IDEAS home Printed from https://ideas.repec.org/p/emu/wpaper/15-05.pdf.html
   My bibliography  Save this paper

Do Precious Metal Prices Help in Forecasting South African Inflation?

Author

Listed:
  • Mehmet Balcilar

    () (Department of Economics, Eastern Mediterranean University)

  • NICO KATZKE

    () (Department of Economics, UNIVERSITY OF STELLENBOSCH)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

Abstract

This study investigates the predictability of 11 industrialized stock returns with emphasis on the role of U.S. returns. Using monthly data spanning 1980:2 to 2014:12, we show that there exist multiple structural breaks and nonlinearities in the data. Therefore, we employ methods that are capable of accounting for these and at the same time date stamping the periods of causal relationship between the U.S. returns and those of the other countries. First we implement a subsample analysis which relies on the set of models, data set and sample range as in Rapach et al. (2013). Our results show that while the U.S. returns played a strong predictive role based on the OLS pairwise Granger causality predictive regression and news-diffusion models, it played no role based on the pooled version of the OLS model and its role based on the adaptive elastic net model is weak relative to Switzerland. Second, we implement our preferred model: a bootstrap rolling window approach using our newly updated data on stock returns for each countries, and find that U.S. stock return has significant predictive ability for all the countries at certain sub-periods. Given these results, it would be misleading to rely on results based on constant-parameter linear models that assume that the relationship between the U.S. returns and those of other industrialized countries are permanent, since the relationship is, in fact, time-varying, and holds only at specific periods.

Suggested Citation

  • Mehmet Balcilar & NICO KATZKE & Rangan Gupta, 2015. "Do Precious Metal Prices Help in Forecasting South African Inflation?," Working Papers 15-05, Eastern Mediterranean University, Department of Economics.
  • Handle: RePEc:emu:wpaper:15-05.pdf
    as

    Download full text from publisher

    File URL: http://repec.economics.emu.edu.tr/RePEc/emu/wpaper/15-05.pdf
    File Function: First version, 2015
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ben S. Bernanke & Mark Gertler, 2001. "Should Central Banks Respond to Movements in Asset Prices?," American Economic Review, American Economic Association, vol. 91(2), pages 253-257, May.
    2. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    3. Charles Bean, 2004. "Inflation Targeting: The UK Experience," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 5(4), pages 405-421, November.
    4. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
    5. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    6. Gelos, Gaston & Ustyugova, Yulia, 2017. "Inflation responses to commodity price shocks – How and why do countries differ?," Journal of International Money and Finance, Elsevier, vol. 72(C), pages 28-47.
    7. Chen, Yu-chin & Turnovsky, Stephen J. & Zivot, Eric, 2014. "Forecasting inflation using commodity price aggregates," Journal of Econometrics, Elsevier, vol. 183(1), pages 117-134.
    8. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
    11. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    12. Ben S. Bernanke & Mark Gertler, 1999. "Monetary policy and asset price volatility," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 77-128.
    13. Stephen G. Cecchetti & Hans Genberg & Sushil Wadhwani, 2002. "Asset Prices in a Flexible Inflation Targeting Framework," NBER Working Papers 8970, National Bureau of Economic Research, Inc.
    14. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 326-336, September.
    15. Hooker, Mark A, 2002. "Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications versus Changes in Regime," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 540-561, May.
    16. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    17. James Mitchell & Stephen G. Hall, 2005. "Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR 'Fan' Charts of Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 995-1033, December.
    18. Dick Dijk & Philip Hans Franses, 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 727-744, December.
    19. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    20. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    21. Fred Furlong & Robert Ingenito, 1996. "Commodity prices and inflation," Economic Review, Federal Reserve Bank of San Francisco, pages 27-47.
    22. 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.
    23. Francesco Ravazzolo & Marco J. Lombardi, 2012. "Oil price density forecasts: Exploring the linkages with stock markets," Working Papers No 3/2012, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    24. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    25. Fuhrer, Jeff & Moore, George, 1992. "Monetary policy rules and the indicator properties of asset prices," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 303-336, April.
    26. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    27. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    28. Dr. James Mitchell, 2005. "Evaluating, comparing and combining density forecasts using the KLIC with an application to the Bank of England and NIESR ÔfanÕ charts of inflation," National Institute of Economic and Social Research (NIESR) Discussion Papers 253, National Institute of Economic and Social Research.
    29. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-618, Nov.-Dec..
    30. Nikolay Gospodinov & Serena Ng, 2013. "Commodity Prices, Convenience Yields, and Inflation," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 206-219, March.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Stock returns; predictability; structural breaks; nonlinearity; time varying causality;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:emu:wpaper:15-05.pdf. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mehmet Balcilar). General contact details of provider: http://edirc.repec.org/data/deemuty.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.