IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v328y2026i1p162-173.html

Controlling antithetic variates

Author

Listed:
  • Kawai, Reiichiro

Abstract

We establish and investigate a theoretical framework for controlling covariance matrices in the method of antithetic variates through control variates to further reduce estimator variance. Instead of preemptively and carefully designing an estimator vector with negatively correlated components, the proposed framework starts with a predefined estimator vector that incorporates specified control variates. The weights and control matrix are then analytically determined through matrix algebra. The joint optimality of the resulting estimator variance is ensured with respect to both the weights and the control matrix, with closed-form implementable formulas derived for the optimal parameter pair. Numerical results are provided for various typical examples to illustrate the effectiveness, potential, and challenges of the proposed framework.

Suggested Citation

  • Kawai, Reiichiro, 2026. "Controlling antithetic variates," European Journal of Operational Research, Elsevier, vol. 328(1), pages 162-173.
  • Handle: RePEc:eee:ejores:v:328:y:2026:i:1:p:162-173
    DOI: 10.1016/j.ejor.2025.08.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221725006642
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2025.08.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Philipson, Pete & Hickey, Graeme L. & Crowther, Michael J. & Kolamunnage-Dona, Ruwanthi, 2020. "Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    2. Reuven Y. Rubinstein & Ruth Marcus, 1985. "Efficiency of Multivariate Control Variates in Monte Carlo Simulation," Operations Research, INFORMS, vol. 33(3), pages 661-677, June.
    3. Chimyung Kwon & Jeffrey D. Tew, 1994. "Strategies for Combining Antithetic Variates and Control Variates in Designed Simulation Experiments," Management Science, INFORMS, vol. 40(8), pages 1021-1034, August.
    4. Athanassios N. Avramidis & James R. Wilson, 1996. "Integrated Variance Reduction Strategies for Simulation," Operations Research, INFORMS, vol. 44(2), pages 327-346, April.
    5. de O. Porta Nova, Acacio M. & Wilson, James R., 1993. "Selecting control variates to estimate multiresponse simulation metamodels," European Journal of Operational Research, Elsevier, vol. 71(1), pages 80-94, November.
    6. Noorani, Idin & Mehrdoust, Farshid & Nasroallah, Abdelaziz, 2021. "A generalized antithetic variates Monte-Carlo simulation method for pricing of Asian option in a Markov regime-switching model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 1-15.
    7. Jangho Park & Rebecca Stockbridge & Güzin Bayraksan, 2021. "Variance reduction for sequential sampling in stochastic programming," Annals of Operations Research, Springer, vol. 300(1), pages 171-204, May.
    8. Athanassios N. Avramidis & James R. Wilson, 1998. "Correlation-Induction Techniques for Estimating Quantiles in Simulation Experiments," Operations Research, INFORMS, vol. 46(4), pages 574-591, August.
    9. Kawai Reiichiro, 2025. "Antithetic variates revisited again," Monte Carlo Methods and Applications, De Gruyter, vol. 31(4), pages 311-328.
    10. Reuven Y. Rubinstein & Gennady Samorodnitsky & Moshe Shaked, 1985. "Antithetic Variates, Multivariate Dependence and Simulation of Stochastic Systems," Management Science, INFORMS, vol. 31(1), pages 66-77, January.
    11. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    12. Bill Mitchell, 1973. "Variance Reduction by Antithetic Variates in GI / G /1 Queuing Simulations," Operations Research, INFORMS, vol. 21(4), pages 988-997, August.
    13. Nelson, Barry L., 1988. "Antithetic-variate splitting for steady-sate simulations," European Journal of Operational Research, Elsevier, vol. 36(3), pages 360-370, September.
    14. J. M. Burt & D. P. Gaver & M. Perlas, 1970. "Simple stochastic networks: Some problems and procedures," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 17(4), pages 439-459, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. E Saliby & R J Paul, 2009. "A farewell to the use of antithetic variates in Monte Carlo simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(7), pages 1026-1035, July.
    2. Shane G. Henderson & Peter W. Glynn, 2001. "Computing Densities for Markov Chains via Simulation," Mathematics of Operations Research, INFORMS, vol. 26(2), pages 375-400, May.
    3. T. Glenn Bailey & Paul A. Jensen & David P. Morton, 1999. "Response surface analysis of two‐stage stochastic linear programming with recourse," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(7), pages 753-776, October.
    4. Pierre L’Ecuyer & Florian Puchhammer & Amal Ben Abdellah, 2022. "Monte Carlo and Quasi–Monte Carlo Density Estimation via Conditioning," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1729-1748, May.
    5. Jong Jun Park & Geon Ho Choe, 2016. "A new variance reduction method for option pricing based on sampling the vertices of a simplex," Quantitative Finance, Taylor & Francis Journals, vol. 16(8), pages 1165-1173, August.
    6. Shing Chih Tsai & Chen Hao Kuo, 2012. "Screening and selection procedures with control variates and correlation induction techniques," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(5), pages 340-361, August.
    7. Chih, Mingchang, 2023. "Stochastic stability analysis of particle swarm optimization with pseudo random number assignment strategy," European Journal of Operational Research, Elsevier, vol. 305(2), pages 562-593.
    8. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    9. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    10. Peter A. Abken & Milind M. Shrikhande, 1997. "The role of currency derivatives in internationally diversified portfolios," Economic Review, Federal Reserve Bank of Atlanta, vol. 82(Q 3), pages 34-59.
    11. Leonard J. Mirman & Egas M. Salgueiro & Marc Santugini, 2013. "Integrating Real and Financial Decisions of the Firm," Cahiers de recherche 1333, CIRPEE.
    12. Dominique Guégan & Wayne Tarrant, 2012. "On the necessity of five risk measures," Annals of Finance, Springer, vol. 8(4), pages 533-552, November.
    13. Raffestin, Louis, 2014. "Diversification and systemic risk," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 85-106.
    14. Gruber, Lutz F. & West, Mike, 2017. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 3-22.
    15. Weidong Lin & Jose Olmo & Abderrahim Taamouti, 2025. "Portfolio Selection under Systemic Risk," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 57(4), pages 905-949, June.
    16. Gupta, Pankaj & Mittal, Garima & Mehlawat, Mukesh Kumar, 2013. "Expected value multiobjective portfolio rebalancing model with fuzzy parameters," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 190-203.
    17. Hany Shawky & Ronald Forbes & Alan Frankle, 1983. "Liquidity Services and Capital Market Equilibrium: The Case for Money Market Mutual Funds," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 6(2), pages 141-152, June.
    18. Colin Atkinson & Emmeline Storey, 2010. "Building an Optimal Portfolio in Discrete Time in the Presence of Transaction Costs," Applied Mathematical Finance, Taylor & Francis Journals, vol. 17(4), pages 323-357.
    19. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2018. "Asset allocation strategies based on penalized quantile regression," Computational Management Science, Springer, vol. 15(1), pages 1-32, January.
    20. Markowitz, Harry, 2014. "Mean–variance approximations to expected utility," European Journal of Operational Research, Elsevier, vol. 234(2), pages 346-355.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:328:y:2026:i:1:p:162-173. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.