Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals, financial stress and economic uncertainty

Rangan Gupta, Shawkat Hammoudeh, Won Joong Kim, Beatrice D. Simo-Kengne

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US-China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.

Original languageEnglish
Pages (from-to)170-189
Number of pages20
JournalNorth American Journal of Economics and Finance
Volume28
DOIs
Publication statusPublished - Apr 2014
Externally publishedYes

Keywords

  • Bayesian
  • Forecasting
  • Foreign reserve
  • Macroeconomic fundamentals
  • State space models

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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