Navigating Volatility and Uncertainty: A Hybrid Forecasting Model for Strategic Investment Decisions in Emerging Markets

Abstract

Emerging markets present both significant opportunities and considerable challenges for investors. High growth potential is often coupled with increased volatility and uncertainty, demanding sophisticated forecasting models for informed investment decisions. This paper proposes a novel hybrid forecasting model that integrates the strengths of traditional econometric techniques, specifically Generalized Autoregressive Conditional Heteroskedasticity (GARCH), with machine learning algorithms, such as Random Forests, to predict asset returns in emerging markets. By combining GARCH's ability to capture volatility clustering with Random Forests' capacity for non-linear pattern recognition, the hybrid model aims to enhance forecasting accuracy and improve risk management. The model's performance is evaluated using historical data from a selection of emerging market indices, demonstrating its superior predictive power compared to benchmark models. The findings provide valuable insights for investors seeking to navigate the complexities of emerging market investments and make more strategic, data-driven decisions.