Gelişmekte olan piyasa döviz opsiyonlarının zımni volatilite yüzeyinin tahmini: Ampirik bir analiz

This study aims to evaluate the predictive power of time series and machine learning models, namely Autoregressive (AR), Principal Component Analysis-Vector Autoregression (PCA-VAR), Principal Component Regression (PCR), and Feedforward Neural Networks (FNN), in modeling the implied volatility surfaces of five emerging market currencies (TRY, INR, MXN, ZAR and BRL against USD). The research assesses model performance using the Root Mean Square Error (RMSE) metric under both the Expanding Window and Rolling Window frameworks. The findings indicate that Principal Component Regression (PCR) and FNN models deliver similarly high precision, particularly for currencies exhibiting lower volatility levels. The study underscores the critical importance of model selection in financial forecasting and suggests that incorporating country-specific macroeconomic or geopolitical factors may further influence model outcomes.