<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this paper, we make use of a Bayesian (supervised learning) approach in pricing American options via Monte Carlo simulations. We first present Gaussian process regression (Kriging) approach for American options pricing and compare its performance in estimating the continuation value with the Longstaff and Schwartz algorithm. Secondly, we explore the control variates technique in combination with Kriging to further improve the estimation of the continuation value. This method allows to reduce dramatically the standard errors and to improve the stability of the Kriging approach. For illustrative purposes, we use American put options on a stock whose dynamics is given by Heston model, and use European options on the same stock as control variates.</p></abstract>

In this paper, we make use of a Bayesian (supervised learning) approach in pricing American options via Monte Carlo simulations. We first present Gaussian process regression (Kriging) approach for American options pricing and compare its performance in estimating the continuation value with the Longstaff and Schwartz algorithm. Secondly, we explore the control variates technique in combination with Kriging to further improve the estimation of the continuation value. This method allows to reduce dramatically the standard errors and to improve the stability of the Kriging approach. For illustrative purposes, we use American put options on a stock whose dynamics is given by Heston model, and use European options on the same stock as control variates.

Supervised Machine Learning with Control Variates for American Option Pricing

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Supervised Machine Learning with Control Variates for American Option Pricing"}

In this paper, we make use of a Bayesian (supervised learning) approach in pricing American options via Monte Carlo simulations. We first present Gaussian...

Supervised Machine Learning with Control Variates for American Option Pricing

Online veröffentlicht: 27. Okt. 2018

Seitenbereich: 207 - 217

Eingereicht: 02. Feb. 2018

Akzeptiert: 05. Sept. 2018

DOI: https://doi.org/10.1515/fcds-2018-0011

Schlüsselwörter
American options, Monte Carlo, Gaussian processes, Kriging, LSM, supervised learning, Heston Model, control variates

© 2018 Gang Mu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Supervised Machine Learning with Control Variates for American Option Pricing

Online veröffentlicht: 27. Okt. 2018

Seitenbereich: 207 - 217

Eingereicht: 02. Feb. 2018

Akzeptiert: 05. Sept. 2018

DOI: https://doi.org/10.1515/fcds-2018-0011

SchlüsselwörterAmerican options, Monte Carlo, Gaussian processes, Kriging, LSM, supervised learning, Heston Model, control variates

© 2018 Gang Mu, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
American options, Monte Carlo, Gaussian processes, Kriging, LSM, supervised learning, Heston Model, control variates