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Article
Affiliation(s)

Hong Kong Polytechnic University, Hong Kong, China
Hong Kong Nang Yan College of Higher Education, Hong Kong, China

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.

KEYWORDS

HIBOR, forecast, multiple seasonal

Cite this paper

Economics World, Jan.-Mar. 2023, Vol.10, No.1, 43-48 doi: 10.17265/2328-7144/2023.01.005

References

Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26(2), pp. 211-252.

Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco: Holden Day.

Bratu, M. (2012). A comparison of two quantitative forecasting methods for macroeconomic. Journal of Management Change, 29(1), 104-124.

Brockwell, P. J., & Davis, R. A. (1996). Introduction to Time Series and Fore-casting. New York: Springer.

Gardner, J. E. S., & McKenzie, E. (1985). Forecasting trends in time series. Management Science, 31(10), 1237-1246.

Harvey, A. (1989). Forecasting Structural Time Series Models and the Kalman Filter. Cambridge Books: Cambridge University Press.

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Snyder, R. (2006). Discussion. International Journal of Forecasting, 22(4), 673-676.

Whittle, P. (1951). Hypothesis Testing in Time Series Analysis. Uppsala: Almquist and sWicksell.

West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models. 2nd ed. New York: Springer-Verlag.

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