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An improved least squares Monte Carlo valuation method based on heteroscedasticity

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  • Fabozzi, Frank J.
  • Paletta, Tommaso
  • Tunaru, Radu

Abstract

Longstaff–Schwartz’s least squares Monte Carlo method is one of the most applied numerical methods for pricing American-style derivatives. We examine the algorithms regression step, demonstrating that the OLS regression is not the best linear unbiased estimator because of heteroscedasticity. We prove the existence of heteroscedasticity for single-asset and multi-asset payoffs numerically and theoretically, and propose weighted-least squares MC valuation method to correct for it. An extensive numerical study shows that the proposed method produces significantly smaller pricing bias than the Longstaff–Schwartz method under several well-known price dynamics. An empirical pricing exercise using market data confirms the advantages of the improved method.

Suggested Citation

  • Fabozzi, Frank J. & Paletta, Tommaso & Tunaru, Radu, 2017. "An improved least squares Monte Carlo valuation method based on heteroscedasticity," European Journal of Operational Research, Elsevier, vol. 263(2), pages 698-706.
    • Handle: RePEc:eee:ejores:v:263:y:2017:i:2:p:698-706
    DOI: 10.1016/j.ejor.2017.05.048
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    15. Fabozzi, Frank J. & Paletta, Tommaso & Stanescu, Silvia & Tunaru, Radu, 2016. "An improved method for pricing and hedging long dated American options," European Journal of Operational Research, Elsevier, vol. 254(2), pages 656-666.
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    3. Maier, Sebastian & Pflug, Georg C. & Polak, John W., 2020. "Valuing portfolios of interdependent real options under exogenous and endogenous uncertainties," European Journal of Operational Research, Elsevier, vol. 285(1), pages 133-147.
    4. Wei, Wei & Zhu, Dan, 2022. "Generic improvements to least squares monte carlo methods with applications to optimal stopping problems," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1132-1144.
    5. Hanbyeol Jang & Sangkwon Kim & Junhee Han & Seongjin Lee & Jungyup Ban & Hyunsoo Han & Chaeyoung Lee & Darae Jeong & Junseok Kim, 2020. "Fast Monte Carlo Simulation for Pricing Equity-Linked Securities," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 865-882, December.
    6. Ben Moews, 2023. "On random number generators and practical market efficiency," Papers 2305.17419, arXiv.org, revised Jul 2023.
    7. Francesco Rotondi, 2019. "American Options on High Dividend Securities: A Numerical Investigation," Risks, MDPI, vol. 7(2), pages 1-20, May.

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