Financial derivatives are financial instruments whose payoff is linked to some fundamental financial assets or indices. They are essential tools for speculation and risk-management. This paper focuses on the pricing of a common type of derivatives: convertible bonds (CBs), which incorporate the features of both bonds and stocks. Chambers and Lu propose a popular two-factor tree model for CBs pricing. This paper assesses the efficiency of their model on both GPU (graphics processing unit) and CPU. The GPU code exploits the GPU's inherently parallel architecture and high memory bandwidth. The numerical results show that the GPU code is orders faster than the CPU code. These positive results encourage more use of GPUs on computation-intensive problems in financial engineering such as pricing derivatives by tree-based models studied in this paper.
['Yuh-Dauh Lyuu', 'Kuo-Wei Wen', 'Yi-Chun Wu']
Performance of GPU for Pricing Financial Derivatives: Convertible Bonds