Effects of securitization and covered bonds on bank stability
TL;DR: In this paper, the authors investigated the relationship of securitization and covered bonds with bank stability and highlighted that this relationship varies with the level of a bank's involvement in a specific instrument.
About: This article is published in Research in International Business and Finance.The article was published on 2020-10-01. It has received 12 citations till now. The article focuses on the topics: Covered bond & Securitization.
Citations
More filters
TL;DR: In this article, the authors examined the impact of asset securitizations on the performance and financial stability of banks in a dual banking system (i.e., Islamic and conventional) using a unique sample of international banks located in 21 countries.
22 citations
TL;DR: In this article, the impact of market competition on the stability of Islamic and conventional banks in countries where these banks operate alongside one another is examined, and the authors find that competition can be beneficial for banks, especially at a low to medium competition level.
12 citations
13 Jul 2021-Competitiveness Review: An International Business Journal Incorporating Journal of Global Competitiveness
Abstract:
Purpose
The purpose of this paper is to investigate the impact of intellectual capital (IC) efficiency on the banks’ risk-taking and stability of Asian emerging markets.
Design/methodology/approach
This study uses a sample of 204 listed banks from 12 Asian emerging countries for the period 2010 to 2019. Data were analyzed using Ordinary Least Squares regression and checked for robustness using system generalized methods moment (GMM) estimation. The dependent variable of bank stability is measured using Z-score-based return on assets (ROA) and return on equity (ROE). The second dependent variable of bank risk is proxied by the standard deviation of ROA, ROE, non-performing loans and loan loss provision.
Findings
The results suggest the IC efficiency has no association with bank risk-taking and stability. The findings lend no support to the resource-based theory. The robustness of this result is confirmed by the system GMM estimation. However, support is found for the competition fragility view as high market power is associated with low risk-taking. The IC subcomponents, human capital efficiency (HCE) report a negative coefficient for bank risk-taking thereby having no support for the hypothesized relationships. Diversified banks with a higher deposit to total asset ratio resort to high risk-taking.
Research limitations/implications
IC efficiency does not have an impact on the bank’s risk-taking behavior and stability for Asian banks. Managers can use these findings to improve their IC and boost investor confidence. Regulatory authorities should increase its monitoring function of banks when the GDP decreases as risk-taking behavior are galvanized during this period.
Originality/value
This research is one of the first to provide empirical evidence of IC efficiency’s relationship with bank stability and bank risk-taking. The implications are useful for policymakers, managers and governing bodies to enhance the banks’ IC efficiency.
10 citations
TL;DR: In this paper, the authors analyzed the relationship between securitization and crash risk in a sample of large European banks listed on the EuroStoxx 600 between 2000 and 2017.
6 citations
TL;DR: In this article , the authors examined the impact of asset securitizations on the performance and financial stability of banks in a dual banking system (i.e., Islamic and conventional) using a unique sample of international banks located in 21 countries.
6 citations
References
More filters
Book•
29 Jul 2021TL;DR: This book presents some of the most important modeling and prediction techniques, along with relevant applications, that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
Abstract: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
3,439 citations
TL;DR: The authors proposed a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit, and GMM that enables cluster-robust inference when there is two-way or multiway clustering that is nonnested.
Abstract: In this article we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit, and GMM. This variance estimator enables cluster-robust inference when there is two-way or multiway clustering that is nonnested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g., Liang and Zeger 1986; Arellano 1987) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state–year effects example of Bertrand, Duflo, and Mullainathan (2004) to two dimensions; and by application to studies in the empirical literature where two-way clustering is present.
2,542 citations
TL;DR: In this paper, the authors discuss a method to estimate the capital that a financial firm would need to raise if we have another financial crisis, based on publicly available information but is conceptually similar to the stress tests conducted by US and European regulators.
Abstract: The financial crisis of 2007-2009 has given way to the sovereign debt crisis of 2010-2012, yet many of the banking issues remain the same. We discuss a method to estimate the capital that a financial firm would need to raise if we have another financial crisis. This measure of capital shortfall is based on publicly available information but is conceptually similar to the stress tests conducted by US and European regulators. We argue that this measure summarizes the major characteristics of systemic risk and provides a reliable interpretation of the past and current financial crises.
941 citations
TL;DR: Bekaert et al. as mentioned in this paper introduced SRISK, a measure to measure the systemic risk contribution of a financial firm, which is a function of its size, leverage and risk.
Abstract: We introduce SRISK to measure the systemic risk contribution of a financial firm. SRISK measures the capital shortfall of a firm conditional on a severe market decline, and is a function of its size, leverage and risk. We use the measure to study top financial institutions in the recent financial crisis. SRISK delivers useful rankings of systemic institutions at various stages of the crisis and identifies Fannie Mae, Freddie Mac, Morgan Stanley, Bear Stearns, and Lehman Brothers as top contributors as early as 2005-Q1. Moreover, aggregate SRISK provides early warning signals of distress in indicators of real activity.Received June 7, 2011; accepted April 18, 2016 by Editor Geert Bekaert.
592 citations
555 citations