The basic assumption that is often used in bond valuations is the assumption on the Black-Scholes model. The practical assumption of the Black-Scholes model is the return of assets with normal distribution, but in reality there are many conditions where the return of assets of a company is not normally distributed and causing improperly developed bond valuation modeling. The Fast-Fourier Transform model (FFT) was developed as a solution to this problem. The Fast-Fourier Transformation Model is a Fourier transformation technique with high accuracy and is more effective because it uses characteristic functions. In this research, a modeling will be carried out to calculate bond valuations designed to take advantage of the computational power of the FFT. The characteristic function used is the Variance Gamma, which has the advantage of being able to capture data return behavior that is not normally distributed. The data used in this study are Sustainable Bonds I of Bank Danamon Phase I Year 2019 Series B, Sustainable Bonds II of Bank CIMB Niaga II Phase IV Year 2018 Series C, Sustainable Subordinated Bonds II of Bank UOB Indonesia Phase II 2019. The results obtained are FFT model using the Variance Gamma characteristic function gives more precise results for the return of assets with not normal distribution. Keywords: Bonds, Bond Valuation, Black-Scholes, Fast-Fourier Transform, Variance Gamma
Robustness of Delta Hedging for Path-Dependent Options in Local Volatility Models
