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Causal Relationships of the Stock Market with other Asset Classes: An Indian Perspective

Published:30 November 2022Publication History

ABSTRACT

The recent decade has seen a rapid rise in risk assets. Stocks, commodities, and cryptocurrencies have exploded to the upside. Global central banks have maintained interest rates at record low levels following the COVID-19 crisis. This has further acted as tailwinds for risky assets. With asset classes being increasingly interlinked with each other, useful information can be gained by studying these inter-relationships. This paper looks at the interrelationships between the Indian stock market Nifty index and some key asset classes such as Gold, Crude oil, short-term and long-term Indian government bond yields, the USD/INR exchange rate, and the cryptocurrency Bitcoin for the period January 2011 to December 2020. Co-integration analysis suggests the absence of long-run relationships between the Nifty and the asset classes studied. Granger causality analysis reveals bi-directional causality between Nifty and USD/INR and Crude oil returns. Gold returns, Bitcoin returns, and changes in short and long-term government bond yields uni-directionally granger-caused Nifty returns. Impulse response analysis reveals that shocks in each of the independent variables caused a shock in the Nifty that persisted for 1 to 3 weeks. Traders in the Nifty can monitor these shocks and accordingly fine-tune their strategies for possible moves in the Nifty.

References

  1. Ghulam Abbas, Shawkat Hammoudeh, Syed J. H. Shahzad, Shouyang Wang, and Yunjie Wei. 2019. Return and Volatility Connectedness between Stock Markets and Macroeconomic Factors in the G-7 Countries. Journal of Systems Science and Systems Engineering, 28(1), 1-36. https://doi.org/10.1007/s11518-018-5371-yGoogle ScholarGoogle ScholarCross RefCross Ref
  2. Kafila and Vijaya Srinivas, R. 2019. Co-integration analysis between international macroeconomic factors and S&P sensex movements. International Journal of Innovative Technology and Exploring Engineering, 8(11), 3849–3859. https://doi.org/10.35940/ijitee. K2304.0981119Google ScholarGoogle ScholarCross RefCross Ref
  3. Syed J.H. Shahzad, Dene Hurley, and Román Ferrer. 2021. US stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach. International Journal of Finance and Economics, 26(3), 3569-3587. https://doi.org/10.1002/ijfe.1976Google ScholarGoogle ScholarCross RefCross Ref
  4. Pooja Misra. 2018. An investigation of the macroeconomic factors affecting the Indian stock market. Australasian Accounting, Business and Finance Journal, 12(2): 71–86. https://doi.org/10.14453/aabfj.v12i2.5Google ScholarGoogle ScholarCross RefCross Ref
  5. Amith V. Megaravalli and Gabriele Sampagnaro. 2018. Macroeconomic indicators and their impact on stock markets in ASIAN 3: A pooled mean group approach. Cogent Economics and Finance, 6(1). https://doi.org/10.1080/23322039.2018.1432450Google ScholarGoogle ScholarCross RefCross Ref
  6.  Khalid A. Chisti, Saila Shakeel, and Khursheed A. Ganai. 2020. An Analysis of Interaction among Macroeconomic Variables through Co-integration and Causality Approach. Journal of Economics and Business, 3(2), 811-824. https://doi. org/10.31014/aior.1992.03.02.239Google ScholarGoogle ScholarCross RefCross Ref
  7. Semei Coronado, Rebeca Jiménez-Rodríguez, and Omar Rojas. 2018. An empirical analysis of the relationships between crude oil, gold and stock markets. Energy Journal, 39, 193–207. https://doi.org/10.5547/01956574.39.SI1.scorGoogle ScholarGoogle ScholarCross RefCross Ref
  8. Farhan Ahmed, Aamir A. Syed, Muhammad A. Kamal, Maria de las N. López-García, Jose P. Ramos-Requena,  and Swati Gupta. 2021. Assessing the impact of COVID-19 pandemic on the stock and commodity markets performance and sustainability: A comparative analysis of South Asian countries. Sustainability (Switzerland), 13(102), 5669. https://doi.org/10.3390/su13105669Google ScholarGoogle ScholarCross RefCross Ref
  9. Andreas Humpe and David G. McMillan. 2020. Macroeconomic variables and long-term stock market performance. A panel ARDL Co-integration approach for G7 countries. Cogent Economics and Finance, 8, 1816257. https://doi.org/10.1080/23322039.2020.1816257Google ScholarGoogle ScholarCross RefCross Ref
  10. Jung W. Lee and Tantatape Brahmasrene. 2020. An Exploration of Dynamic relationships between macroeconomic Variables and Stock prices in Korea Revisited. Journal of Asian Finance, Economics and Business, 7(10), 23-34. https://doi.org/10.13106/jafeb.2020.vol7.no10.023Google ScholarGoogle ScholarCross RefCross Ref
  11. Yang Ning, Liu C. Wah, and Luo Erdan. 2019. Stock price prediction based on error correction model and Granger causality test. Cluster Computing, 22, 4849-4858. https://doi.org/10.1007/s10586-018-2406-6Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Sabariah Nordin, Afiruddin Tapa, and Hamdan Al-Jaifi. 2018. Long run dynamic relationships between oil prices, exchange rates, stock market and interest rate in Malaysia. International Journal of Supply Chain Management, 7(6), 165-176. https://ojs.excelingtech.co.uk/index.php/IJSCM/article/view/2733Google ScholarGoogle Scholar
  13. Bisharat H. Chang, Muhammad S. Meo, Qasim R. Syed, and Zahida Chang Abro. 2019. Dynamic analysis of the relationship between stock prices and macroeconomic variables: An empirical study of Pakistan stock exchange. South Asian Journal of Business Studies, 8(3), 229-245. https://doi.org/10.1108/SAJBS-06-2018-0062Google ScholarGoogle ScholarCross RefCross Ref
  14. Rupinder Katoch and Arpit Sidhu. 2019. Revisiting causal relationship between NIFTY 50 index and macroeconomic variables in India: A partial equilibrium approach. Journal of Advanced Research in Dynamical and Control Systems, 11(10) Special Issue, 944-952. https://doi.org/10.5373/JARDCS/V11SP10/20192891Google ScholarGoogle ScholarCross RefCross Ref
  15. Ashish Varughese and Ranjith M. Abraham. 2021. Determinants of Indian stock market movements: An empirical study. SCMS Journal of Indian Management, 18(2), 139-152. https://www.scms.edu.in/uploads/journal/April-June-2021.pdfGoogle ScholarGoogle Scholar
  16. Priyanka Aggarwal and Manoj K. Manish. 2020. Effect of oil fluctuation on stock market return: An empirical study from India. International Journal of Energy Economics and Policy, 10(2), 213-217. https://doi.org/10.32479/ijeep.8802Google ScholarGoogle ScholarCross RefCross Ref
  17. Silvio J. Camilleri, Nicolanne Scicluna, and Ye Bai. 2019. Do stock markets lead or lag macroeconomic variables? Evidence from select European countries. North American Journal of Economics and Finance, 48, 170-186. https://doi.org/10.1016/j.najef.2019.01.019Google ScholarGoogle ScholarCross RefCross Ref
  18. Rudiger Dornbusch and Stanley Fischer. 1980. Exchange Rate and the Current Account. American Economic Review 70 (5), 960–971.Google ScholarGoogle Scholar
  19. James Tobin. 1969. A general equilibrium approach to monetary theory. Journal of Money, Credit and Banking, 1(1), 15–29.Google ScholarGoogle ScholarCross RefCross Ref
  20. Rizwan Ali, Inayat U. Mangla, Ramiz U. Rehman, Wuzhao Xue, Muhammad A. Naseem, and Muhammad I. Ahmad. 2020. Exchange rate, gold price, and stock market nexus: A quantile regression approach. Risks, 8(3), 1-16. https://doi.org/10.3390/risks8030086Google ScholarGoogle ScholarCross RefCross Ref
  21. Ana B.T. Prieto, and Young Hwan Lee. 2019. Determinants of stock market performance: VAR and VECM designs in Korea and Japan. Global Business and Finance Review, 24(4), 24–44. https://doi.org/10.17549/gbfr.2019.24.4.24Google ScholarGoogle ScholarCross RefCross Ref
  22. Shelly Singhal, Sangita Choudhary, and Pratap C. Biswal. 2019. Return and volatility linkages among International crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico. Resources Policy, 60, 255-261. https://doi.org/10.1016/j.resourpol.2019.01.004Google ScholarGoogle ScholarCross RefCross Ref
  23. Vinodh K. Natarajan, Muhammad A. UL Haq, Farheen Akram, and Jayendira P. Sankar. 2021. Dynamic Relationship between Stock Index and Asset Prices: A Long-run Analysis. Journal of Asian Finance, Economics and Business, 8(4), 601–611. https://doi.org/10.13106/jafeb.2021.vol8.no4.0601Google ScholarGoogle ScholarCross RefCross Ref
  24. Arpit Sidhu and Rupinder Katoch. 2021. Do international gold prices and NSE Nifty 50 move together? Advances in Mathematics: Scientific Journal, 10(1), 497-506. https://doi.org/10.37418/AMSJ.10.1.49Google ScholarGoogle ScholarCross RefCross Ref
  25. Arya Kumar, Saroj K. Biswal, and Prafulla K. Swain. 2019. A dynamic association between stock markets, Forex, gold and oil prices in Indian context. Revista ESPACIOS, 40(6). http://www.revistaespacios.com/a19v40n06/a19v40n06p20.pdfGoogle ScholarGoogle Scholar
  26. Garishma Gulyani, Priyanka Gupta, and Ramanpreet Singh. 2021. Impact of Stock Market Volatility on Gold prices during the COVID-19 pandemic. Transnational Marketing Journal, 9(3), 681-692. https://doi.org/10.33182/tmj.v9i3.1321Google ScholarGoogle ScholarCross RefCross Ref
  27. Nawaf Abuoliem, Safwan M. Nor, Ali Matar, and Terrence Hallahan. 2019. Crude oil prices, macroeconomic indicators and the financial sector in Jordan: Dynamic causes and responses. Journal of International Studies, 12(3), 131-146. https://doi.org/10.14254/2071-8330.2019/12-3/11Google ScholarGoogle ScholarCross RefCross Ref
  28. Rubaiyat A. Bhuiyan, Afzol Husain, and Changyong Zhang. 2021. A wavelet approach for causal relationship between bitcoin and conventional asset classes. Resources Policy, 71, 101971. https://doi.org/10.1016/j.resourpol.2020.101971Google ScholarGoogle ScholarCross RefCross Ref
  29. Elie I. Bouri, Syed J.H. Shahzad, David Roubaud, Ladislav Kristoufek, and Brian M. Lucey. 2020. Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis. Quarterly Review of Economics and Finance, 77, 156-164. https://doi.org/10.1016/j.qref.2020.03.004Google ScholarGoogle ScholarCross RefCross Ref
  30. Jamal Bouoiyour and Refk Selmi. 2017. Ether: Bitcoin's competitor or ally? Technical report, July 2017. https://www.researchgate.net/publication/318635518Google ScholarGoogle Scholar
  31. Dimitrios Koutmos. 2019. Market risk and Bitcoin returns. Annals of Operations Research, 294(1–2), 453–477. https://doi.org/10.1007/s10479-019-03255-6Google ScholarGoogle ScholarCross RefCross Ref
  32. Yutaka Kurihara and Akio Fukushima. 2017. The Market Efficiency of Bitcoin: A Weekly Anomaly Perspective. Journal of Applied Finance and Banking, 7(3), 1792–6599. http://www.scienpress.com/Upload/JAFB%2fVol%207_3_4.pdfGoogle ScholarGoogle Scholar
  33. David A Dickey and Wayne A. Fuller. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427–431.Google ScholarGoogle ScholarCross RefCross Ref
  34. Clive W.J. Granger. 1969. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37 (3), 424–438.Google ScholarGoogle ScholarCross RefCross Ref
  35. Robert F Engle and Clive W.J. Granger. 1987. Co-integration and Error Correction Representation, Estimation and Testing. Econometrica, 55, 251-276.Google ScholarGoogle ScholarCross RefCross Ref
  36. Saurabh Ghosh and Shekhar Tomar. 2019. The Impact of Crude Price Shock on India's Current Account Deficit, Inflation and Fiscal Deficit. Mint Street Memo No.17, 1–11. Reserve Bank of India.Google ScholarGoogle Scholar

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    • Published in

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      ICEME '22: Proceedings of the 2022 13th International Conference on E-business, Management and Economics
      July 2022
      691 pages
      ISBN:9781450396394
      DOI:10.1145/3556089

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      • Published: 30 November 2022

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