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January 1, 2026International Journal of Complexity in Applied Science and TechnologyOpen Access

Financial Market Volatility Using the SVR-GARCH for Frontier Market Equities

Carl Hope Korkpoe N.A.

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Key Points

This research aims to investigate financial market volatility specifically within frontier market equities using SVR-GARCH modeling.
Employ SVR (Support Vector Regression) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) to analyze market data.
Focus on frontier market equities over a defined time period to assess volatility patterns.
Collect and analyze data from multiple financial sources to ensure robustness.
Findings indicate significant volatility patterns in frontier market equities using the SVR-GARCH model.
Proves effective in predicting market fluctuations, showcasing improved accuracy over traditional models.

Implication

Analyzes financial market volatility in frontier markets, highlighting the role of SVR-GARCH.

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Cite This Study

Carl Hope Korkpoe N.A. (2026) studied this question.

synapsesocial.com/papers/6a13e78b0e02ee3982d322d9 https://doi.org/10.1504/ijcast.2026.10078685

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Machining, the Better – A Look at Machine Learning-Based Volatility Using the SVR-GARCH for Frontier Market Equities2025
2Managing volatility risks with options contracts: insights from case studiesOriginal Research · 2025
3Applications of GARCH Models in Forecasting Financial Market Volatility: Insights from Leading Global Stock Indexes2024 · 7 citations
4Volatility Contagion during Covid-19: New Evidence from Oil, Cryptocurrency, Gold and Stock MarketsOriginal Research · 2026
5Investigating Volatility Dynamics of Stock Market Indices via GARCH Models2025 · 2 citations

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