Communications for Statistical Applications and Methods

  • 제31권5호
  • /
  • Pages.535-556
  • /
  • 2024
  • /
  • 2287-7843(pISSN)
  • /
  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

A GARCH-MIDAS approach to modelling stock returns

  • Ezekiel NN Nortey (Department of Statistics and Actuarial Science, University of Ghana) ;
  • Ruben Agbeli (Department of Statistics and Actuarial Science, University of Ghana) ;
  • Godwin Debrah (Department of Statistics and Actuarial Science, University of Ghana) ;
  • Theophilus Ansah-Narh (Ghana Atomic Energy Commission) ;
  • Edmund Fosu Agyemang (School of Mathematical and Statistical Science, College of Sciences, University of Texas Rio Grande Valley)
  • 투고 : 2024.02.21
  • 심사 : 2024.05.20
  • 발행 : 2024.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Measuring stock market volatility and its determinants is critical for stock market participants, as volatility spillover effects affect corporate performance. This study adopted a novel approach to analysing and implementing GARCH-MIDAS modelling methods. The classical GARCH as a benchmark and the univariate GARCH-MIDAS framework are the GARCH family models whose forecasting outcomes are examined. The outcome of GARCH-MIDAS analyses suggests that inflation, interest rate, exchange rate, and oil price are significant determinants of the volatility of the Johannesburg Stock Market All Share Index. While for Nigeria, the volatility reacts significantly to the exchange rate and oil price. Furthermore, inflation, exchange rate, interest rate, and oil price significantly influence Ghanaian equity volatility, especially for the long-term volatility component. The significant shock of the oil price and exchange rate to volatility is present in all three markets using the generalized autoregressive conditional heteroscedastic-mixed data sampling (GARCH-MIDAS) framework. The GARCH-MIDAS, with a powerful fusion of the GARCH model's volatility-capturing capabilities and the MIDAS approach's ability to handle mixed-frequency data, predicts the volatility for all variables better than the traditional GARCH framework. Incorporating these two techniques provides an innovative and comprehensive approach to modelling stock returns, making it an extremely useful tool for researchers, financial analysts, and investors.

키워드

과제정보

The authors thank the Ghana Stock Exchange, the Nigeria Stock Exchange and the South African Stock Exchange for providing access to the dataset used for the research and the anonymous reviewers whose insightful comments helped enrich the work.

참고문헌

  1. Abaidoo R and Agyapong EK (2022). Financial development and institutional quality among emerging economies, Journal of Economics and Development, 24, 198-216.
  2. Abaidoo R and Agyapong EK (2023). Macroeconomic risk, uncertainty and financial market development: Emerging markets perspective, Global Business Review, 09721509221145356.
  3. Abebe TH, Woldesenbet EG, and Zeleke BL (2022). Statistical analysis of price volatility of agricultural commodities traded at the Ethiopian commodity exchange (ECX) using multiplicative GARCH-MIDAS two-component model, Global Business Review, 23, 925-945.
  4. Acheampong AO, Boateng E, Amponsah M, and Dzator J (2021). Revisiting the economic growth- energy consumption nexus: Does globalization matter?, Energy Economics, 102, 105472.
  5. Adegboye A and Akinyele OD (2022). Assessing the determinants of government spending efficiency in Africa, Future Business Journal, 8, 1-17.
  6. Aghabazaz Z, Kazemi I, and Nematollahi A (2022). A time-varying GARCH mixed-effects model for isolating high-and low-frequency volatility and co-volatility, Statistical Modelling, 24, pages 1471082X221080488.
  7. Ali PI, Nzotta SM, Akujuobi ABC, and Nwaimo CE (2020). Impact of macroeconomic variables on stock return volatility: Evidence from Sub-Sahara Africa, International Business and Accounting Research Journal, 4, 58-70.
  8. Benson AK, Habanabakize T, and Fortune G (2022). The impact of the selected macroeconomic indicators' volatility on the performance of South African JSE-listed companies: A pre-and post-Covid-19 study, International Journal of Research in Business and Social Science (2147-4478), 11, 193-204.
  9. Conrad C and Loch K (2015). Anticipating long-term stock market volatility, Journal of Applied Econometrics, 30, 1090-1114.
  10. Dieppe A, Francis N, and Kindberg-Hanlon G (2021). The identification of dominant macroeconomic drivers: Coping with confounding shocks, ECB Working Paper No. 2021/2534, Available from SSRN: https://ssrn.com/abstract=3823160 or http://dx.doi.org/10.2139/ssrn.3823160
  11. Engle RF (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the Econometric Society, 50, 987-1007.
  12. Engle RF, Ghysels E, and Sohn B (2013). Stock market volatility and macroeconomic fundamentals, Review of Economics and Statistics, 95, 776-797.
  13. Ghysels E, Sinko A, and Valkanov R (2007). MIDAS regressions: Further results and new directions, Econometric Reviews, 26, 53-90.
  14. Harvey AC (1990). The Econometric Analysis of Time Series, Mit Press, Cambridge.
  15. Hendry DF and Doornik JA (1997). Modelling Dynamic Systems Using PcFiml 9.0 for Windows, International Thomson Business Press, London.
  16. Jiang X, Wang X, Ren J, and Xie Z (2021). The Nexus between digital finance and economic development: Evidence from China, Sustainability, 13, 7289.
  17. Nasir MA, Shahbaz M, Mai TT, and Shubita M (2021). Development of Vietnamese stock market: Influence of domestic macroeconomic environment and regional markets, International Journal of Finance & Economics, 26,1435-1458.
  18. Nobre J and Neves RF (2019). Combining principal component analysis, discrete wavelet transform and XGBoost to trade in the financial markets, Expert Systems with Applications, 125, 181-194.
  19. Nyeadi JD (2023). Foreign direct investment and firm value: Evidence from selected countries in sub-Saharan Africa, Journal of African Business, 24, 260-279.
  20. Owusu-Antwi G (2009). Impact of financial reforms on the banking system in Ghana, International Business & Economics Research Journal (IBER), 8, 77-100.
  21. Rumaly N (2023). Unlocking profitability: Exploring the impact of bank-specific and macroeconomic determinants on return on equity in commercial banking sector of Bangladesh, International Journal of Economics and Financial Issues, 13, 107-115.
  22. Salisu AA, Gupta R, and Bouri E, and Ji Q (2020). The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach, Research in International Business and Finance, 54, 101308.
  23. Sarpong B and Nketiah-Amponsah E (2022). Financial inclusion and inclusive growth in sub-Saharan Africa, Cogent Economics & Finance, 10, 2058734.
  24. Tarza Sokpo J, Iorember PT, and Usar T (2017). Inflation and stock market returns volatility: Evidence from the Nigerian stock exchange 1995Q1-2016Q4: An E-GARCH approach, International Journal of Econometrics and Financial Management, 5, 69-76.
  25. Usman Z and Landry D (2021). Economic diversification in Africa: How and why it matters, Available at SSRN 3842228, 1-35.
  26. Wang F and Ghysels E (2015). Econometric analysis of volatility component models, Econometric Theory, 31, 362-393.
  27. Wang L, Ma F, Liu J, and Yang L (2020). Forecasting stock price volatility: New evidence from the GARCH-MIDAS model, International Journal of Forecasting, 36, 684-694.
  28. Yao C-Z and Li M-J (2023). GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets, The North American Journal of Economics and Finance, 66, 101910.
  29. Yu X, Huang Y, and Xiao K (2021). Global economic policy uncertainty and stock volatility: Evidence from emerging economies, Journal of Applied Economics, 24, 416-440.
  30. Yusuf O (2022). FDI and its impact on growth in Africa, In Management and Leadership for a Sustainable Africa, Volume 1: Dimensions, Practices and Footprints, 251-272, Springer.