Monitoring and Forecasting Cyclical Dynamics in Bank Credits: Evidence from Turkish Banking Sector

Credit growth rate deviating from its long-run trend or equilibrium value holds importance for policymakers given the implications on economic activity and macro-financial interactions. In the first part of this study, the main aim is to construct indicators for determining the episodes of moderate-to-excessive credit slowdown and expansion by utilizing time-series filtering methods such as Hodrick-Prescott filter, Butterworth filter, Christiano-Fitzgerald filter and Hamilton filter over the time period 2007-2019. In addition to filtering choices, four different credit ratios (which are credit-to-GDP ratio, real credit growth, logarithm of real credit, credit impulse ratio) are included in the methodology to ensure the robustness. This framework enables one to generate monitoring tools for not only total loans, but also for financial intermediation activities with different loan breakdowns regarding type, sector and currency denomination. Moreover, industry-based dynamics of commercial loans are examined by using micro-level Credit Registry data set. In the following part, the credit cycle implied by macroeconomic dynamics are investigated by using factor-augmented predictive regression models. In this context, factors representing the global economic developments, banking sector outlook, local financial conditions and economic growth tendencies are created from large data set of 107 time series by utilizing principal component analysis. Analysis conducted for January 2009-April 2019 interval seems to be in line with exogenous shocks affecting the credit market in the corresponding period. To gain more knowledge about the predictive power of factor-augmented regression models, out-of-sample forecasting exercises are performed. It is found that global forces and economic activity provide substantial improvement in terms of predictive power over simple autoregressive benchmark models given low level of relative forecast errors.