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This study investigates the impact of geopolitical events on the volatility dynamics of Ukrainian financial markets amid the Russia-Ukraine conflict. It analyzes historical price data of key indices and companies, including the PFTS Index, Kernel Holding S.A., MHP SE, and Ukrnafta by computing volatility using rolling window measures and technical indicators such as ATR, PSO, and Bollinger Bands. A bidirectional LSTM model is employed to integrate these indicators, enabling the capture of temporal dependencies in both directions and yielding high predictive accuracy. The findings reveal distinct, asset-specific volatility patterns, providing valuable insights for risk assessment and strategic investment decisions in times of geopolitical uncertainty.

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Market Volatility Prediction Under Powerful Geopolitical Events. A Case Study on the Latest European Conflict

  • 会议论文

  • 发表日期：

    2026年

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  • Lavinia Roxana Toma

摘要

This study investigates the impact of geopolitical events on the volatility dynamics of Ukrainian financial markets amid the Russia-Ukraine conflict. It analyzes historical price data of key indices and companies, including the PFTS Index, Kernel Holding S.A., MHP SE, and Ukrnafta by computing volatility using rolling window measures and technical indicators such as ATR, PSO, and Bollinger Bands. A bidirectional LSTM model is employed to integrate these indicators, enabling the capture of temporal dependencies in both directions and yielding high predictive accuracy. The findings reveal distinct, asset-specific volatility patterns, providing valuable insights for risk assessment and strategic investment decisions in times of geopolitical uncertainty.