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The Early Exercise Premium In American Options By Using Nonparametric Regressions

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  • WEIPING LI

    (Institute of Finance and Big Data, Southwest Jiaotong University, Chengdu, Sichuan Province 611756, P. R. China2Department of Finance, Oklahoma State University, Stillwater, OK 74078-0613, USA)

  • SU CHEN

    (Department of Mathematical Sciences, The University of Memphis, Memphis, TN 38152, USA)

Abstract

The early exercise premium and the price of an American put option are evaluated by using nonparametric regression on the time to expiration, the moneyness and the volatility of underlying assets. In terms of mean square error (MSE), our nonparametric methods of American put option pricings outperform the existing classical methods for both in-the-sample (1 September 2011–31 January 2012) and out-of-sample (1 September 2012–28 February 2013) testings on the S&P 100 Index (OEX). Our methods have better predictions and more accurate approximations. The Greek letters for both the early exercise premium and the American put option are computed numerically.

Suggested Citation

  • Weiping Li & Su Chen, 2018. "The Early Exercise Premium In American Options By Using Nonparametric Regressions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(07), pages 1-29, November.
  • Handle: RePEc:wsi:ijtafx:v:21:y:2018:i:07:n:s0219024918500395
    DOI: 10.1142/S0219024918500395
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    Cited by:

    1. Shuaiqiang Liu & 'Alvaro Leitao & Anastasia Borovykh & Cornelis W. Oosterlee, 2020. "On Calibration Neural Networks for extracting implied information from American options," Papers 2001.11786, arXiv.org.

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