| Abstract [eng] |
The aim of this master's thesis is to evaluate the role of leading economic indicators in forecasting financial market volatility, especially in the Baltic stock markets. In an unstable global environment characterized by economic and geopolitical uncertainty, forecasting volatility becomes extremely important for investor decision making and ensuring market stability. The thesis consists of three main parts: literature review, methodology and empirical analysis, and conclusions and proposals. The literature reviews the concept of volatility, estimation methods and predictive power of leading indicators, with a special focus on the VIX, GEPU, CLI and FSI indicators. The empirical part uses data from the OMX Baltic Benchmark GI index and selected leading indicators. AR, GARCH, EGARCH and GARCH-MIDAS models were applied. The results showed that the VIX index had the highest predictive power (R² = 0.4692), FSI and CLI were also statistically significant, but weaker. GEPU was statistically significant, but explained almost no volatility (R² = 0.0023). Sensitivity analysis confirmed the robustness of the results when changing the number of lags. The study showed that global uncertainty and stress indicators, especially VIX, can be used as effective tools for forecasting volatility in small and open markets. The study contributes to the understanding of risk dynamics in European emerging markets and shows that combining macro indicators and high-frequency models improves forecasting accuracy. |
