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Randomized quasi-Monte Carlo methods in pricing securities

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  • Okten, Giray
  • Eastman, Warren

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  • Okten, Giray & Eastman, Warren, 2004. "Randomized quasi-Monte Carlo methods in pricing securities," Journal of Economic Dynamics and Control, Elsevier, vol. 28(12), pages 2399-2426, December.
    • Handle: RePEc:eee:dyncon:v:28:y:2004:i:12:p:2399-2426
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    References listed on IDEAS

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    1. Boyle, Phelim & Broadie, Mark & Glasserman, Paul, 1997. "Monte Carlo methods for security pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1267-1321, June.
    2. Tuffin Bruno, 1996. "On the use of low discrepancy sequences in Monte Carlo methods," Monte Carlo Methods and Applications, De Gruyter, vol. 2(4), pages 295-320, December.
    3. den Haan, Wouter J & Marcet, Albert, 1990. "Solving the Stochastic Growth Model by Parameterizing Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 31-34, January.
    4. S. Ninomiya & S. Tezuka, 1996. "Toward real-time pricing of complex financial derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 3(1), pages 1-20.
    5. Jenny X. Li & Peter Winker, 2003. "Time Series Simulation with Quasi Monte Carlo Methods," Computational Economics, Springer;Society for Computational Economics, vol. 21(1_2), pages 23-43, February.
    6. Franz, Wolfgang & Göggelmann, Klaus & Schellhorn, Martin & Winker, Peter, 1998. "Quasi-Monte Carlo Methods in Stochastic Simulations: An Application to Fiscal Policy Simulations using an Aggregate Disequilibrium Model of the West German Economy," ZEW Discussion Papers 98-03, ZEW - Leibniz Centre for European Economic Research.
    7. Tan, Ken Seng & Boyle, Phelim P., 2000. "Applications of randomized low discrepancy sequences to the valuation of complex securities," Journal of Economic Dynamics and Control, Elsevier, vol. 24(11-12), pages 1747-1782, October.
    8. Corwin Joy & Phelim P. Boyle & Ken Seng Tan, 1996. "Quasi-Monte Carlo Methods in Numerical Finance," Management Science, INFORMS, vol. 42(6), pages 926-938, June.
    9. Spassimir H. Paskov & Joseph F. Traub, 1995. "Faster Valuation of Financial Derivatives," Working Papers 95-03-034, Santa Fe Institute.
    10. Pierre L’Ecuyer & Christiane Lemieux, 2002. "Recent Advances in Randomized Quasi-Monte Carlo Methods," International Series in Operations Research & Management Science, in: Moshe Dror & Pierre L’Ecuyer & Ferenc Szidarovszky (ed.), Modeling Uncertainty, chapter 0, pages 419-474, Springer.
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    Citations

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    Cited by:

    1. Vladimir K. Kaishev & Dimitrina S. Dimitrova, 2009. "Dirichlet Bridge Sampling for the Variance Gamma Process: Pricing Path-Dependent Options," Management Science, INFORMS, vol. 55(3), pages 483-496, March.
    2. Zhang, Ling & Lai, Yongzeng & Zhang, Shuhua & Li, Lin, 2019. "Efficient control variate methods with applications to exotic options pricing under subordinated Brownian motion models," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 602-621.
    3. Joshi, Mark & Yang, Chao, 2011. "Fast delta computations in the swap-rate market model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(5), pages 764-775, May.
    4. Nguyen Nguyet & Xu Linlin & Ökten Giray, 2018. "A quasi-Monte Carlo implementation of the ziggurat method," Monte Carlo Methods and Applications, De Gruyter, vol. 24(2), pages 93-99, June.
    5. Jacob Lundgren & Yuri Shpolyanskiy, 2017. "Approaches to Asian Option Pricing with Discrete Dividends," Papers 1702.00994, arXiv.org, revised Mar 2021.
    6. Ömür Ugur, 2008. "An Introduction to Computational Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number p556, February.
    7. Yu-Ying Tzeng & Paul M. Beaumont & Giray Ökten, 2018. "Time Series Simulation with Randomized Quasi-Monte Carlo Methods: An Application to Value at Risk and Expected Shortfall," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 55-77, June.
    8. Maria Giuseppina Bruno & Antonio Grande, "undated". "Pricing arithmetic average options and basket options using Monte Carlo and Quasi-Monte methods," Working Papers 143/15, Sapienza University of Rome, Metodi e Modelli per l'Economia, il Territorio e la Finanza MEMOTEF.
    9. Yiran Chen & Giray Ökten, 2022. "A goodness-of-fit test for copulas based on the collision test," Statistical Papers, Springer, vol. 63(5), pages 1369-1385, October.
    10. Beveridge, Christopher & Joshi, Mark & Tang, Robert, 2013. "Practical policy iteration: Generic methods for obtaining rapid and tight bounds for Bermudan exotic derivatives using Monte Carlo simulation," Journal of Economic Dynamics and Control, Elsevier, vol. 37(7), pages 1342-1361.
    11. Linlin Xu & Giray Ökten, 2015. "High-performance financial simulation using randomized quasi-Monte Carlo methods," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1425-1436, August.
    12. Polala Arun Kumar & Ökten Giray, 2020. "Implementing de-biased estimators using mixed sequences," Monte Carlo Methods and Applications, De Gruyter, vol. 26(4), pages 293-301, December.
    13. Liu, Yaning & Yousuff Hussaini, M. & Ökten, Giray, 2016. "Accurate construction of high dimensional model representation with applications to uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 281-295.

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