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The Dynamic Linkages among Gold Prices, Stock Prices, the Exchange Rate and Interest Rate in South Africa

Author

Listed:
  • Thabang NDLOVU
  • Nozibusiso Mavuso NDLOVU

    (Competition Commission South Africa
    Competition Commission South Africa)

Abstract

The fundamental aim of this study is to examine the intricate interplay among gold prices, interest rates, exchange rates, and stock price indices within the context of South Africa. To achieve this, both a conventional Vector Autoregression Model and a Bayesian Vector Autoregression Model were applied to monthly data spanning from June 1995 to December 2022. The findings indicate that a positive shock in stock prices triggers positive reactions in exchange rates, gold prices, and interest rates. Conversely, a positive shock in interest rates induces negative reactions in both gold prices and stock prices. Moreover, a positive shock in gold prices elicits negative responses in both interest rates and stock prices. Additionally, a positive shock in exchange rates prompts positive reactions in gold prices and interest rates, while simultaneously resulting in a negative response in stock prices.

Suggested Citation

  • Thabang NDLOVU & Nozibusiso Mavuso NDLOVU, 2024. "The Dynamic Linkages among Gold Prices, Stock Prices, the Exchange Rate and Interest Rate in South Africa," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 8(1), pages 35-56.
    • Handle: RePEc:trp:01jefa:jefa0070
    DOI: 10.1991/jefa.v8i1.a65
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    References listed on IDEAS

    as
    1. Jain, Anshul & Biswal, P.C., 2016. "Dynamic linkages among oil price, gold price, exchange rate, and stock market in India," Resources Policy, Elsevier, vol. 49(C), pages 179-185.
    2. Gary M. Koop, 2013. "Forecasting with Medium and Large Bayesian VARS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 177-203, March.
    3. Mehmet Balcilar & Zeynel Abidin Ozdemir & Muhammad Shahbaz & Serkan Gunes, 2018. "Does inflation cause gold market price changes? evidence on the G7 countries from the tests of nonparametric quantile causality in mean and variance," Applied Economics, Taylor & Francis Journals, vol. 50(17), pages 1891-1909, April.
    4. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    5. Akbar, Muhammad & Iqbal, Farhan & Noor, Farzana, 2019. "Bayesian analysis of dynamic linkages among gold price, stock prices, exchange rate and interest rate in Pakistan," Resources Policy, Elsevier, vol. 62(C), pages 154-164.
    6. Cepni, Oguzhan & Gupta, Rangan, 2021. "Time-varying impact of monetary policy shocks on US stock returns: The role of investor sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    7. Ansari, Md Gyasuddin & Sensarma, Rudra, 2019. "US monetary policy, oil and gold prices: Which has a greater impact on BRICS stock markets?," Economic Analysis and Policy, Elsevier, vol. 64(C), pages 130-151.
    8. Kuschnig, Nikolas & Vashold, Lukas, 2019. "BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R," Department of Economics Working Paper Series 296, WU Vienna University of Economics and Business.
    9. Kumar, Suresh & Choudhary, Sangita & Singh, Gurcharan & Singhal, Shelly, 2021. "Crude oil, gold, natural gas, exchange rate and indian stock market: Evidence from the asymmetric nonlinear ARDL model," Resources Policy, Elsevier, vol. 73(C).
    10. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    11. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, November.
    12. Shaikh, Imlak & Vallabh, Priyanka, 2022. "Monetary policy uncertainty and gold price in India: Evidence from Reserve Bank of India's Monetary Policy Committee (MPC) review," Resources Policy, Elsevier, vol. 76(C).
    13. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M, 2014. "On the economic determinants of the gold–inflation relation," Resources Policy, Elsevier, vol. 41(C), pages 101-108.
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    More about this item

    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q30 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - General

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