Abstract
Since China's economic reform in 1978, the country's economic system has foreseen rapid evolvements, particularly in issuing debt securities by the Chinese central government to financing its drastic economic growth (Loo & Lqbal, 2019). This research study examines the zero-coupon bond yield curve's predictive power on China's GDP growth rate by adopting the Nelson-Siegel (1987) dynamic yield curve model. This research study adopts various approaches to refining the Nelson-Siegel (1987) model to enhance its predictive power on future economic activities, drawing upon the modifications undertaken by Diebold & Li's (2006) study to examining the constructed yield curve in accordance with the three latent factors of level, slope and curvature of the entire yield curve. A 67 period of zero-coupon bond yields is gathered between Q3 2002 and Q1 2019 quarterly from zero-coupon bond maturities of 1, 3, 5, 10, 20, and 30 years, finding that all types of zero-coupon bond maturities to exhibit similar yield curve movements across short, intermediary and long term durations. A multiple regression model was used to examine the correlation coefficient between the three latent factors and China's GDP, finding a significant relationship in the slope factor. A relationship was also found between the level and slope factors with a significance of 0.854, whereby the average rates between the two variables were calculated under the augmented Dicky Fuller test to ensure all factors are at stationary states to enhance the accuracy of future testing. The researcher also performed a least-squares equation (OLS) test to addressing the identified multicollinearity problem aforementioned, finding the R-squared value of 29.6%. Which suggested the level and curvature factors of the constructed yield curve would accurately explain 29.6% of China's GDP growth rates. To further examine the predictive power of the constructed yield curve in accordance to Nelson-Siegel's (1987) dynamic model, an out-of-sample forecasting method is employed with the out-of-sample size of 40 periods between Q3 2002 and Q2 2012 against 27 periods between Q3 and Q1 2019. The out-of-sample regression test founded an R squared value of 11.4,% suggesting that in sample forecasts contained higher predictive power to China's GDP growth based on the constructed yield curve. Furthermore, the out-of-sample forecast results show no significant relationship between the level and curvature factors, further reaffirming the argument that the yield curve in sample forecasts would better predict future economic activities. The research findings were consistent with findings from other studies conducted by Diebold & Li (2006); Hvozdenska (2015), and Campbell & Thompson (2008), whereby the spread of the yield curve constructed by the Nelson-Siegel (1987) dynamic model showed a strong relationship between China's GDP growth and the produced yield curve, representing strong predictive power and offers valuable insights to addressing the identified research gap where minimal research studies have explored the predictive power of China's zero-coupon bond yields in relation to the macroeconomic outlook. Keywords: Nelson-Siegel model, Yield curve, Zero-coupon bond, Maturity, Regression, Dynamic model. DOI: 10.7176/JEP/11-24-02 Publication date: December 31 st 2020
Highlights
The Chinese economy has experienced substantial growth since the country's economic reform in 1978, opening up the traditionally state-owned economic environment with the introduction of a series of new economic policies to encourage and stimulate foreign direct investments and exports from domestic firms (Li, 1998)
A total of 67 yield curves are used to predicting real economic output growth (GDP) using the dynamic Nelson-Siegel (1987) yield curve model, and three independent variables are extracted for later analysis
Major research studies such as Diebold & Li (2006) expanded upon Nelson-Siegel's (1987) dynamic yield curve model to enhance its predictive power with modification
Summary
The Chinese economy has experienced substantial growth since the country's economic reform in 1978, opening up the traditionally state-owned economic environment with the introduction of a series of new economic policies to encourage and stimulate foreign direct investments and exports from domestic firms (Li, 1998). External demand has experienced steady declines as domestic overcapacity has continued to draw significant academic attention, voicing the concerns over the slowing growth of the Chinese economy in recent years in comparison to the early phases of the economic reform era (Lardy, 2019) This current state of affairs is widely regarded as the 'new normal' for the Chinese economy, concerning domestic demand and consumption in the rapidly evolving macroeconomic environment of China (Jiang et al, 2017). As the second largest economy worldwide, the key determinant factors to China's GDP growth are complicated by a wide range of new and changing macroeconomic and financial variables. China's increasing integration with expanding inward and outward economic activities have further driven international trade, becoming a member of the World Trade Organization in 2001 (Rumbaugh & Blancher, 2004)
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