How to Reduce Extreme Risk in Portfolios with Developed and Emerging Stock Indices?

Boris Kuzman

Economic Computation and Economic Cybernetics Studies and Research2026https://doi.org/10.24818/18423264/60.1.26.10article
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What the paper says

This study constructs diversified portfolios incorporating equities from both emerging and developed markets to assess which group demonstrates reduced exposure to downside risk.This is evaluated by using both parametric and semiparametric Conditional Value at Risk (CVaR) models.An additional analysis explores return-adjusted risk efficiency through the STAR Ratio metric.The semiparametric CVaR (mCVaR) model yields notably higher risk estimates than the parametric CVaR, due to its sensitivity to distributional asymmetries such as excess kurtosis and negative skewness.Interestingly, the portfolio of emerging markets shows lower downside risk compared to its developed market counterpart, not because of inherently lower tail risk, but rather due to weaker financial integration among emerging economies.Furthermore, results from the STAR Ratio reveal that the emerging market portfolio outperforms in return-to-risk terms, largely driven by the exceptional average daily returns of Indian SENSEX index, which surpass those of any developed market index included.These findings offer practical insights for international investors and portfolio strategists allocating assets across both market categories.

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https://doi.org/https://doi.org/10.24818/18423264/60.1.26.10

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@article{boris2026,
  title        = {{How to Reduce Extreme Risk in Portfolios with Developed and Emerging Stock Indices?}},
  author       = {Boris Kuzman},
  journal      = {Economic Computation and Economic Cybernetics Studies and Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.24818/18423264/60.1.26.10},
}

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Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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