Journal of Computational Finance

Risk.net

Portfolio optimization for American options

Yaxiong Zeng and Diego Klabjan

  • The first work that considers exercise timings of an American option portfolio.
  • A non-standard progressive hedging algorithm combined with Q-learning.
  • An empirical study that discusses best and worst investment scenarios.

American options allow early exercise, which yields an additional challenge – besides the weights of each option – when optimizing a portfolio of American options. In this work, we construct strategies for an American option portfolio by exercising options at optimal timings with optimal weights determined concurrently. To model such portfolios, a reinforcement learning (Q-learning) algorithm is proposed, combining an iterative progressive hedging method and a quadratic approximation to Q-values by regression. By means of Monte Carlo simulation and empirical experiments, using data from the SPY options market, we evaluate the quality of our algorithms and examine their performance under various investment assumptions, such as different portfolio settings and distributions of the underlying asset returns. With discretized timings, our strategies work better in a relatively long time horizon and when the portfolio is hedged using the underlying instrument. Due to the highly leveraged and risky nature of our strategies, overly risk-averse investors are proved unsuitable for such investment opportunities.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to Risk.net? View our subscription options

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here