financial derivatives and real options: an empirical evaluation of sampling methods in risk analysis simulation: quasi-monte carlo, descriptive sampling, and Latin Hypercube sampling

Authors:
Eduardo Saliby;Flavio Pacheco
Affiliations:
Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil;Microeconomics and Investment Analysis, Banco Boreal de Investimentos S/A, Av. Pres. Wilson, Rio de Janeiro, RJ, Brazil
Venue:
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Year:
2002

Citing 4
Cited 1

Random number generation and quasi-Monte Carlo methods

Random number generation and quasi-Monte Carlo methods
Quasi-Monte Carlo integration

Journal of Computational Physics
Descriptive sampling: an improvement over Latin hypercube sampling

Proceedings of the 29th conference on Winter simulation
Latin supercube sampling for very high-dimensional simulations

ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation

New simulation methodology for finance: work reduction in financial simulations

Proceedings of the 35th conference on Winter simulation: driving innovation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper compares the performance, in terms of convergence rates and precision of the estimates, for six Monte Carlo Simulation sampling methods: Quasi-Monte Carlo using Halton, Sobol, and Faure numeric sequences; Descriptive Sampling, based on the use of deterministic sets and Latin Hypercube Sampling, based on stratified numerical sets. Those methods are compared to the classical Monte Carlo. The comparison was made for two basic risky applications: the first one evaluates the risk in a decision making process when launching a new product; the second evaluates the risk of accomplishing an expected rate of return in a correlated stock portfolio. Descriptive sampling and Latin Hypercube sampling have shown the best aggregate results.