İleri düzey işlem maliyeti fonksiyonlarını entegre eden gelişmiş dinamik çok dönemli portföy optimizasyonu

This study investigates the impact of transaction costs, often overlooked in multi-period portfolio optimization. Traditional portfolio optimization models tend to underestimate the influence of transaction costs such as bid-ask spreads, market impact costs, and commission fees by assuming these costs to be constant. However, these costs, which can vary over time, were analyzed using daily data from the most liquid ETFs between January 2009 and May 2024 to identify the exogenous variables (e.g. VIX, trading volume) that best predict transaction cost function. These variables were integrated into advanced forecasting models, including ARIMA-X, SARIMA-X, and GARCH-X, to improve transaction cost forecasting accuracy. The performance of these models was compared using metrics such as RMSE and MAE, with the SARIMA-X model achieving the lowest RMSE and MAE values, indicating the most accurate predictions. The transaction costs predicted by the SARIMA-X model were then integrated into the multi-period portfolio optimization framework developed by Boyd [4]. The findings of this study indicate that accurately forecasting these time-varying factors plays an important role in modeling transaction costs and that incorporating these costs into the optimization process enhances multi-period portfolio performance.