Forecasting the Indonesian Government Securities Yield Curve Using Neural Networks and Vector Autoregressive Model
Dedi Rosadi, Yoga Aji Nugraha
Department of Mathematics, Gadjah Mada University, Yogyakarta, DIY, Indonesia; Department of Mathematics, Gadjah Mada University, Yogyakarta, DIY, Indonesia

In this paper, we discuss the problem of forecasting the yield curve of Indonesian Government bond using neural networks and Vector Autoregressive Model. We first model the yield curve using the Nelson-Siegel-Svensson (NSS) model and estimate its parameters using sequential quadratic programming method. Then, we forecast various parameters of the Nelson-Siegel yield curve using neural networks and Vector Autoregression (VAR). The forecasted NSS parameters are then used to calculate the yield curve of the various Indonesian government bonds. For empirical example, we implement the methods using data of Indonesian Government Bond.

Keywords: Yield curve; Nelson-Siegel-Svensson model; Neural networks; Vector autoregression

Biography: Dr. Dedi Rosadi graduated from Vienna Univ. of Technology, Austria. His current research interest includes Time Series analysis and Statistical Computing with application in Finance. See http://dedirosadi.staff.ugm.ac.id for further detail.