The valuation of self-funding instalment warrants

Journal article

We present two models for the fair value of a self-funding instalment warrant. In both models we assume the underlying stock process follows a geometric Brownian motion. In the first model, we assume that the underlying stock pays a continuous dividend yield and in the second we assume that it pays a series of discrete dividend yields. We show that both models admit similarity reductions and use these to obtain simple finite-difference and Monte Carlo solutions. We use the method of multiple scales to connect these two models and establish the first-order correction term to be applied to the first model in order to obtain the second, thereby establishing that the former model is justified when many dividends are paid during the life of the warrant. Further, we show that the functional form of this correction may be expressed in terms of the hedging parameters for the first model and is, from this point of view, independent of the particular payoff in the first model. In two appendices we present approximate solutions for the first model which are valid in the small volatility and the short time-To-expiry limits, respectively, by using singular perturbation techniques. The small volatility solutions are used to check our finite-difference solutions and the small time-To-expiry solutions are used as a means of systematically smoothing the payoffs so we may use pathwise sensitivities for our Monte Carlo methods.

Authors: Dewynne, J; El-Hassan, N

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: World Scientific Publishing
• Journal: International Journal of Theoretical and Applied Finance
• Volume: 20
• Issue: 4
• Article number: 1750025
• Publication date: 2017-05-02
• Acceptance date: 2017-03-29
• DOI: 10.1142/S021902491750025X
• EISSN: 1793-6322
• ISSN: 0219-0249
• Keywords: finite-difference methods; Asian options; Monte Carlo simulation; Black–Scholes’ partial differential equation; self-funding instalment warrants