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What explains foreign portfolio investment inflows to BRICS countries?

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  • Kumar, Virender
  • Dua, Pami

Abstract

Large and volatile foreign portfolio investment (FPI) inflows raise financial instability concerns and create several macroeconomic challenges for developing countries. Identifying the factors driving FPI inflows is therefore essential for formulating appropriate policies to manage these inflows efficiently. This study uses a GM-FMOLS panel cointegration approach to examine and identify such factors for BRICS countries, the leading recipients of FPI inflows among all developing countries. The study finds that the 10-year US Treasury bond yield exerts a significant negative influence on FPI inflows. These bonds provide risk-free long-term returns to portfolio investors; hence, a rise in the yield on such bonds prompts them to readjust their portfolios away from assets in developing countries to US Treasuries, leading to a decline in FPI inflows. The study further finds that a thriving domestic stock market, a higher interest rate differential, an appreciating domestic currency, an improvement in the recipient country’s creditworthiness, and a greater degree of trade openness are conducive to FPI inflows. However, a rising consumer price index, higher foreign output growth, and greater global financial volatility discourage these inflows.

Suggested Citation

  • Kumar, Virender & Dua, Pami, 2024. "What explains foreign portfolio investment inflows to BRICS countries?," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 32-46.
  • Handle: RePEc:eee:ecanpo:v:82:y:2024:i:c:p:32-46
    DOI: 10.1016/j.eap.2024.02.033
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    More about this item

    Keywords

    FPI inflows; Portfolio capital flows; Panel cointegration; Group Mean-FMOLS; BRICS;
    All these keywords.

    JEL classification:

    • F32 - International Economics - - International Finance - - - Current Account Adjustment; Short-term Capital Movements
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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