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Long memory and volatility persistence across BRICS stock markets

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  • Tripathy, Naliniprava

Abstract

This study aims to assess the persistence of volatility shocks and long memory in stock market returns and volatility in Brazil, Russia, India, China, and South Africa (BRICS), employing the GARCH, APARCH, ARFIMA, and FIGARCH models from January 2000 to November 2019. The results of GARCH confirm evidence of persistence in volatility shocks, while APARCH indicates the existence of leverage effects in all BRICS stock markets. The results of the ARFIMA and FIGARCH models offer significant indications of long-range dependence in the mean returns and volatility series, posing a challenge to the Efficient Market Hypothesis. The findings have implications for investors, traders, portfolio managers and policymakers who need to understand the varying behavior of stock returns and volatility across BRICS to make valid investment judgments before taking decisions.

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  • Tripathy, Naliniprava, 2022. "Long memory and volatility persistence across BRICS stock markets," Research in International Business and Finance, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:riibaf:v:63:y:2022:i:c:s0275531922001684
    DOI: 10.1016/j.ribaf.2022.101782
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    as
    1. Dimitrios Vougas, 2004. "Analysing long memory and volatility of returns in the Athens stock exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 14(6), pages 457-460.
    2. de Melo Mendes, Beatriz Vaz & Kolev, Nikolai, 2008. "How long memory in volatility affects true dependence structure," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 1070-1086, December.
    3. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-268, July.
    4. Ciprian Necula & Alina-Nicoleta Radu, 2012. "Long Memory in Eastern European Financial Markets Returns," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 25(2), pages 316-377, January.
    5. Sadique, Shibley & Silvapulle, Param, 2001. "Long-Term Memory in Stock Market Returns: International Evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 59-67, January.
    6. Siow-Hooi Tan & Mohammad Tariqul Islam Khan, 2010. "Long Memory Features in Return and Volatility of the Malaysian Stock Market," Economics Bulletin, AccessEcon, vol. 30(4), pages 3267-3281.
    7. Adnan Kasman & Erdost Torun, 2007. "Long Memory in the Turkish Stock Market Return and Volatility," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 7(2), pages 13-27.
    8. Pilar Grau-Carles, 2005. "Tests of Long Memory: A Bootstrap Approach," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 103-113, February.
    9. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    10. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    11. Luo, Di & Mishra, Tapas & Yarovaya, Larisa & Zhang, Zhuang, 2021. "Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    12. Kasman, Adnan & Kasman, Saadet & Torun, Erdost, 2009. "Dual long memory property in returns and volatility: Evidence from the CEE countries' stock markets," Emerging Markets Review, Elsevier, vol. 10(2), pages 122-139, June.
    13. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    14. Baillie, Richard T. & Chung, Sang-Kuck, 2002. "Modeling and forecasting from trend-stationary long memory models with applications to climatology," International Journal of Forecasting, Elsevier, vol. 18(2), pages 215-226.
    15. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    16. Jonathan Wright, 2002. "Log-Periodogram Estimation Of Long Memory Volatility Dependencies With Conditionally Heavy Tailed Returns," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 397-417.
    17. Geoffrey Ngene & Kenneth A. Tah & Ali F. Darrat, 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, John Wiley & Sons, vol. 34(1), pages 61-73, September.
    18. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2014. "Predicting BRICS stock returns using ARFIMA models," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1159-1166, September.
    19. Serpil TURKYILMAZ & Mesut BALIBEY, 2014. "Long Memory Behavior in the Returns of Pakistan Stock Market: ARFIMA-FIGARCH Models," International Journal of Economics and Financial Issues, Econjournals, vol. 4(2), pages 400-410.
    20. Huimin Chung & William T. Lin & Soushan Wu, 2000. "An Analysis of Long Memory in Volatility for Asian Stock Markets," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 309-330.
    21. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    22. John T. Barkoulas & Christopher F. Baum & Nickolaos Travlos, 1996. "Long Memory in the Greek Stock Market," Boston College Working Papers in Economics 356., Boston College Department of Economics.
    23. Moussa Wajdi & Mgadmi Nidhal & Regaïeg Rym, 2018. "On the Co-movements between Exchange Rate and Stock Price from Japan: A Multivariate FIGARCH-DCC Approach," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 7(4), pages 1-4.
    24. Lahiani, Amine & jeribi, Ahmed & Jlassi, Nabila Boukef, 2021. "Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures," Research in International Business and Finance, Elsevier, vol. 56(C).
    25. Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
    26. Thomas Lux, 1996. "Long-term stochastic dependence in financial prices: evidence from the German stock market," Applied Economics Letters, Taylor & Francis Journals, vol. 3(11), pages 701-706.
    27. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    28. Cajueiro, Daniel O. & Tabak, Benjamin M., 2004. "Evidence of long range dependence in Asian equity markets: the role of liquidity and market restrictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(3), pages 656-664.
    29. Keith Jefferis & Pako Thupayagale, 2008. "Long Memory In Southern African Stock Markets," South African Journal of Economics, Economic Society of South Africa, vol. 76(3), pages 384-398, September.
    30. Olan Henry, 2002. "Long memory in stock returns: some international evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 12(10), pages 725-729.
    31. Bana Abuzayed & Nedal Al-Fayoumi & Lanouar Charfeddine, 2018. "Long range dependence in an emerging stock market’s sectors: volatility modelling and VaR forecasting," Applied Economics, Taylor & Francis Journals, vol. 50(23), pages 2569-2599, May.
    32. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    33. McIver, Ron P. & Kang, Sang Hoon, 2020. "Financial crises and the dynamics of the spillovers between the U.S. and BRICS stock markets," Research in International Business and Finance, Elsevier, vol. 54(C).
    34. Guglielmo Maria Caporale & Luis Gil-Alana, 2004. "Long range dependence in daily stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 14(6), pages 375-383.
    35. Hiemstra, Craig & Jones, Jonathan D., 1997. "Another look at long memory in common stock returns," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 373-401, December.
    36. Kang, Sang Hoon & Yoon, Seong-Min, 2007. "Long memory properties in return and volatility: Evidence from the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 591-600.
    37. Amine Lahiani & Ahmed Jeribi & Nabila Boukef Jlassi, 2021. "Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures," Post-Print hal-03573206, HAL.
    38. Goddard, John & Onali, Enrico, 2012. "Short and long memory in stock returns data," Economics Letters, Elsevier, vol. 117(1), pages 253-255.
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    More about this item

    Keywords

    GARCH model; APARCH model; ARFIMA model; FIGARCH model; Long memory;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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