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

Author

Listed:
  • Mehmet Selman Colak
  • Ibrahim Ethem Guney
  • Ahmet Senol
  • Muhammed Hasan Yilmaz

Abstract

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.

Suggested Citation

  • Mehmet Selman Colak & Ibrahim Ethem Guney & Ahmet Senol & Muhammed Hasan Yilmaz, 2019. "Monitoring and Forecasting Cyclical Dynamics in Bank Credits: Evidence from Turkish Banking Sector," Working Papers 1929, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
  • Handle: RePEc:tcb:wpaper:1929
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    File URL: https://www.tcmb.gov.tr/wps/wcm/connect/EN/TCMB+EN/Main+Menu/Publications/Research/Working+Paperss/2019/19-29
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    More about this item

    Keywords

    Credit cycle; Macroeconomic dynamics; Filtering; Factor models; Forecasting;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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