Statistical Prediction for the Pricing of Bond Using Random Number Generation

摘要

In this paper, we propose a dynamic prediction algorithm to predict the interest rate for the bond price using real data set. Our algorithm is based on term structure model of the interest rate, which takes place in financial modelling, such as the standard Wiener process. We use cumulative distribution function (CDF) of real data for the interest rate measurement. The CDF is obtained by the natural cubic spline (CS) method, which is the frequently used numerical methods for interpolation. Most useful when the CDF F(x) has an inverse F~(-1)(x) which is easy to compute. We use the random number generation to calculate the pricing of bond. In empirical computer simulation, we show that the lower values of precision in the proposed prediction algorithm corresponds to sharper estimates. It is very reasonable on prediction.