IDEAS home Printed from https://ideas.repec.org/p/sza/wpaper/wpapers157.html
   My bibliography  Save this paper

Macroeconomic Surprises and Stock Returns in South Africa

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Monique Reid

    (Department of Economics, University of Stellenbosch)

Abstract

The objective of this paper is to explore the sensitivity of industry-specific stock returns to monetary policy and macroeconomic news. The paper looks at a range of industry-specific South African stock market indices and evaluates the sensitivity of these indices to a various unanticipated macroeconomic shocks. We begin with an event study, which examines the immediate impact of macroeconomic shocks on the stock market indices, and then use a Bayesian Vector Autoregressive (BVAR) analysis, which provides insight into the dynamic effects of the shocks on the stock market indices, by allowing us to treat the shocks as exogenous through appropriate setting of priors defining the mean and variance of the parameters in the VAR. The results from the event study indicate that with the exception of the gold mining index, where the CPI surprise plays a significant role, monetary surprise is the only variable that consistently negatively affects the stock returns significantly, both at the aggregate and sectoral levels. The BVAR model based on monthly data, however, indicates that, in addition to the monetary policy surprises, the CPI and PPI surprises also affect aggregate stock returns significantly. However, the effects of the CPI and PPI surprises are quite small in magnitude and are mainly experienced at shorter horizons immediately after the shock.

Suggested Citation

  • Rangan Gupta & Monique Reid, 2012. "Macroeconomic Surprises and Stock Returns in South Africa," Working Papers 05/2012, Stellenbosch University, Department of Economics.
    • Handle: RePEc:sza:wpaper:wpapers157
    as

    Download full text from publisher

    File URL: https://www.ekon.sun.ac.za/wpapers/2012/wp052012/wp-05-2012.pdf
    File Function: First version, 2012
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ben S. Bernanke & Kenneth N. Kuttner, 2005. "What Explains the Stock Market's Reaction to Federal Reserve Policy?," Journal of Finance, American Finance Association, vol. 60(3), pages 1221-1257, June.
    2. Rangan Gupta & Alain Kabundi, 2008. "A Dynamic Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 200815, University of Pretoria, Department of Economics.
    3. 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.
    4. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    5. Refet S. Gürkaynak & Brian Sack & Eric Swanson, 2005. "The Sensitivity of Long-Term Interest Rates to Economic News: Evidence and Implications for Macroeconomic Models," American Economic Review, American Economic Association, vol. 95(1), pages 425-436, March.
    6. 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.
    7. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    8. David Fadiran & Hammed Amusa, 2019. "The J-Curve Phenomenon: Evidence from Commodity Trade Between South Africa and the United States," Working Papers 187, Economic Research Southern Africa.
    9. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    10. 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.
    11. Monique Reid, 2009. "The Sensitivity Of South African Inflation Expectations To Surprises," South African Journal of Economics, Economic Society of South Africa, vol. 77(3), pages 414-429, September.
    12. Bonga-Bonga, Lumengo & Kabundi, Alain, 2015. "Monetary Policy Instrument and Inflation in South Africa: Structural Vector Error Correction Model Approach," MPRA Paper 63731, University Library of Munich, Germany.
    13. 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.
    14. Foluso Akinsola & Sylvanus Ikhide, 2018. "Can bank capital adequacy changes amplify the business cycle in South Africa?," Working Papers 143, Economic Research Southern Africa.
    15. Clive Coetzee, 2002. "Monetary Conditions and Stock Returns: A South African Case Study," Finance 0205002, University Library of Munich, Germany.
    16. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cakan Esin & Rangan Gupta, 2017. "Does the US. macroeconomic news make the South African stock market riskier?," Journal of Developing Areas, Tennessee State University, College of Business, vol. 51(4), pages 17-27, October-D.
    2. Pooja Joshi & A K Giri, 2015. "Dynamic Relations between Macroeconomic Variables and Indian Stock Price: An Application of ARDL Bounds Testing Approach," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 5(10), pages 1119-1133, October.
    3. Beatrice D. Simo - Kengne & Mehmet Balcilar & Rangan Gupta & Monique Reid & Goodness C. Aye, 2012. "Is the relationship between monetary policy and house prices asymmetric in South Africa? Evidence from a Markov-Switching Vector Autoregressive mode," Working Papers 15-26, Eastern Mediterranean University, Department of Economics.
    4. Dewenter, Kathryn L. & Riddick, Leigh A., 2018. "What's the value of a TBTF guaranty? Evidence from the G-SII designation for insurance companies✰," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 70-85.
    5. Nasha Maveé & Mr. Roberto Perrelli & Mr. Axel Schimmelpfennig, 2016. "Surprise, Surprise: What Drives the Rand / U.S. Dollar Exchange Rate Volatility?," IMF Working Papers 2016/205, International Monetary Fund.
    6. Rafiqul Bhuyan & Mohammad Sogir Hossain Khandoker & Mahjuja Taznin & Md. Shanur Rahman & Lamia Akter, 2021. "Determining Stock Return movements of Banking Sector during Global Financial Crisis: An Examination on Emerging Markets of Bangladesh," Bulletin of Applied Economics, Risk Market Journals, vol. 8(2), pages 111-123.
    7. Ligita Gasparėnienė & Rita Remeikienė & Aleksejus Sosidko & Vigita Vėbraitė & Evaldas Raistenskis, 2020. "Modeling of EURO STOXX 50 index price returns based on industrial production surprises: basic and machine learning approach," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 1305-1320, December.
    8. Sonali Das & Rangan Gupta & Patrick Kanda & Monique Reid & Christian Tipoy & Mulatu Zerihun, 2014. "Real interest rate persistence in South Africa: evidence and implications," Economic Change and Restructuring, Springer, vol. 47(1), pages 41-62, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    2. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2016. "Large Bayesian VARMAs," Journal of Econometrics, Elsevier, vol. 192(2), pages 374-390.
    3. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    4. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    5. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    6. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    7. Fady Barsoum, 2013. "The Effects of Monetary Policy Shocks on a Panel of Stock Market Volatilities: A Factor-Augmented Bayesian VAR Approach," Working Paper Series of the Department of Economics, University of Konstanz 2013-15, Department of Economics, University of Konstanz.
    8. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    9. Silvia Miranda-Agrippino & Giovanni Ricco, 2021. "Bayesian local projections," Working Papers hal-03373574, HAL.
    10. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    11. Bekiros Stelios & Paccagnini Alessia, 2015. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 107-136, April.
    12. Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
    13. Демешев Борис Борисович & Малаховская Оксана Анатольевна, 2016. "Макроэкономическое Прогнозирование С Помощью Bvar Литтермана," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(4), pages 691-710.
    14. Mirriam Chitalu Chama-Chiliba & Rangan Gupta & Nonophile Nkambule & Naomi Tlotlego, 2011. "Forecasting Key Macroeconomic Variables of the South African Economy Using Bayesian Variable Selection," Working Papers 201132, University of Pretoria, Department of Economics.
    15. Caruso, Alberto & Reichlin, Lucrezia & Ricco, Giovanni, 2019. "Financial and fiscal interaction in the Euro Area crisis: This time was different," European Economic Review, Elsevier, vol. 119(C), pages 333-355.
    16. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.
    17. Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2014. "Short-term inflation projections: A Bayesian vector autoregressive approach," International Journal of Forecasting, Elsevier, vol. 30(3), pages 635-644.
    18. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    19. 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.
    20. Demeshev, Boris & Malakhovskaya, Oxana, 2016. "BVAR mapping," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 118-141.

    More about this item

    Keywords

    Bayesian Vector Autoregressive Model; Event Study; Macroeconomic Surprises; Stock Returns.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G1 - Financial Economics - - General 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:sza:wpaper:wpapers157. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Melt van Schoor (email available below). General contact details of provider: https://edirc.repec.org/data/desunza.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.