Abstract
Infinitely divisible random vectors and Lévy processes without Gaussian component admit representations with shot noise series. To enhance efficiency of the series representation in Monte Carlo simulations, we discuss variance reduction methods, such as stratified sampling, control variates and importance sampling, applied to exponential interarrival times forming the shot noise series. We also investigate the applicability of the generalized linear transformation method in the quasi-Monte Carlo framework to random elements of the series representation. Although implementation of the proposed techniques requires a small amount of initial work, the techniques have the potential to yield substantial improvements in estimator efficiency, as the plain use of the series representation in those frameworks is often expensive. Numerical results are provided to illustrate the effectiveness of our approaches.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Asmussen, S., Glynn, P.W.: Stochastic Simulation: Algorithms and Analysis, Springer, New York (2007)
Avramidis, A.N., L’Ecuyer, P.: Efficient Monte Carlo and quasi-Monte Carlo option pricing under the variance gamma model, Management Science, 52(12) 1930–1944 (2006)
Bondesson, L.: On simulation from infinitely divisible distributions, Advances in Applied Probability, 14(4) 855–869 (1982)
Caflisch, R., Morokoff, W., Owen, A.: Valuation of mortgaged-backed securities using Brownian bridges to reduce effective dimension, Journal of Computational Finance, 1(1) 27–46 (1997)
Ferguson, T.S., Klass, M.J.: A representation of independent increment processes with Gaussian components, Annals of Mathematical Statistics, 43(5) 1634–1643 (1972)
Imai, J., Kawai, R.: Quasi-Monte Carlo methods for infinitely divisible random vectors via series representations, SIAM Journal on Scientific Computing, 32(4) 1879–1897 (2010)
Imai, J., Kawai, R.: Numerical inverse Lévy measure method for infinite shot noise series representation, preprint.
Imai, J., Kawai, R.: On finite truncation of infinite shot noise series representation of tempered stable laws, Physica A, 390(23–24) 4411–4425 (2011)
Imai, J., Tan, K.S.: A general dimension reduction technique for derivative pricing, Journal of Computational Finance, 10 129–155 (2007)
Imai, J., Tan, K.S.: An accelerating quasi-Monte Carlo method for option pricing under the generalized hyperbolic Lévy process, SIAM Journal on Scientific Computing, 31(3) 2282–2302 (2009)
Kawai, R.: An importance sampling method based on the density transformation of Lévy processes, Monte Carlo Methods and Applications, 12(2) 171–186 (2006)
Kawai, R.: Adaptive Monte Carlo variance reduction with two-time-scale stochastic approximation, Monte Carlo Methods and Applications, 13(3) 197–217 (2007)
Kawai, R.: Adaptive Monte Carlo variance reduction for Lévy processes with two-time-scale stochastic approximation, Methodology and Computing in Applied Probability, 10(2) 199–223 (2008)
Kawai, R.: Optimal importance sampling parameter search for Lévy processes via stochastic approximation, SIAM Journal on Numerical Analysis, 47(1) 293–307 (2008)
Kawai, R.: Asymptotically optimal allocation of stratified sampling with adaptive variance reduction by strata, ACM Transactions on Modeling and Computer Simulation, 20(2) Article 9 (2010)
Kawai, R., Takeuchi, A.: Greeks formulas for an asset price model with gamma processes, Mathematical Finance, 21(4) 723–742 (2011)
L’Ecuyer, P., J-S. Parent-Chartier, M. Dion: Simulation of a Lévy process by PCA sampling to reduce the effective dimension, In: S.J. Mason, R.R., et al. (Eds.) Proceedings of the 2008 Winter Simulation Conference, 436–442 (2008)
Leobacher, G.: Stratified sampling and quasi-Monte Carlo simulation of Lévy processes, Monte Carlo Methods and Applications, 12(3–4) 231–238 (2006)
LePage, R.: Multidimensional infinitely divisible variables and processes II, In: Lecture Notes in Mathematics 860, Springer-Verlag, Berlin, New York, Heidelberg, 279–284 (1980)
Madan, D.B., Seneta, E.: The variance gamma (V.G.) model for share market returns, Journal of Business, 63(4) 511–524 (1990)
Madan, D.B., Yor, M.: Representing the CGMY and Meixner Lévy processes as time changed Brownian motions, Journal of Computational Finance, 12(1) (2008)
Owen, A.B.: Randomly permuted (t, m, s)-nets and (t, s)-sequence, In: Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, H. Niederreiter, J.S. Shiue (Eds.) Springer-Verlag, New York, 299–317 (1995)
Ribeiro, C., Webber, N.: Valuing path-dependent options in the variance-gamma model by Monte Carlo with a gamma bridge, Journal of Computational Finance, 7, 81–100 (2003)
Rosiński, J.: Series representations of Lévy processes from the perspective of point processes, In: Lévy Processes – Theory and Applications, O-E. Barndorff-Nielsen et al. (Eds.) Birkhäuser, Boston, 401–415 (2001)
Sato, K.: Lévy processes and infinitely divisible distributions, Cambridge University Press, Cambridge (1999)
Sobol’, I.M.: Distribution of points in a cube and integration nets, UMN, 5(131) 271–272 (1966)
Wang, X., Fang, K.T.: The effective dimension and quasi-Monte Carlo integration, Journal of Complexity, 19(2) 101–124 (2003)
Acknowledgements
The authors would like to thank an anonymous referee for various valuable comments and Japan Society for the Promotion of Science for Grant-in-Aid for Scientific Research 21340024 and 21710157.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kawai, R., Imai, J. (2012). On Monte Carlo and Quasi-Monte Carlo Methods for Series Representation of Infinitely Divisible Laws. In: Plaskota, L., Woźniakowski, H. (eds) Monte Carlo and Quasi-Monte Carlo Methods 2010. Springer Proceedings in Mathematics & Statistics, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27440-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-27440-4_26
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27439-8
Online ISBN: 978-3-642-27440-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)