François-Michel Boire, R. Mark Reesor, Lars Stentoft
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引用次数: 0
Abstract
This paper addresses the issue of foresight bias in the Longstaff and Schwartz (Rev Financ Stud 14(1):113–147, 2001) algorithm for American option pricing. Using standard regression theory, we estimate approximations of the local foresight bias caused by in-sample overfitting. Complementing the local sub-optimality bias estimator previously identified by Kan and Reesor (Appl Math Financ 19(3):195–217, 2012), recursive local bias corrections significantly reduce overall bias for the in-sample pricing approach where the estimated early-exercise policy depends on future simulated cash flows. The bias reduction scheme holds for general asset price processes and square-integrable option payoffs, and is computationally efficient across a wide range of option characteristics. Extensive numerical experiments show that the relative efficiency gain generally increases with the frequency of exercise opportunities and with the number of basis functions, producing the most favorable time-accuracy trade-offs when using a small number of sample paths.
期刊介绍:
Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing