Monte Carlo pathwise sensitivities for barrier options
For practitioners in quantitative finance, this method extends pathwise sensitivity estimation to discontinuous payoffs, addressing a known limitation.
This work presents a new Monte Carlo algorithm for computing pathwise sensitivities for discontinuous payoff functions, enabling calibration of CoCo-Bonds modeled with barrier options.
The Monte Carlo pathwise sensitivities approach is well established for smooth payoff functions. In this work, we present a new Monte Carlo algorithm that is able to calculate the pathwise sensitivities for discontinuous payoff functions. Our main tool is to combine the one-step survival idea of Glasserman and Staum with the stable differentiation approach of Alm, Harrach, Harrach and Keller. As an application we use the derived results for a two-dimensional calibration of a CoCo-Bond, which we model with different types of discretely monitored barrier options.