Abstract
This study develops the Turkey banking sector stress index for the period of 2012-2021. For this, sector beta, volatility, return on assets, capital adequacy ratio, and asset quality variables were used. The variables were combined with the variance equal-weight method, and a single index was constructed. The reliability of the index was evaluated in terms of whether it detects the years shown as the economic crisis years by the experts. As a result, it has been determined that the index detects the exchange rate crisis that broke out in Turkey in 2018 and the global economic crisis caused by COVID-19, the effects of which began to appear in 2020. In addition, it is predicted that there may be an increase in the number of concordats and bankruptcies in years when the stress index is high. It has been determined that the Turkish banking sector is resistant to crises.
TopLiterature Review
Hanschel and Monnin (2005) used the stock price index of banks, yield spreads for bank-issued bonds, total interbank deposits, return on assets, bank capital, provision rate, total assets and the number of bank branches variables to measure the stress of the Swiss banking sector for the period 1987-2002. Then, by combining all the variables with the variance equal-weight method, they created a single index showing the stress of the Swiss banking sector. As a result, it was stated that the index caught the stress of the years that were shown as crisis years by experts. The researchers also built a regression model to predict the index and found that the variables of Swiss and European GDP, loan and investment rates, stock price index, and house price explain a large part of the stress level in the banking sector.
Geršl and Heřmánek (2007) aimed to construct a financial stability index for the Czech Republic banking sector for the period 1997-2007. For this, they used capital adequacy, asset quality, profitability, liquidity, interest rate risk, and foreign exchange risk variables. All variables were given different weights and the variables were combined with the variance weighting method. As a result, they revealed that the Czech Republic's banking sector has good resilience to potential shocks.
Dridi, El Ghourabi and Limam (2010) used total deposits, variation in bank capital, loans to the private sector, foreign liabilities of banks, and total assets variables to measure the financial stress in the Tunisian banking sector for the period 1975-2006. They preferred the variance equal-weight method to combine the variables. They proposed a stress index model based on the theory of extreme value to predict the stress of the banking sector. As a result, it has been stated that this model can be used to accurately describe stress periods.
Puddu (2013) studied various alternative approaches to constructing the US banking sector stress index. Monthly data for the period 1984-2007 were used in the study. The variables used in the study are banks' return on assets, net interest margin, the ratio of net loan losses to average total loans, the ratio of non-performing loans to total loans, the ratio of loan loss reserve to total loans, and a number of the failed commercial banks. He developed two models based on signaling and a zero-inflated Poisson approach to construct the index and compared the results with the variance equal-weight and factor analysis approaches. As a result, it has been revealed that the index constructed based on zero-inflated Poisson outperforms other approaches in capturing stress in the industry.
Key Terms in this Chapter
Volatility: It is the variability in the price of the security. High volatility is an indicator of uncertainty.
Economic Crisis: Abnormal fluctuations in the prices of goods, services, production, and foreign exchange.
CAR: It is a measure of bank capital.
Stress Index: It is to reveal the current instability in financial markets and to show it with a single variable.
Sector Beta: It is a measure of the yield volatility of the relevant sector relative to the whole market.