Derivatives and credit risk: enhanced quasi-monte carlo methods with dimension reduction

Authors:
Junichi Imai;Ken Seng Tan
Affiliations:
Iwate Prefectural University, Takizawa-aza-sugo, Takizawa, Iwate;University Avenue West, Waterloo, Ontario
Venue:
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Year:
2002

Citing 4
Cited 6

Implementation and tests of low-discrepancy sequences

ACM Transactions on Modeling and Computer Simulation (TOMACS)
Quasi-Monte Carlo methods in numerical finance

Management Science
Latin supercube sampling for very high-dimensional simulations

ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Smoothness and dimension reduction in Quasi-Monte Carlo methods

Mathematical and Computer Modelling: An International Journal

State of the art tutorial II: simulations for financial engineering: efficient simulations for option pricing

Proceedings of the 35th conference on Winter simulation: driving innovation
Randomized Quasi-Monte Carlo: a tool for improving the efficiency of simulations in finance

WSC '04 Proceedings of the 36th conference on Winter simulation
Quasi-Monte Carlo methods in finance

WSC '04 Proceedings of the 36th conference on Winter simulation
On the Effects of Dimension Reduction Techniques on Some High-Dimensional Problems in Finance

Operations Research
Simulation of a Lévy process by PCA sampling to reduce the effective dimension

Proceedings of the 40th Conference on Winter Simulation
Dimension Reduction Techniques in Quasi-Monte Carlo Methods for Option Pricing

INFORMS Journal on Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent years, the quasi-Monte Carlo approach for pricing high-dimensional derivative securities has been used widely relative to other competitive approaches such as the Monte Carlo methods. Such success can be, in part, attributed to the notion of effective dimension of the finance problems. In this paper, we provide additional insight on the connection between the effective dimension and the quasi-Monte Carlo method. We also propose a dimension reduction technique which further enhances the quasi-Monte Carlo method for derivative pricing. The efficiency of the proposed method is illustrated by applying it to high-dimensional multi-factor path-dependent derivative securities.