FORECASTING EMERGING MARKET EXCHANGE RATE VOLATILITY POST-GEOPOLITICAL SHOCKS
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The following study is about examining the impact of an AI in decision making model alongside standard statistic model use to predict exchange rate volatility in new market currency during major news worldwide. For this we will be using foreign exchange daily spot rate data relative to the US Dollar across 5 major emerging market currencies—the Brazilian Real (BRL), South African Rand (ZAR), Turkish Lira (TRY), Indian Rupee (INR), and Indonesian Rupiah (IDR)—from 2018 to 2024. We will also compare traditional GARCH ecosystem against a AI model combining this model with a short LLM network. The result will indicate if the hybrid AI model performs better than standalone classical methods and generates more accurate volatility forecasts at the end of our experiment.
Keywords
Artificial Intelligence, Bounded Rationality, Emerging Markets, Exchange Rate Volatility, Foreign Exchange, Geopolitical Shocks, Hybrid Garch-Lstm, Machine Learning, Risk Management, Time-Series Forecasting