INTEREST RATE MODELING UNDER CONTEMPORARY UNCERTAINTY

Abstract

Introduction. Modern financial markets are influenced by numerous and often contradictory factors, including the consequences of the pandemic, geopolitical conflicts, military actions in Ukraine, fluctuations in energy prices, and other specific risks. Under such conditions, traditional interest rate models and approaches to monetary policy are increasingly proving to be insufficiently flexible and adaptive, which necessitates the improvement of approaches to their modeling under uncertainty.

Purpose. The purpose of the study is to analyze theoretical and empirical approaches to interest rate modeling under uncertainty and to identify the most effective approaches for application in Ukrainian conditions.

Materials and methods. The study employs econometric and financial models, in particular ARDL and TVP-ARDL for analyzing interest rate pass-through, short-rate models (Vasicek, CIR, and Hull–White modifications) for yield curve modeling, as well as time series models (ARIMA) and machine learning methods (LightGBM, RNN) for forecasting. The empirical base is grounded on studies of the Ukrainian financial market and data from the National Bank of Ukraine.

Results. It has been established that the pass-through of policy rate changes is incomplete, asymmetric, and depends on macroeconomic factors and characteristics of the banking system. It is shown that under instability, interest rates behave less predictably, which complicates the application of classical models. The study substantiates the feasibility of using more flexible modeling approaches that account for time-varying parameters, economic instability, and asymmetry of responses.

Prospects. Further research should focus on combining classical econometric models with modern machine learning methods to improve forecasting accuracy, as well as on expanding the empirical base by incorporating new data from the Ukrainian financial market.