nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒04‒17
23 papers chosen by

  1. Assessing the contribution of South African Insurance Firms to Systemic Risk By Zulu, Thulani; Manguzvane, Mathias Mandla; Bonga-Bonga, Lumengo
  2. A Multilevel Stochastic Approximation Algorithm for Value-at-Risk and Expected Shortfall Estimation By Stéphane Crépey; Noufel Frikha; Azar Louzi
  3. Portfolio Volatility Estimation Relative to Stock Market Cross-Sectional Intrinsic Entropy By Claudiu Vinte; Marcel Ausloos
  4. Lone (loan) wolf pack risk By Gao, Mingze; Hasan, Iftekhar; Qiu, Buhui; Wu, Eliza
  5. Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets By Yun-Shi Dai; Peng-Fei Dai; Wei-Xing Zhou
  6. Optimal investment with insurable background risk and nonlinear portfolio allocation frictions By Hugo E. Ramirez; Rafael Serrano
  7. Mean field game of mutual holding with defaultable agents, and systemic risk By Mao Fabrice Djete; Gaoyue Guo; Nizar Touzi
  8. A fixed point approach for computing actuarially fair Pareto optimal risk-sharing rules By Fallou Niakh
  9. The Impact of Feature Selection and Transformation on Machine Learning Methods in Determining the Credit Scoring By Oguz Koc; Omur Ugur; A. Sevtap Kestel
  10. The cyclicality of bank credit losses and capital ratios under expected loss model By Fatouh, Mahmoud; Giansante, Simone
  11. Risk management in public universities in pursuit of performance: A synthesis of the literature By Gazoulit Sarra; Khadija Oubal
  12. Combining Risk Adjustment with Risk Sharing in Health Plan Payment Systems: Private Health Insurance in Australia By Josefa Henriquez; Richard C. van Kleef; Andrew Matthews; Thomas McGuire; Francesco Paolucci
  13. Probabilistic Overview of Probabilities of Default for Low Default Portfolios by K. Pluto and D. Tasche By Andrius Grigutis
  14. Network log-ARCH models for forecasting stock market volatility By Raffaele Mattera; Philipp Otto
  15. High-Frequency Volatility Estimation with Fast Multiple Change Points Detection By Greeshma Balabhadra; El Mehdi Ainasse; Pawel Polak
  16. Reconciling rough volatility with jumps By Eduardo Abi Jaber; Nathan De Carvalho
  17. A parsimonious neural network approach to solve portfolio optimization problems without using dynamic programming By Pieter M. van Staden; Peter A. Forsyth; Yuying Li
  18. Estimation of Asymmetric Stochastic Volatility in Mean Models By Antonis Demos
  19. Longevity, Health and Housing Risks Management in Retirement By Pierre-Carl Michaud; Pascal St. Amour
  20. The demand for long-term mortgage contracts and the role of collateral By Liu, Lu
  21. Recursive preferences, correlation aversion, and the temporal resolution of uncertainty By Stanca Lorenzo
  22. Cost of Implementation of Basel III reforms in Bangladesh -- A Panel data analysis By Dipti Rani Hazra; Md. Shah Naoaj; Mohammed Mahinur Alam; Abdul Kader
  23. Bank capital and economic activity By Paul-Olivier Klein; Rima Turk-Ariss

  1. By: Zulu, Thulani; Manguzvane, Mathias Mandla; Bonga-Bonga, Lumengo
    Abstract: In light of the crucial contribution insurance firms make to global investment, this paper examines the extent of systemic risks facing emerging market insurance, with a particular focus on South Africa, one of the African continent's most prominent emerging economies. Contrary to past studies, the paper relies on delta conditional value at risk (∆CoVaR) based dynamic conditional correlation (DCC)-GARCH model to this end. Moreover, the paper assesses how selected developed economies contribute to the systemic of the South African insurance industry. The results of the empirical analysis show that Santam, Sanlam, and Momentum Holdings account for the largest systemic risks. At the same time, the least contributors are Discovery and Liberty. Meanwhile, Australia and Japan appear to contribute the most to systemic risk in the South African insurance industry. Moreover, the paper finds that periods of economic turmoil significantly increased developed markets' systemic risk contributions to the South African insurance industry.
    Keywords: Delta conditional value at risk; dcc-gjr-garch; systemically important financial institutions.
    JEL: C58 F3 G22
    Date: 2023
  2. By: Stéphane Crépey (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité); Noufel Frikha (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Azar Louzi (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité)
    Abstract: We propose a multilevel stochastic approximation (MLSA) scheme for the computation of the Value-at-Risk (VaR) and the Expected Shortfall (ES) of a financial loss, which can only be computed via simulations conditional on the realization of future risk factors. Thus, the problem of estimating its VaR and ES is nested in nature and can be viewed as an instance of a stochastic approximation problem with biased innovation. In this framework, for a prescribed accuracy ε, the optimal complexity of a standard stochastic approximation algorithm is shown to be of order ε −3. To estimate the VaR, our MLSA algorithm attains an optimal complexity of order ε −2−δ , where δ
    Keywords: Value-at-Risk, Expected Shortfall, stochastic approximation algorithm, Nested Monte Carlo, Multilevel Monte Carlo
    Date: 2023–03–22
  3. By: Claudiu Vinte; Marcel Ausloos
    Abstract: Selecting stock portfolios and assessing their relative volatility risk compared to the market as a whole, market indices, or other portfolios is of great importance to professional fund managers and individual investors alike. Our research uses the cross-sectional intrinsic entropy (CSIE) model to estimate the cross-sectional volatility of the stock groups that can be considered together as portfolio constituents. In our study, we benchmark portfolio volatility risks against the volatility of the entire market provided by the CSIE and the volatility of market indices computed using longitudinal data. This article introduces CSIE-based betas to characterise the relative volatility risk of the portfolio against market indices and the market as a whole. We empirically prove that, through CSIE-based betas, multiple sets of symbols that outperform the market indices in terms of rate of return while maintaining the same level of risk or even lower than the one exhibited by the market index can be discovered, for any given time interval. These sets of symbols can be used as constituent stock portfolios and, in connection with the perspective provided by the CSIE volatility estimates, to hierarchically assess their relative volatility risk within the broader context of the overall volatility of the stock market.
    Date: 2023–03
  4. By: Gao, Mingze; Hasan, Iftekhar; Qiu, Buhui; Wu, Eliza
    Abstract: This paper proposes an early-warning bank risk measure based on the syndicate concentration of recent syndicated loans that a bank participates in. At the bank level, higher values of the measure predict greater risks (i.e., loan loss provisions, idiosyncratic return volatility, default probability, and frequency of lawsuits) and lower profitability at least three years ahead, especially for opaque and complex banks. Banks failing the Federal Reserve's forward-looking stress tests subsequently exhibit a reduction in the syndicate concentration measure. At the aggregate level, higher values of the measure predict both greater financial sector risks and economic slowdowns measured by private-sector investment, business activity, total factor productivity, industrial production, and gross domestic product.
    Keywords: syndicate concentration, early-warning, bank risks, financial sector risks, economic slowdowns
    JEL: G21 E02
    Date: 2023
  5. By: Yun-Shi Dai; Peng-Fei Dai; Wei-Xing Zhou
    Abstract: This paper combines the Copula-CoVaR approach with the ARMA-GARCH-skewed Student-t model to investigate the tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets, taking four main agricultural commodities, namely soybean, maize, wheat, and rice as examples. The empirical results indicate that the tail dependence structures for the four futures-spot pairs are quite different, and each of them exhibits a certain degree of asymmetry. In addition, the futures market for each agricultural commodity has significant and robust extreme downside and upside risk spillover effects on the spot market, and the downside risk spillover effects for both soybeans and maize are significantly stronger than their corresponding upside risk spillover effects, while there is no significant strength difference between the two risk spillover effects for wheat, and rice. This study provides a theoretical basis for strengthening global food cooperation and maintaining global food security, and has practical significance for investors to use agricultural commodities for risk management and portfolio optimization.
    Date: 2023–03
  6. By: Hugo E. Ramirez; Rafael Serrano
    Abstract: We study investment and insurance demand decisions for an agent in a theoretical continuous-time expected utility maximization model that combines risky assets with an (exogenous) insurable background risk. This risk takes the form of a jump-diffusion process with negative jumps in the return rate of the (self-financed) wealth. The main distinctive feature of our model is that the agent's decision on portfolio choice and insurance demand causes nonlinear friction in the dynamics of the wealth process. We use the dynamic programming approach to find optimality conditions under which the agent assumes the insurable risk entirely, or partially, or purchases total insurance against it. In particular, we consider differential and piece-wise linear portfolio allocation frictions, with differential borrowing and lending rates as our most emblematic example. Finally, we present a mutual-fund separation result and illustrate our results with several numerical examples when the adverse jump risk has Beta distribution.
    Date: 2023–03
  7. By: Mao Fabrice Djete; Gaoyue Guo; Nizar Touzi
    Abstract: We introduce the possibility of default in the mean field game of mutual holding of Djete and Touzi [11]. This is modeled by introducing absorption at the origin of the equity process. We provide an explicit solution of this mean field game. Moreover, we provide a particle system approximation, and we derive an autonomous equation for the time evolution of the default probability, or equivalently the law of the hitting time of the origin by the equity process. The systemic risk is thus described by the evolution of the default probability.
    Date: 2023–03
  8. By: Fallou Niakh
    Abstract: Risk-sharing is one way to pool risks without the need for a third party. To ensure the attractiveness of such a system, the rule should be accepted and understood by all participants. A desirable risk-sharing rule should fulfill actuarial fairness and Pareto optimality while being easy to compute. This paper establishes a one-to-one correspondence between an actuarially fair Pareto optimal (AFPO) risk-sharing rule and a fixed point of a specific function. A fast numerical method for computing these risk-sharing rules is also derived. As a result, we are able to compute AFPO risk-sharing rules for a large number of heterogeneous participants in this framework.
    Date: 2023–03
  9. By: Oguz Koc; Omur Ugur; A. Sevtap Kestel
    Abstract: Banks utilize credit scoring as an important indicator of financial strength and eligibility for credit. Scoring models aim to assign statistical odds or probabilities for predicting if there is a risk of nonpayment in relation to many other factors which may be involved in. This paper aims to illustrate the beneficial use of the eight machine learning (ML) methods (Support Vector Machine, Gaussian Naive Bayes, Decision Trees, Random Forest, XGBoost, K-Nearest Neighbors, Multi-layer Perceptron Neural Networks) and Logistic Regression in finding the default risk as well as the features contributing to it. An extensive comparison is made in three aspects: (i) which ML models with and without its own wrapper feature selection performs the best; (ii) how feature selection combined with appropriate data scaling method influences the performance; (iii) which of the most successful combination (algorithm, feature selection, and scaling) delivers the best validation indicators such as accuracy rate, Type I and II errors and AUC. An open-access credit scoring default risk data sets on German and Australian cases are taken into account, for which we determine the best method, scaling, and features contributing to default risk best and compare our findings with the literature ones in related. We illustrate the positive contribution of the selection method and scaling on the performance indicators compared to the existing literature.
    Date: 2023–03
  10. By: Fatouh, Mahmoud (Bank of England); Giansante, Simone (University of Palermo)
    Abstract: We model the evolution of stylised bank loan portfolios to assess the impact of IFRS 9 and US GAAP expected loss model (ECL) on the cyclicality of loan write-off losses, loan loss provisions (LLPs) and capital ratios of banks, relative to the incurred loss model of IAS 39. We focus on the interaction between the changes in LLPs' charges (the flow channel) and stocks (the stock channel) under ECL. Our results show that, when GDP growth does not demonstrate high volatility, ECL model smooths the impact of credit losses on profits and capital resources, reducing the procyclicality of capital and leverage ratios, especially under US GAAP. However, when GDP growth is highly volatile, the large differences in lifetime probabilities of defaults (PDs) between booms and busts cause sharp increases in LLPs in deep downturns, as seen for US banks during the Covid-19 crisis. Volatile GDP growth makes capital and leverage ratios more procyclical, with sharper falls in both ratios in deep downturns under US GAAP, compared to IAS 39. IFRS 9 ECL demonstrates less sensitivity to lifetime PDs fluctuations due to the existence of loan stages, and hence can reduce the procyclicality of capital and leverage ratios, even when GDP is highly volatile.
    Keywords: IFRS 9; IAS 39; US GAAP; expected credit loss model; loan loss provisions; cyclicality of bank profits; leverage ratio; risk-weighted assets
    JEL: D92 G21 G28 G31 L51
    Date: 2023–01–20
  11. By: Gazoulit Sarra (UM5R - Université Mohammed V de Rabat); Khadija Oubal (UMVR - Université Mohammed V de Rabat)
    Abstract: Risk management has become an emerging trend in the higher education sector due to the dynamic and evolving environment of the sector and the opportunities it offers to control the risks involved, improve the governance system and achieve the expected performance. The purpose of this work is to analyze the link between risk management and performance in public universities, through a review of the literature, based on recent bibliographic references. The analysis revealed that, in the majority of cases, there is a significant link between the risk management system, whose implementation is based on sequential steps (risk identification, risk assessment, risk treatment and risk monitoring), and the performance of public universities, which is in itself measured by several indicators (academic excellence, scientific production, information technology, satisfaction of stakeholder expectations and by increased responsiveness).
    Abstract: La gestion des risques est devenue une tendance émergente dans le secteur de l'enseignement supérieur en raison de l'environnement dynamique et évolutif du secteur et des possibilités qu'elle offre pour maîtriser les risques encourus, améliorer le système de gouvernance et atteindre les performances attendues. Ce travail a pour objet d'analyser le lien entre la gestion des risques et la performance en milieu des universités publiques, à travers une revue de la littérature, basée sur des références bibliographiques récentes. L'analyse a révélé que, dans la majorité des cas, il existe un lien significatif entre le système de gestion des risques, dont la mise en œuvre est basée sur des étapes enchaînées (l'identification des risques, l'évaluation des risques, le traitement des risques et le suivi des risques), et la performance des universités publiques, qui est en elle-même mesurée par plusieurs indicateurs (l'excellence académique, la production scientifique, les technologies de l'information, la satisfaction des attentes des parties prenantes et par la réactivité accrue).
    Keywords: Risk, risk management, risk management systems, performance, public university., OUBAL Khadija 2 (0000-0002-7486-9724 * Enseignante Chercheuse), OUBAL Khadija 2, (0000-0002-7486-9724 *, Enseignante Chercheuse)
    Date: 2023–01–21
  12. By: Josefa Henriquez; Richard C. van Kleef; Andrew Matthews; Thomas McGuire; Francesco Paolucci
    Abstract: Health plan payment systems with community-rated premiums typically include risk adjustment, risk sharing or both to compensate insurers for predictable profits (on young and healthy people) and predictable losses (on the elderly and chronically ill). This paper shows how a payment system based only on risk sharing (like in Australia), is improved by combining risk sharing with risk adjustment. Using Australia’s private health insurance market as a case study, we compare and assess the current risk sharing based payment system against alternative systems which combine risk adjustment and risk sharing. Specifically, we develop outcome measures to compare the models in terms of incentives for risk selection and incentives for cost control. We find that a payment system composed of risk adjustment based on simple risk-adjustor variables, supplemented with outlier risk sharing outperforms the current system based solely on risk sharing. Our results show that as more and better data become available, reliance on risk sharing can be reduced whilst the use of risk adjustment can be expanded. In an additional analysis, we show that changes in the payment system affect the redistribution of claims costs across different levels of coverage. We discuss qualitatively additional measures that can be taken to achieve the desired level of redistribution.
    JEL: I11 I13
    Date: 2023–03
  13. By: Andrius Grigutis
    Abstract: This article gives a probabilistic overview of the widely used method of default probability estimation proposed by K. Pluto and D. Tasche. There are listed detailed assumptions and derivation of the inequality where the probability of default is involved under the influence of systematic factor. The author anticipates adding more clarity, especially for early career analysts or scholars, regarding the assumption of borrowers' independence, conditional independence and interaction between the probability distributions such as binomial, beta, normal and others. There is also shown the relation between the probability of default and the joint distribution of $\sqrt{\varrho}X-\sqrt{1-\varrho}Y$, where $X$, including but not limiting, is the standard normal, $Y$ admits, including but not limiting, the beta-normal distribution and $X, \, Y$ are independent.
    Date: 2023–02
  14. By: Raffaele Mattera; Philipp Otto
    Abstract: This paper presents a novel dynamic network autoregressive conditional heteroscedasticity (ARCH) model based on spatiotemporal ARCH models to forecast volatility in the US stock market. To improve the forecasting accuracy, the model integrates temporally lagged volatility information and information from adjacent nodes, which may instantaneously spill across the entire network. The model is also suitable for high-dimensional cases where multivariate ARCH models are typically no longer applicable. We adopt the theoretical foundations from spatiotemporal statistics and transfer the dynamic ARCH model for processes to networks. This new approach is compared with independent univariate log-ARCH models. We could quantify the improvements due to the instantaneous network ARCH effects, which are studied for the first time in this paper. The edges are determined based on various distance and correlation measures between the time series. The performances of the alternative networks' definitions are compared in terms of out-of-sample accuracy. Furthermore, we consider ensemble forecasts based on different network definitions.
    Date: 2023–03
  15. By: Greeshma Balabhadra; El Mehdi Ainasse; Pawel Polak
    Abstract: We propose high-frequency volatility estimators with multiple change points that are $\ell_1$-regularized versions of two classical estimators: quadratic variation and bipower variation. We establish consistency of these estimators for the true unobserved volatility and the change points locations under general sub-Weibull distribution assumptions on the jump process. The proposed estimators employ the computationally efficient least angle regression algorithm for estimation purposes, followed by a reduced dynamic programming step to refine the final number of change points. In terms of numerical performance, the proposed estimators are computationally fast and accurately identify breakpoints near the end of the sample, which is highly desirable in today's electronic trading environment. In terms of out-of-sample volatility prediction, our new estimators provide more realistic and smoother volatility forecasts, and they outperform a wide range of classical and recent volatility estimators across various frequencies and forecasting horizons.
    Date: 2023–03
  16. By: Eduardo Abi Jaber; Nathan De Carvalho
    Abstract: We reconcile rough volatility models and jump models using a class of reversionary Heston models with fast mean reversions and large vol-of-vols. Starting from hyper-rough Heston models with a Hurst index $H \in (-1/2, 1/2)$, we derive a Markovian approximating class of one dimensional reversionary Heston-type models. Such proxies encode a trade-off between an exploding vol-of-vol and a fast mean-reversion speed controlled by a reversionary time-scale $\epsilon>0$ and an unconstrained parameter $H \in \mathbb R$. Sending $\epsilon$ to 0 yields convergence of the reversionary Heston model towards different explicit asymptotic regimes based on the value of the parameter H. In particular, for $H \leq -1/2$, the reversionary Heston model converges to a class of L\'evy jump processes of Normal Inverse Gaussian type. Numerical illustrations show that the reversionary Heston model is capable of generating at-the-money skews similar to the ones generated by rough, hyper-rough and jump models.
    Date: 2023–03
  17. By: Pieter M. van Staden; Peter A. Forsyth; Yuying Li
    Abstract: We present a parsimonious neural network approach, which does not rely on dynamic programming techniques, to solve dynamic portfolio optimization problems subject to multiple investment constraints. The number of parameters of the (potentially deep) neural network remains independent of the number of portfolio rebalancing events, and in contrast to, for example, reinforcement learning, the approach avoids the computation of high-dimensional conditional expectations. As a result, the approach remains practical even when considering large numbers of underlying assets, long investment time horizons or very frequent rebalancing events. We prove convergence of the numerical solution to the theoretical optimal solution of a large class of problems under fairly general conditions, and present ground truth analyses for a number of popular formulations, including mean-variance and mean-conditional value-at-risk problems. We also show that it is feasible to solve Sortino ratio-inspired objectives (penalizing only the variance of wealth outcomes below the mean) in dynamic trading settings with the proposed approach. Using numerical experiments, we demonstrate that if the investment objective functional is separable in the sense of dynamic programming, the correct time-consistent optimal investment strategy is recovered, otherwise we obtain the correct pre-commitment (time-inconsistent) investment strategy. The proposed approach remains agnostic as to the underlying data generating assumptions, and results are illustrated using (i) parametric models for underlying asset returns, (ii) stationary block bootstrap resampling of empirical returns, and (iii) generative adversarial network (GAN)-generated synthetic asset returns.
    Date: 2023–03
  18. By: Antonis Demos (
    Abstract: Here we investigate the estimation of asymmetric Autoregressive Stochastic Volatility models with possibly time varying risk premia. We employ the Indirect Inference estimation developed in Gallant and Tauchen (1996), with a first step estimator either the Generalized Quadratic ARCH or the Exponential GARCH. We employ Monte-Carlo simulations to compare the two first step models in terms of bias and root Mean Squared Error. We apply the developed methods for the estimation of an asymmetric autoregressive SV-M model to international stock markets excess returns.
    Keywords: Stochastic Volatility estimation asymmetry leverage indirect inference
    Date: 2023–03–21
  19. By: Pierre-Carl Michaud; Pascal St. Amour
    Abstract: Annuities, long-term care insurance and reverse mortgages remain unpopular to manage longevity, medical and housing price risks after retirement. We analyze low demand using a life-cycle model structurally estimated with a unique stated-preference survey experiment of Canadian households. Low risk aversion, substitution between housing and consumption and low marginal utility when in poor health explain most of the reduced demand. Bequests motives are found to be a luxury good and play a limited role. The remaining disinterest is explained by information frictions and behavioural status-quo biases. We find evidence of strong spousal co-insurance motives motivating LTCI and of responsiveness to bundling with a near doubling of demand for annuities when reverse mortgages can be used to annuitize, instead of consuming home equity.
    JEL: G51 G53 I13 J14
    Date: 2023–03
  20. By: Liu, Lu (The Wharton School, University of Pennsylvania)
    Abstract: Long-term fixed-rate mortgage contracts protect households against interest rate risk, yet most countries have relatively short interest rate fixation lengths. Using administrative data from the UK, the paper finds that the choice of fixation length tracks the life-cycle decline of credit risk in the mortgage market: the loan-to-value (LTV) ratio decreases and collateral coverage improves over the life of the loan due to principal repayment and house price appreciation. High-LTV borrowers, who pay large initial credit spreads, trade off their insurance motive against reducing credit spreads over time using shorter-term contracts. To quantify demand for long-term contracts, I develop a life-cycle model of optimal mortgage fixation choice. With baseline house price growth and interest rate risk, households prefer shorter-term contracts at high LTV levels, and longer-term contracts once LTV is sufficiently low, in line with the data. The mechanism helps explain reduced and heterogeneous demand for long-term mortgage contracts.
    Keywords: Mortgage choice; house prices; credit risk; interest rate risk; household risk management; household finance.
    JEL: D15 E43 G21 G22 G50 G52
    Date: 2023–01–06
  21. By: Stanca Lorenzo (Department of Economics, Social Studies, Applied Mathematics and Statistics (ESOMAS) and Collegio Carlo Alberto, University of Torino, Italy;)
    Abstract: Models of recursive utility are of central importance in many economic applications. This paper investigates a new behavioral feature exhibited by these models: aversion to risks that exhibit persistence (positive autocorrelation) through time, referred to as correlation aversion. I introduce a formal notion of such a property and provide a characterization based on risk attitudes, and show that correlation averse preferences admit a specific variational representation. I discuss how these findings imply that attitudes toward correlation are a crucial behavioral aspect driving the applications of recursive utility in fields such as asset pricing, climate policy, and optimal fiscal policy.
    Keywords: Intertemporal Substitution, Risk Aversion, Correlation Aversion, Recursive Utility, Preference for Early Resolution of Uncertainty, Information.
    JEL: C61 D81
    Date: 2023–04
  22. By: Dipti Rani Hazra; Md. Shah Naoaj; Mohammed Mahinur Alam; Abdul Kader
    Abstract: Inspired by the recent debate on the macroeconomic implications of the new bank regulatory standards known as Basel III, we tried to find out in this study that the impact of Basel III liquidity and capital requirements in Bangladesh proposed by Basel Committee on Banking Supervision (BCBS, 2010a). A small set of macro variables, using a sample of 22 private commercial banks operating in Bangladesh for the period of 2010-2014, are used to estimate long-run relationships among the variables. The macroeconomic variables are included The profitability of banks, GDP, banks' lending to private sector, Net Stable Funding Ratio, Tier 1 capital Ratio, Interest rate spread, real interest rate. The cost is quantified using Driscoll and Kraay panel data models with fixed effect. Impact of higher capital and liquidity requirement on Interest rate spread and lending to private sector of banks were considered as the cost to the economy as a whole whereas impact of higher capital and liquidity requirement on profitability of banks(ROE) was considered as the cost of banks. Here it is found that, the interest rate level is positively affected by the tighter liquidity and capital requirements which driven toward lessen of the private sector lending of banks. The return on equity of banks varies negatively with the liquidity and capital. The economic costs are considerably below the estimated positive benefit that the reform should have by reducing the probability of banking crises and the associated banking losses (BCBS, 2010b).
    Date: 2023–03
  23. By: Paul-Olivier Klein (Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon); Rima Turk-Ariss (International Monetary Fund (IMF))
    Abstract: Banks argue that holding higher capital will have adverse implications on their lending activities and thereby on economic growth. Yet, the effect of a stronger capital base on economic growth remains largely unsettled. We argue that better capitalized banks improve financial stability conditions and, in dire times, they are able to sustain credit to the economy thereby containing adverse macroeconomic implications. Using various methods, we test for the presence and strength of a financial stability channel and a bank lending channel by drawing evidence from 47 advanced and developing countries over close to two decades. We find that higher capital ratios improve financial stability and help sustain bank lending, ultimately exerting a positive influence on economic activity. These effects on real GDP growth are economically significant, reaching up to 1¼ percentage points for each percentage point acceleration in capital. Our main results are robust to various sensitivity checks, supporting the conclusion that safer banking systems do not bridle economic activity.
    Keywords: Bank capital, Financial stability, Bank lending, Economic growth
    Date: 2022–10

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