Time Series Forecasting of Hong Kong Inter-bank Offered Rate (HIBOR) using Exponential Smoothing State Space Model

Abstract

This paper set out to analyze and forecast the Hong Kong Interbank Interest Rate (HIBOR) for a period 2006 to 2018. The main objective of this study is to propose an appropriate time series forecasting model for HIBOR. HIBOR conceptually captures the interaction between demand and supply of Hong Kong dollar in the interbank market. The volatility of HIBOR reflects market sentiment, changes in underlying macroeconomic environment, random events and even political climate. Thus, the time series data of HIBOR appears to have multiple seasonality during the aforesaid period. The TBATS model, the state space modeling framework developed by De Livera, Hyndman and Snyder (2010) is adopted for this study to improve the accuracy and efficiency of the time series modeling and forecasting of HIBOR. The TBATS model incorporates Box-Cox transformations, Fourier representations with time varying coefficients, and ARMA error correction. Likelihood evaluation and analytical expressions for point forecasts and interval predictions under the assumption of Gaussian errors are derived, leading to a simple, comprehensive approach to forecasting complex seasonal time series. In addition, the trigonometric formulation is used as a means of decomposing complex seasonal time series, which helps to identify and extract seasonal components which are otherwise not apparent in the time series plot itself. The performance of the TBATS model as evaluated by measures of forecast error are presented.