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A note on stochastic polynomial chaos expansions for uncertain volatility and Asian option pricing

Author

Listed:
  • Lin, Y.-T.
  • Shih, Y.-T.
  • Chien, C.-S.
  • Sheng, Q.

Abstract

This paper concerns accurate and efficient polynomial chaos expansions (PCEs) for Asian option pricing with uncertain volatilities. While arbitrary distributions of the volatility parameter are applied for estimating real-world option prices, arbitrary polynomial chaos (aPC) are incorporated for approximating raw data of the historical volatility distributions. Rigorous analysis is carried out to ensure the numerical stability of the compact aPC Crank-Nicolson finite difference method accomplished. Numerical results acquired are compared with solutions via standard Monte Carlo schemes (MCSs) and generalized polynomial chaos (gPC) with different random volatilities. Stock data from Asian financial industry are used. It is evident that the novel schemes derived are highly accurate and efficient for evaluating means and variances of uncertain volatility and stochastic Asian option pricing.

Suggested Citation

  • Lin, Y.-T. & Shih, Y.-T. & Chien, C.-S. & Sheng, Q., 2021. "A note on stochastic polynomial chaos expansions for uncertain volatility and Asian option pricing," Applied Mathematics and Computation, Elsevier, vol. 393(C).
  • Handle: RePEc:eee:apmaco:v:393:y:2021:i:c:s0096300320307177
    DOI: 10.1016/j.amc.2020.125764
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    References listed on IDEAS

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    1. Stein, Elias M & Stein, Jeremy C, 1991. "Stock Price Distributions with Stochastic Volatility: An Analytic Approach," Review of Financial Studies, Society for Financial Studies, vol. 4(4), pages 727-752.
    2. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 8, pages 229-288, World Scientific Publishing Co. Pte. Ltd..
    3. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    4. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    5. Pulch, Roland & van Emmerich, Cathrin, 2009. "Polynomial chaos for simulating random volatilities," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(2), pages 245-255.
    6. Ball, Clifford A. & Roma, Antonio, 1994. "Stochastic Volatility Option Pricing," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 29(4), pages 589-607, December.
    7. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    8. Oladyshkin, S. & Nowak, W., 2012. "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 179-190.
    9. Prempraneerach, P. & Hover, F.S. & Triantafyllou, M.S. & Karniadakis, G.E., 2010. "Uncertainty quantification in simulations of power systems: Multi-element polynomial chaos methods," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 632-646.
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    Cited by:

    1. Dias, Fabio S. & Peters, Gareth W., 2021. "Option pricing with polynomial chaos expansion stochastic bridge interpolators and signed path dependence," Applied Mathematics and Computation, Elsevier, vol. 411(C).

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