Valuing American Options under Jump Diffusion Model: Forward Monte Carlo Method

• Main Authors: Jhen-Yin Liu, 劉貞吟
• Other Authors: Yung-Hsin Lee
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/cj6gy4

碩士 === 國立臺灣科技大學 === 財務金融研究所 === 102 === This study proposes an adjusted Forward Monte Carlo method for the pricing of American options. The main advantage of Forward Monte Carlo method is that it can determine whether the American option should be exercised or not when a stock price is simulated. It does not use backward induction as required by other methods, for example Binomial Tree and LSM. The difference between adjusted Forward Monte Carlo method and Forward Monte Carlo method is in the calculation of optimal exercise boundary. In the adjusted version proposed in this study, the boundary values at a chosen set of time points are calculated using the Quadratic Approximation, and the whole boundary is approximated by exponential interpolation. This method is developed for two versions of commonly used jump-diffusion models, the Merton's model and Kou's model, where jump size follows normal and double exponential distribution respectively. Our numerical results show that the adjusted Forward Monte-Carlo method provides more accuracy than the least square Monte-Carlo method under the two jump-diffusion models and more efficiency under Merton's model.