An Integrated Quasi-Monte Carlo Method for Handling High Dimensional Problems with Discontinuities in Financial Engineering

Zhijian He () and Xiaoqun Wang ()
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Zhijian He: South China University of Technology
Xiaoqun Wang: Tsinghua University

Computational Economics, 2021, vol. 57, issue 2, No 13, 693-718

Abstract: Abstract Quasi-Monte Carlo (QMC) method is a useful numerical tool for pricing and hedging of complex financial derivatives. These problems are usually of high dimensionality and discontinuities. The two factors may significantly deteriorate the performance of the QMC method. This paper develops an integrated method that overcomes the challenges of the high dimensionality and discontinuities concurrently. For this purpose, a smoothing method is proposed to remove the discontinuities for some typical functions arising from financial engineering. To make the smoothing method applicable for more general functions, a new path generation method is designed for simulating the paths of the underlying assets such that the resulting function has the required form. The new path generation method has an additional power to reduce the effective dimension of the target function. Our proposed method caters for a large variety of model specifications, including the Black–Scholes, exponential normal inverse Gaussian Lévy, and Heston models. Numerical experiments dealing with these models show that in the QMC setting the proposed smoothing method in combination with the new path generation method can lead to a dramatic variance reduction for pricing exotic options with discontinuous payoffs and for calculating options’ Greeks. The investigation on the effective dimension and the related characteristics explains the significant enhancement of the combined procedure.

Keywords: Simulation; Option pricing; Quasi-Monte Carlo methods; Smoothing; Dimension reduction (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10614-020-09976-2

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