SCIENTIFIC eBooks
We explain, compare and improve two algorithms to compute American or Bermudan options by Monte-Carlo. The …rst one is based on threshold optimisation in the exercise strategy (Andersen 1999). The notion of "fuzzy threshold"is introduced to ease optimisation. The second one uses a linear regression to get an estimate of the option price at intermediary dates and determine the exercise strategy (Carriere 1997, Longsta¤-Schwartz 1999). We thoroughly study the convergence of these two approaches, including a mixture of both.
Annals of Applied Probability, 2007
Pricing of American options can be achieved by solving optimal stopping problems. This in turn can be done by computing so-called continuation values, which we represent as regression functions defined by the aid of a cash flow for the next few time periods. We use Monte Carlo to generate data and apply nonparametric least squares regression estimates to estimate the continuation values from these data. The parameters of the regression estimates and of the underlying regression problems are chosen data-dependent. Results concerning consistency and rate of convergence of these estimates are presented, and the resulting pricing of American options is illustrated by the aid of simulated data.
SSRN Electronic Journal, 2000
Least-squares methods enable us to price Bermudan-style options by Monte Carlo simulation. They are based on estimating the option continuation value by least-squares. We show that the Bermudan price is maximized when this continuation value is estimated near the exercise boundary, which is equivalent to implicitly estimating the optimal exercise boundary by using the value-matching condition. Localization is the key difference with respect to global regression methods, but is fundamental for optimal exercise decisions and requires estimation of the continuation value by iterating local least-squares (because we estimate and localize the exercise boundary at the same time). In the numerical example, in agreement with this optimality, the new prices or lower bounds (i) improve upon the prices reported by other methods and (ii) are very close to the associated dual upper bounds. We also study the method's convergence.
American option pricing is challenging in terms of numerical methods as they can be exercised anytime. There is a mixture of advantages and disadvantages of particular methods. Binomial trees are simpler, faster but may not approximate any diffusion process and may be difficult to implement for high-dimensional options. On the other hand, Monte Carlo methods are computationally complex due to the large number of iterations involved but can be successfully applied to any diffusion model as well as high-dimensional options. The purpose of this thesis is to assess the efficiency of pricing methods for American and Bermudan style option on several diffusion processes and in both one and two dimensions. The thesis shows that binomial trees give a good approximation for the Ornstein-Uhlenbeck and CEV processes. Monte Carlo can be applied for American options by approximating the optimal stopping strategy with cross-sectional regression over a set of basis functions yielding lower bound for the true price. Additionally, the upper bound is obtained through a minimization problem over a set of martingales, in a duality setting.
The Journal of Computational Finance, 2001
A number of Monte Carlo simulation-based approaches have been proposed within the past decade to address the problem of pricing American-style derivatives. The purpose of this paper is to empirically test some of these algorithms on a common set of problems in order to be able to assess the strengths and weaknesses of each approach as a function of the problem characteristics. In addition, we introduce another simulation-based approach that parameterizes the early exercise curve and casts the valuation problem as an optimization problem of maximizing the expected payoff (under the martingale measure) with respect to the associated parameters, the optimization problem carried out using a simultaneous perturbation stochastic approximation (SPSA) algorithm.
The pricing of options is a very important problem encountered in financial markets today. Many problems in mathematical finance entail the computation of a particular integral. In many cases these integrals can be valued analytically, and in still more cases they can be valued using numerical integration, or computed using a partial differential equation (PDE). The famous Black-Scholes model, for instance, provides explicit closed form solutions for the values of certain (European style) call and put options. However when the number of dimensions in the problem is large, PDEs and numerical integrals become intractable, the formulas exhibiting them are complicated and difficult to evaluate accurately by conventional methods. In these cases, Monte Carlo methods often give better results, because they have proved to be valuable and flexible computational tools to calculate the value of options with multiple sources of uncertainty or with complicated features.
Journal of computational finance, 2001
We explain how a carefully chosen scheme can lead to competitive Monte Carlo algorithms for the computation of the price of Asian options. We give evidence of the efficiency of these algorithms with a mathematical study of the rate of convergence and a numerical comparison with some existing methods.
2003
Abstract In this paper we discuss accuracy issues of the Monte-Carlo method for valuing American options. Two major error sources are discussed: the discretization error of numerical methods for simulating stochastic models and the statistical error of finite samples. As the explicit Euler method is dominant in the extant literature of computational finance, it is strongly recommended to use numerical methods with higher convergence order to reduce the discretization error.
Asia-Pacific journal of financial studies
This paper presents a new methodology to approximate the value of American options by least-squares Monte Carlo simulation. Whereas Longstaff and Schwartz's approach do not utilize the underlying asset price movement, we develop several methods that incorporates it into option pricing. One category improves the R-squares from the regressions by using, [1] the weighted regression with the same regressors and, [2] new regressors which are related to the discount factor from the current decision to exercise time. The other category improves the computational speed without sacrificing the convergence level by, [1] terminating early during the backwardation procedure and, [2] decreasing the number of observations for the regressors. Finally, combining both methods, we can get improved R-squares and computational speed in comparison to Longstaff and Schwartz's approach.
We present a new method for reducing the bias present in Monte-Carlo estimators of the price of American-style contingent claims. At each exer- cise opportunity (in a time discretization), we assume there is an unbiased estimator of the claim value at the next exercise opportunity. We approx- imate the distribution of this statistic using the central limit theorem, and use this to derive an asymptotic expression for the bias. This expression is easily estimated in the context of a simulation, which allows for the straight- forward computation of bias-reduced estimators of the claim value. We con- clude by presenting a well-studied multivariate pricing example to show that this method offers significant improvements over the vanilla stochastic mesh technique, and that it is much more computationally efficient approach to re- ducing bias than nonparametric bootstrapping.
The Journal of Derivatives, 1997
A simulation-based methodology to price American options with finite exercise opportunities has recently been introduced by Broadie and Glasserman [1995a]. This method simulates the evolution of underlying assets via random trees that branch at each of the possible earlyexercise dates. From these trees, two consistent and asymptotically unbiased price estimates, one biased high and one biased low, are obtained. These two estimates can be used to give a conservative confidence interval for the option price. In this paper, we develop several enhancements to improve the efficiency of the two estimates so that the resulting confidence interval is small. Since branching can be computationally very expensive, we suggest "pruning" the trees by eliminating branching whenever possible, thus cutting down the simulation time and allowing for faster convergence of the estimates. In particular, it is shown that branching at the penultimate exercise point is certainly not required whenever a formula for pricing the corresponding European option is available. Next, in order to further improve the estimators, we forego the idea of generating branches from independent samples. Indeed, we demonstrate that if half of the branches at a node are generated using the antithetic variates of the other half, both bias and variance are reduced significantly. Further enhancement is possible by combining pruning with this technique. It is also shown that by selecting the branches from Latin hypercube samples, better results are obtained. We conclude by showing that an option with infinite exercise opportunities can be well approximated by extrapolating a series of options with finitely many exercise points.
This article presents a simple yet powerful new approach for approximating the value of America11 options by simulation. The kcy to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable in path-dependent and multifactor situations where traditional finite difference techniques cannot be used. We illustrate this technique with several realistic exatnples including valuing an option when the underlying asset follows a jump-diffusion process and valuing an America11 swaption in a 20-factor string model of the term structure.
Operations Research, 2003
American-Asian options are average-price options that allow early exercise. In this paper, we derive structural properties for the optimal exercise policy, which are then used to develop an efficient numerical algorithm for pricing such options. In particular, we show that the optimal policy is a threshold policy: The option should be exercised as soon as the average asset price reaches a characterized threshold, which can be written as a function of the asset price at that time. By exploiting this and other structural properties, we are able to parameterize the exercise boundary, and derive gradient estimators for the option payoff with respect to the parameters of the model. These estimators are then incorporated into a simulation-based algorithm to price American-Asian options. Computational experiments carried out indicate that the algorithm is very competitive with other recently proposed numerical algorithms.
2008
Among derivative financial contracts, the widely traded in the financial markets are the Bermudan-American options. However, pricing high-dimensional Bermudan-American options is quite computationally intensive and using traditional computing infrastructures may take up to hours for these computations. This can result in potential financial losses, further weakening the competitiveness of an organization. Several parallel approaches for pricing have been practiced utilizing parallel or cluster computing techniques. We aim to address this problem in the context of grid computing, relying on the ProActive Java distributed computing platform. We parallelize the classification-Monte Carlo algorithm, which relies on classification techniques from the machine learning domain, for pricing Bermudan-American options. Consequently, we evaluate the performance of two machine learning techniques, boosting and support vector machines, and compare the numerical results with respect to accuracy, speed up and their applicability in the grid settings. Furthermore, the paper also contributes to the numerical experiments of a high-dimensional Bermudan-American option with 40 underlying assets.
… and Computers in …, 2010
In this paper we present two parallel Monte Carlo based algorithms for pricing multi-dimensional Bermudan/American options. First approach relies on computation of the optimal exercise boundary while the second relies on classification of continuation and exercise values. We also evaluate the performance of both the algorithms in a desktop grid environment. We show the effectiveness of the proposed approaches in a heterogeneous computing environment, and identify scalability constraints due to the algorithmic structure.
This paper proposes several improvements to the least squares Monte Carlo (LSMC) option valuation method. We test different regression algorithms and suggest a variation to the estimation of the option continuation value, which reduces the execution time of the algorithm without any significant loss in accuracy. We test the choice of varying polynomial families with different number of basis functions and various variance reduction techniques, using a large sample of vanilla American options, and find that the use of low discrepancy sequences with Brownian bridges can increase substantially the accuracy of the simulation method. We also extend our analysis to the valuation of portfolios of compound and mutually exclusive options. For the latter, we also propose an improved algorithm which is faster and more accurate.
In this article we propose a novel approach to reduce the computational complexity of various approximation methods for pricing discrete time American or Bermudan options. Given a sequence of continuation values estimates corresponding to different levels of spatial approximation, we propose a multilevel low biased estimate for the price of the option.
The European Journal of Finance, 2010
Chen and Shen (2003) argue that we can improve the Least Squares Monte Carlo Method (LSMC) to value American options by removing the least squares regression module. This would make it not only faster but also more accurate. We demonstrate, using a large sample of 2500 put options, that the proposed algorithm-the Perfect Foresight Method (PFM)-is, as argued by the authors, faster than the LSMC algorithm but, contrary to what they state, it is not more accurate than the LSMC. In fact, the PFM algorithm incorrectly prices American options. We therefore, do not recommend the use of the PFM.
2008 Workshop on High Performance Computational Finance, 2008
Among derivative financial contracts, the widely traded in the financial markets are the Bermudan-American options. However, pricing high-dimensional Bermudan-American options is quite computationally intensive and using traditional computing infrastructures may take up to hours for these computations. This can result in potential financial losses, further weakening the competitiveness of an organization. Several parallel approaches for pricing have been practiced utilizing parallel or cluster computing techniques. We aim to address this problem in the context of grid computing, relying on the ProActive Java distributed computing platform. We parallelize the Classification-Monte Carlo algorithm, which relies on classification techniques from the machine learning domain, for pricing Bermudan-American options. Consequently, we evaluate the performance of two machine learning techniques, Boosting and Support Vector Machines, and compare the numerical results with respect to accuracy, speed up and their applicability in the grid settings. Furthermore, the paper also contributes to the numerical experiments of a high-dimensional Bermudan-American option with 40 underlying assets.
The Journal of Computational Finance, 2014
We investigate the performance of the Ordinary Least Squares (OLS) regression method in Monte Carlo simulation algorithms for pricing American options. We compare OLS regression against several alternatives and find that OLS regression underperforms methods that penalize the size of coefficient estimates. The degree of underperformance of OLS regression is greater when the number of simulation paths is small, when the number of functions in the approximation scheme is large, when European option prices are included in the approximation scheme, and when the number of exercise opportunities is large. Based on our findings, instead of using OLS regression we recommend an alternative method based on a modification of Matching Projection Pursuit.
Sadhana, 2005
Pricing financial options is amongst the most important and challenging problems in the modern financial industry. Except in the simplest cases, the prices of options do not have a simple closed form solution and efficient computational methods are needed to determine them. Monte Carlo methods have increasingly become a popular computational tool to price complex financial options, especially when the underlying space of assets has a large dimensionality, as the performance of other numerical methods typically suffer from the 'curse of dimensionality'. However, even Monte-Carlo techniques can be quite slow as the problem-size increases, motivating research in variance reduction techniques to increase the efficiency of the simulations. In this paper, we review some of the popular variance reduction techniques and their application to pricing options. We particularly focus on the recent Monte-Carlo techniques proposed to tackle the difficult problem of pricing American options. These include: regression-based methods, random tree methods and stochastic mesh methods. Further, we show how importance sampling, a popular variance reduction technique, may be combined with these methods to enhance their effectiveness. We also briefly review the evolving options market in India.
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