Abstract
Effective dimension, an indicator for the difficulty of high-dimensional integration, describes whether a function can be well approximated by low-dimensional terms or sums of low-order terms. Some problems in option pricing are believed to have low effective dimensions, which help explain the success of quasi-Monte Carlo (QMC) methods recently observed in financial engineering. This paper provides a way of studying the structure of effective dimensions by finding a proper space the function of interest belongs to and then determining the effective dimension of that space. To this end, we extend the definitions of effective dimensions to weighted function spaces with product-order-dependent weights and give bounds on norms and variances. Furthermore, we show that the proposed method is applicable to functions arising in option pricing and consequently offers some hints on the performance of QMC methods.
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Fan, C., Wu, Q. & Khan, Y. A new method to explore the structure of effective dimensions for functions. Neural Comput & Applic 30, 2479–2487 (2018). https://doi.org/10.1007/s00521-016-2814-6
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DOI: https://doi.org/10.1007/s00521-016-2814-6