The Concept Of Financial Stability And Financial Stability Index For Turkey

2016 ◽  
Vol 27 (101) ◽  
pp. 111
Author(s):  
Faruk Sanar ◽  
Mahmut Kara
Keyword(s):  
Financial Stability ◽  
Stability Index

Identification of excessive Russia's household debt burden

2021 ◽  
Vol 21 (1) ◽  
pp. 189-207
Author(s):  
Galina Gospodarchuk ◽  
Ekaterina Suchkova
Keyword(s):  
Financial Stability ◽  
Evaluation Method ◽  
Gap Analysis ◽  
Stability Index ◽  
Threshold Value ◽  
Federal State ◽  
Household Debt ◽  
Calculation Algorithm ◽  
Debt Burden ◽  
Household Sector

Modern trends characterized by increasing Russian household debt against the stagnation of real income of the population demonstrate the importance of analytical tools within the regulator. It helps identify the level of debt burden in household sector and to develop an analytical toolkit that makes it possible to reveal debt burden. The paper uses the methods of statistic and graphical analysis as well as comparative and GAP-analysis. The empirical analysis is based on data of the Federal State Statics Service and Bank of Russia over a period of 1.01.2007–1.01.2019. The study develops the methodology to create an indicator for household sector debt burden both on macro- and micro-level. Based on the methodology, we develop a new financial stability index of household sector and its calculation algorithm. We offer the evaluation method of threshold value of this index and determine its quantitative value. The findings concerning debt burden level in Russia’s household sector drawn on the basis of this indicator confirm its suitability for using as an additional diagnostic tool of Russia’s financial stability.

scholarly journals Panel modeling of z-score: evidence from Islamic and conventional Saudi banks

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hassan Belkacem Ghassan ◽  
Abdelkrim Ahmed Guendouz
Keyword(s):  
Financial Stability ◽  
Banking Sector ◽  
Stability Index ◽  
Z Score ◽  
Strong Concentration ◽  
Content Type ◽  
Quarterly Data ◽  
Annual Frequency ◽  
Conventional Banks ◽  
The Stability

Purpose This paper aims to measure the stability extent of the banking sector in Saudi Arabia, including Islamic and conventional banks (CBs), using quarterly data. Design/methodology/approach The paper uses seemingly unrelated regressions to estimate the determinants of the z-score. Findings The panel data model shows that Islamic banks (IBs) reduce the financial stability index relatively; meanwhile, they contribute efficiently to enhance the financial stability through the diversification of their assets. The Saudi banking sector exhibits strong concentration affecting the financial stability negatively. Research limitations/implications The paper’s topic can be extended to cover the recent period. Practical implications The limited presence of IBs in the Saudi banking sector jeopardizes any effort to improve the financial stability. Social implications By attracting more clients, IBs would contribute more to the financial stability in the Saudi economy. Also, the monetary authority has to expand the share of IBs in the financial system at least 50-50 compared to CBs. Originality/value The z-score is mostly analyzed with yearly data; in this paper we use quarterly data to describe at infra-annual frequency the variability of the z-score index. Also, we consider in detail the statistical properties of the banks’ data.

scholarly journals Financial Stability Index for the Financial Sector of Pakistan

Economies ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 81
Author(s):  
Sadia Babar ◽  
Rashid Latief ◽  
Sumaira Ashraf ◽  
Sania Nawaz
Keyword(s):  
Financial Stability ◽  
Stability Index ◽  
Financial Sector ◽  
Decision Makers ◽  
Return On Assets ◽  
Financial Reports ◽  
Liquid Asset ◽  
Return On Equity ◽  
Linear Probability

This study aims to develop a financial stability index for the Pakistani financial sector by using the financial reports for the period of 2001–2011. Specifically, we constructed three different classes of indices in this study based on a variance-equal weighted approach, a linear probability approach, and a logistic approach. We also assessed the prediction accuracy of the financial stability index. All indices indicated that profitability, liquid liability to the liquid asset, non-performing loan, uncovered liabilities, interest spread and inter-fund to liquid liabilities variables contribute significantly to the determination of financial stress of commercial banks. We also compared the results of indices computed with different methodologies—among them was the index constructed by employing coefficients of the logistic model and which performed outstandingly in predicting distressed and non-distressed banks. Moreover, the findings of this study suggest that in regard to return on assets and return on equity, when employed in a stepwise manner for developing the financial stability index, the results are similar in the sense that both profitability indicators have the same behavior. Finally, we conclude that the financial stability indices developed in this study could help decision makers to detect and avoid instability in the future.

scholarly journals Impact of determinants of the financial distress on financial sustainability of Ethiopian commercial banks

2019 ◽  
Vol 14 (3) ◽  
pp. 187-201 ◽  
Author(s):  
Kishor Meher ◽  
Henok Getaneh
Keyword(s):  
Financial Distress ◽  
Commercial Banks ◽  
Financial Stability ◽  
Liquidity Risk ◽  
Stability Index ◽  
Financial Sustainability ◽  
Net Income ◽  
Return On Assets ◽  
Asset Quality ◽  
Bank Soundness

The study aims to investigate the impact of determinants of financial distress on financial sustainability of Ethiopian commercial banks. The balanced panel data of 12 commercial banks of Ethiopia have been taken for the study from 2011 to 2017. The research deploys Ordinary Least Square (OLS) Regression Model. The indicators of financial distress are bank’s specific internals and macro-economic factors. The proxies of financial sustainability are Return on Assets, Return on Equity, Financial Stability Index and Bank Soundness. The findings reveal that the Absolute Liquidity Risk and Net Income Growth are found to be positive and significant and Solvency Risk negative and significant in relation to Return on Assets. Asset Quality is found to be positive and significant and Solvency Risk negative and significant with respect to Return on Equity. The Asset Quality and Net Income Risk are positive and significant and Solvency Risk is negative and significant with relation to the Financial Stability Index. Absolute Liquidity Risk and Liquidity Risk are positive and significant and Credit Risk negative and significant with Bank Soundness. Free Cash Flow and Net Income Growth are essential for enhancing Return on Assets and Bank Soundness, and managing equity within the prudential norms could bring forth short-term financial sustainability of commercial banks. By lowering provisioning of loan loss, Growth in Net Interest Income and managing Solvency Risk could ensure financial stability to the banks, which in turn leads to financial sustainability. The study reveals that financial sustainability of banks is insulated from the exposures of systematic risks originating from macroeconomic factors.

The Effect of Equity Linked Securities on Financial Stability in Korea

2018 ◽  
Vol 26 (1) ◽  
pp. 85-114
Author(s):  
Sun-Joong Yoon ◽  
Jaehoon Jung
Keyword(s):  
Interest Rate ◽  
Financial Markets ◽  
Rapid Growth ◽  
Financial Stability ◽  
Stability Index ◽  
Negative Effects ◽  
Structured Products ◽  
Korean Economy ◽  
Retail Investors ◽  
The Relationship

Since the introduction of ELS (Equity-linked securities) in 2003, the structured products have become one of the most important investment vehicles to Korean retail investors. However, the rapid growth of those structured products has induced the imbalance of Korean financial markets and may have eventually damaged the financial stability of Korean economy. In this paper, we investigate how Korean securities companies issuing the structured products hedge their positions and how their activities affect the financial stability. In addition, we conduct a simple empirical analysis to examine the relationship between the issue of ELS and the financial stability using FSI (financial stability index) provided by Bank of Korea. According to the results, the balance of ELS affects the financial stability negatively and this is significant even after adjusting for the control variables such as the KOSPI index, VKOSPI, the risk-free interest rate, and CPI. More specifically, the balance rather than the amount of monthly issuance is significant to financial stability. In addition, the decrease in underlying indices reduces the early redemption, thereby damaging the financial stability. Lastly, we suggest several solutions to alleviate the negative effects.

scholarly journals Monetary Policy and Financial Stability in the Nigerian Banking Industry

2019 ◽  
Vol 11 (1) ◽  
pp. 82
Author(s):  
Oparah Felix Chukwudi ◽  
James Tumba Henry
Keyword(s):  
Monetary Policy ◽  
Financial Stability ◽  
Banking Industry ◽  
Stability Index ◽  
Open Market ◽  
Long Run ◽  
Market Operation ◽  
The Impact ◽  
Nigerian Banking ◽  
Long Run Equilibrium

This study examined the impact of monetary policy on financial stability in the Nigerian banking industry for the period 2008Q1 to 2016Q2, using an error correction model. Banking industry financial stability index (BIFSI) was computed within the study and was used as a measure of financial stability in the Nigerian banking industry. The study discovered that the impact of monetary policy on financial stability in the Nigerian banking industry was weak. It also revealed a significant long run equilibrium relationship between monetary policy and financial stability in the Nigerian banking industry with a speed of adjustment to long run equilibrium of 66.54%. It was concluded that open market operation and exchange rate channels are more effective channels of transmitting monetary policy to financial stability in the banking industry, than interest rate channel.

A financial stability index for Colombia

2010 ◽  
Vol 6 (4) ◽  
pp. 555-581 ◽  
Author(s):  
Miguel A. Morales ◽  
Dairo Estrada
Keyword(s):  
Financial Stability ◽  
Stability Index

scholarly journals KONSTRUKSI INDEKS KESTABILAN SISTEM KEUANGAN INDONESIA

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Anhar Fauzan Priyono
Keyword(s):  
Financial Stability ◽  
Banking Sector ◽  
Principal Component ◽  
Stability Index ◽  
System Stability ◽  
Equal Weight ◽  
Market Sector ◽  
Index Measurement ◽  
Managing Risk ◽  
The Interest Rate

Financial system stability is necessary to ensure a sustainable economic development. It undertakes 3 major functions: (i) payment system, (ii) financial intermediation, and (iii) managing risk. Data showed that the Indonesian economy experienced a negative correction in the event of financial instability, e.g bank panic in 1992, Asian financial crisis (1997), and Sub-prime mortgage crisis (2008). Therefore, it is necessary in having a method of financial stability index measurement, which in turn can be used to predict the direction of future financial stability. This research was conducted in order to provide an option incalculating the index of financial stability of Indonesia by two methods, namelyAggregation with Variance Equal Weight with Principal Component Analysis (PCA). The results show that the trend of Indonesian financial stability index which constructed through these two techniques have similar trend with a different magnitude. PCA method was employed in making reductions on variable dimensions without losing the information on the movement of the variable’s variation. There are four sectors to be included in the index. Those four sectors are banking sector, money market sector, capital market sector,and monetary sector. We found that the contribution of the financial performance of banks in Indonesia and the interest rate is the highest among other sector to the Indonesia financial stability.

scholarly journals United States banking stability: An explanation through machine learning

2020 ◽  
Vol 15 (4) ◽  
pp. 137-149
Author(s):  
José Alejandro Fernández Fernández
Keyword(s):  
United States ◽  
Machine Learning ◽  
Interest Rates ◽  
Financial Stability ◽  
Ad Hoc ◽  
Stability Index ◽  
The United States ◽  
Important Variable ◽  
Bank Stability ◽  
Bank Solvency

In this paper, an analysis of the prediction of bank stability in the United States from 1990 to 2017 is carried out, using bank solvency, delinquency and an ad hoc bank stability indicator as variables to measure said stability. Different machine learning assembly models have been used in the study, a random forest is developed because it is the most accurate of all those tested. Another novel element of the work is the use of partial dependency graphs (PDP) and individual conditional expectation curves (ICES) to interpret the results that allow observing for specific values how the banking variables vary, when the macro-financial variables vary.It is concluded that the most determining variables to predict bank solvency in the United States are interest rates, specifically the mortgage rate and the 5 and 10-year interest rates of treasury bonds, reducing solvency as these rates increase. For delinquency, the most important variable is the unemployment rate in the forecast. The financial stability index is made up of the normalized difference between the two factors obtained, one for solvency and the other for delinquency. The index prediction concludes that stability worsens as BBB corporate yield increases.

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