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Inflation and infrastructure sector returns in emerging markets—panel ARDL approach

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  • Rabson Magweva
  • Mabutho Sibanda
  • Mariam Camarero

Abstract

This study evaluated the relationship between inflation and infrastructure sector stock returns in emerging markets in the long and short run. It employed a panel autoregressive distributed lag (PARDL) model applying the mean group (MG), pooled mean group (PMG) and dynamic fixed effects (DFE) estimators after preliminary cross-sectional dependence and stationarity tests. The results from the three estimators were insignificant in both the short and long run, illustrating the inability of infrastructure sector returns in emerging markets to hedge inflation. Similar results were obtained when the inflation-hedging capacity of real estate and general listed equity was assessed. This suggests the existence of significant beta risk in emerging stock markets. The results imply that investors interested in hedging inflation in emerging markets should go beyond individual asset classes and embrace the portfolio optimization concept to reduce inflation risk. Given the heterogenic nature of the infrastructure sector, a deeper analysis that focuses on infrastructure sector sub-categories might be fruitful as the pricing power is heterogeneous across these sub-sectors.

Suggested Citation

  • Rabson Magweva & Mabutho Sibanda & Mariam Camarero, 2020. "Inflation and infrastructure sector returns in emerging markets—panel ARDL approach," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1730078-173, January.
  • Handle: RePEc:taf:oaefxx:v:8:y:2020:i:1:p:1730078
    DOI: 10.1080/23322039.2020.1730078
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    5. Tanzeem Hasnat, 2021. "Infrastructure Equity and Firm Performance in India," Millennial Asia, , vol. 12(1), pages 97-115, April.
    6. Ogede Jimoh S. & Tiamiyu Hammed O., 2023. "Does Financial Inclusion Moderate CO2 Emissions in Sub-Saharan Africa? Evidence From Panel Data Analysis," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 33(3), pages 21-36, September.
    7. Muñoz-Villamizar, Andrés & Piatti, Matias & Mejía-Argueta, Christopher & Pirabe, Luis Felipe & Namdar, Jafar & Gomez, Juan Felipe, 2024. "Navigating retail inflation in Brazil: A machine learning and web scraping approach to the basic food basket," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).

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