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Several developing economies witnessed a large number of systemic financial and currency crises since the 1980s that resulted in severe economic, social, and political…
Several developing economies witnessed a large number of systemic financial and currency crises since the 1980s that resulted in severe economic, social, and political problems. The devastating impact of the 1982 and 1994–1995 Mexican crises, the 1997–1998 Asian financial crisis, the 1998 Russian crisis, and the ongoing financial crisis of 2008–2009 suggests that maintaining financial sector stability through reduction in vulnerability is highly crucial. The world is now witnessing an unprecedented systemic financial crisis originated from the USA in September 2008 together with a deep worldwide economic recession, particularly in developed countries of Europe and North America. This calls for devising and using on a regular basis an appropriate and effective monitoring and policy formulation system for detecting and addressing vulnerabilities leading to crisis. This chapter proposes a macroprudential/financial soundness monitoring, analysis, and remedial policy formulation system that can be used by most developing countries with or without crisis experience as well as with limited data. It also discusses a process for identifying and compiling a set of leading macroprudential/financial soundness indicators. An empirical illustration using Philippines data is presented. There is an urgent need for increased coordination, collaboration, and partnership among central banks, banking and financial market supervision agencies, and ministries of finance, economic, and planning for proper macroprudential monitoring. A high-level national financial stability committee under the auspices of the head of the state as well as a ‘‘regional financial stability board’’ needs to be established to complement and support the activities of an “international stability board.”
This paper aims to establish the effect of bank regulations on financial stability in Kenya. Specifically, the study seeks to uncover the effect of micro and macro…
This paper aims to establish the effect of bank regulations on financial stability in Kenya. Specifically, the study seeks to uncover the effect of micro and macro prudential regulations on financial stability and their trade-offs or complementarities.
Using annual time series data over the period 1990–2017, the study uses structural equation model (SEM) estimation technique. This solves the problem of approximating measurement errors, using both latent constructs and indicator constructs.
Study findings reveal that macro and micro prudential regulations are significant drivers of financial stability. Further, prudential regulations are more effective when they complement each other.
This study centers on how bank regulations affect financial stability. Future research could be carried out on the effect of Non-Bank Financial Institutions regulations on financial system stability.
Complementing macro and micro prudential regulation is more effective and efficient in ensuring stability of the financial system other than letting the two policy objectives operate independently.
Regulatory authorities should introduce prudential regulations that would encourage innovations in the banking sector. This ensures easy deposit mobilization that enhances financial inclusion. Prudential regulations that ensure financial stability will be effective when low income earners are included in the financial system.
To the best of the authors’ knowledge, this study is the first to investigate the role of banking regulations on financial stability. This study is also pioneering in the use of SEM estimation technique, in examining how prudential regulations affect financial stability. Previous cross-country studies have focused on macro prudential regulations ignoring the importance of micro prudential regulations.
The purpose of this paper is to develop an explicitly macroprudential supervisory framework designed to identify threats to financial stability use existing mechanisms to…
The purpose of this paper is to develop an explicitly macroprudential supervisory framework designed to identify threats to financial stability use existing mechanisms to reduce the risk of these threats and to provide information to the authorities to more efficiently mitigate any instability that does arise.
This paper begins with an analysis of the limitations of microprudential regulation. It then develops a macroprudential surveillance framework focused on those financial markets that have the potential to undermine financial stability. It concludes with a discussion of how the surveillance results may be used to enhance financial stability.
The current supervisory focus on microprudential supervision of systemically important institutions is insufficient; an explicitly macroprudential focus is required.
Although this paper’s conceptual framework is applicable to all advanced financial systems the discussion of specific regulatory structures focuses on the USA.
An explicit supervisory focus on the threats posed by major financial markets is feasible and desirable.
The probability of a financial crisis and the economic damage caused by a crisis can be significantly reduced by redirecting some regulatory efforts toward in-depth analysis of major financial markets.
The paper emphasizes that macroprudential supervision must include both quantitative and detailed analysis of the qualitative aspects of key markets.
The role of macroprudential policies (MPPs) in influencing bank risk-taking has recently attracted significant attention in the literature. Several studies have emerged…
The role of macroprudential policies (MPPs) in influencing bank risk-taking has recently attracted significant attention in the literature. Several studies have emerged, both at the cross-country level as well as at the level of individual countries that have examined this issue. However, whether and to what extent do MPPs affect risk-taking by Gulf Cooperation Council (GCC) banks has not been investigated in prior empirical research. Toward this end, using data during 1996-2010, the author examines the impact of MPPs on risk-taking by GCC banks. The author considers the entire gamut of MPPs – those focused on credit, capital and liquidity – and how they impact bank risk.
In view of the possible endogeneity between the dependent variables and the crucial independent variable (i.e. MPP), the paper uses advanced panel data techniques that address this endogeneity. Toward this end, the author uses dynamic panel data methodology to examine the interlinkage between bank risk taking and MPPs for GCC banks.
The findings appear to suggest that although MPPs are useful, not all of them are equally effective in containing the potential build-up of financial stress. Viewed from this standpoint, it appears that capital adequacy ratios and reserve requirements are the ones with maximum efficacy in limiting potential build-up of risks. Classifying the MPPs as per their impact on major balance sheet variables, the results indicate that capital-related measures tend to exert the greatest impact on credit.
A significant volume of literature has emerged in recent years that examine the efficacy of MPPs on bank risk-taking. Notwithstanding available cross-country research, limited analysis on this aspect in the context of GCC banks. Toward this end, an extended sample of GCC banks has been used to examine this issue. To the best of the author’s knowledge, this is one of the earliest studies for GCC banking systems to examine this issue.
Despite the sophisticated regulatory regime established in Solvency II, analysts should be able to consider other less complex indicators of the soundness of insurers. The…
Despite the sophisticated regulatory regime established in Solvency II, analysts should be able to consider other less complex indicators of the soundness of insurers. The Z-score measure, which has traditionally been used as a proxy of individual risk in the banking sector, may be a useful tool when applied in the insurance sector. However, different methods for calculating this indicator have been proposed in the literature. This paper compares six different Z-score approaches to examine which one best fits insurance companies. The authors use a final dataset of 183 firms (1,382 observations) operating in the Spanish insurance sector during the period 2010–2017.
In the first stage, the authors opt for a root mean squared error (RMSE) criterion to evaluate which of the various mean and SD estimates that are used to compute the Z-score best fits the data. In the second stage, the authors estimate and compare the explanatory power of the six Z-score measures that are considered by using an ordinary least squares (OLS) regression model. Finally, the authors report the results of the baseline equation using the system-GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) for dynamic panel data models.
The authors find that the best formula for calculating the Z-score of insurance firms is the one that combines the current value of the return on assets (ROA) and capitalization with the SD of the returns calculated over the full sample period.
The main limitation of the research is that it addresses only the Spanish insurance sector, and consequently, the implications of the findings must be framed in this institutional context. However, the authors think that the results could be extrapolated to other countries. Future research should consider including different countries and analyzing the usefulness of aggregated insurer-level Z-scores for macroprudential monitoring.
The Z-score may be a useful early warning indicator for microprudential supervision. In addition to being an indicator of the soundness of insurers simpler than those established in the current regulation, the information provided by this accounting-based measure may help analysts and investors obtain a better understanding of insurance firms' risk factors.
To the best of the authors’ knowledge, this study is the first to examine and compare different approaches to calculating Z-scores in the insurance sector. The few available results on the predictive power of the Z-score are mixed and focus on the banking sector.
雖然在償付能力標準II 內已建立了精密的監管制度，但分析人員應可以考慮以不太複雑的指標，來分析保險公司的穩健程度。Z-分數的估量在銀行業一向作為是個體風險的代理而使用，而Z-分數如應用於保險業，或許會成為有用的工具。唯在文獻裏，學者和研究人員提出了不同的方法來計算這個指標。本文比較六個不同的Z-分數估量方法，以研究出最適合保險公司的方法。我們使用一個最終數據集，包括在2010年至2017年期間在西班牙保險業界營運的183間公司（1382 個觀察）。
在首個階段，我們選擇使用一個方均根誤差(RMSE) 標準來衡量用來計算Z-分數的各個平均值和標準差估量中哪個最適合使用於有關的數據。在第二個階段， 我們以普通最小平方 (OLS) 迴歸模型，去估計並比較被考慮的六個Z-分數估量的解釋力。最後，我們以Arellano與Bover (1995), 以及Blundell與Bond (1998) 為動態追蹤資料模型而發展出來的系統-廣義動差估計推定量，來發表我們基線方程式的結果。