A Quantum Mechanics Approach to Pricing Binary Options: an Application to the S&P 500

Abstract

In this paper we analyze two relevant dimensions of the properties of binary options. First, we provide new insights into the risk-reward profile of these options and an application to portfolio management. Using an algorithm that implements an investment strategy that exploits such profile, we build a portfolio of these options on S\&P 500 stocks that beats the benchmark. Second, we propose a novel pricing method for binary options using a quantum mechanics formalism. We derive a neat and elegant pricing kernel that achieves a degree of accuracy at least as great as those from Black-Scholes or Montecarlo simulations, while overcoming the limitations of the former in coping with complex options and the computational burden of the latter. We further conclude that our pricing method can be extended to other options.

In this paper we analyze two relevant dimensions of the properties of binary options. First, we provide new insights into the risk-reward profile of these options and an application to portfolio management. Using an algorithm that implements an investment strategy that exploits such profile, we build a portfolio of these options on S\&P 500 stocks that beats the benchmark. Second, we propose a novel pricing method for binary options using a quantum mechanics formalism. We derive a neat and elegant pricing kernel that achieves a degree of accuracy at least as great as those from Black-Scholes or Montecarlo simulations, while overcoming the limitations of the former in coping with complex options and the computational burden of the latter. We further conclude that our pricing method can be extended to other options.