Computational Economics

, Volume 53, Issue 1, pp 343–366 | Cite as

How Strong is the Relationship Among Gold and USD Exchange Rates? Analytics Based on Structural Change Models

  • Manh Cuong Dong
  • Cathy W. S. Chen
  • Sangyoel Lee
  • Songsak Sriboonchitta


This study examines the dynamic relationship among gold and USD exchange rates. Since one single time series model can suffer from structural (or parameter) changes in underlying models, we consider those models with structural breaks. We first employ the cumulative sum of squared residual test to determine the number and locations of change points in the volatility of time series and then divide the whole period by the change points to investigate the relationship between gold and USD exchange rates in each sub-period, based on the time-varying correlations obtained from dynamic conditional correlation models. We show that a negative correlation exists in almost all periods and that the correlation coefficients have higher absolute values during the global financial crisis period than in other periods. Furthermore, the correlation becomes much greater along with downside moves of USD versus upside moves, indicating that a depreciating trend of USD typically has more influence on gold than an appreciating trend. This phenomenon is in line with the leverage effect in financial markets. After comparing the two methods of with/without structural changes, our findings from an empirical study provide evidence that ignoring structural changes can lead to a false conclusion and confirm that our method offers a functional tool to analyze gold prices and USD exchange rates.


Leverage effect CUSUM test Dynamic conditional correlation Multivariate GARCH model Time-varying correlation Structural breaks 

JEL Classification

C13 C32 C58 G15 



The authors thank the editors and referees for their precious time and valuable comments to improve the quality of this paper. Cathy W.S. Chen’s research is funded by the Ministry of Science and Technology, Taiwan (MOST 104-2410-H-035-004 and MOST 105-2118-M-035-003-MY2). Sangyeol Lee’s research is supported by the Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (No. 2015R1A2A2A010003894).


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Manh Cuong Dong
    • 1
  • Cathy W. S. Chen
    • 2
  • Sangyoel Lee
    • 3
  • Songsak Sriboonchitta
    • 4
  1. 1.Department of EconomicsFeng Chia UniversityTaichungTaiwan
  2. 2.Department of StatisticsFeng Chia UniversityTaichungTaiwan
  3. 3.Department of StatisticsSeoul National UniversitySeoulKorea
  4. 4.School of EconomicsChiang Mai UniversityChiang MaiThailand

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