Volatility Modeling and Dependence Structure of ESG and Conventional Investments

The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the environment by investing in environmentally friendly companies. Examples of institutions that are paying attention to ESG issues are banks, which are increasingly including products that support sustainability goals in their offers. This analysis could be also important for policymakers. The European Banking Authority (EBA) has admitted that ESG factors can contribute to risk. Therefore, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail-dependence coefficients from the copula-based approach are applied. The analysis period covered 2007 until 2019. The period of the COVID-19 pandemic has not been analyzed due to the relatively short time series regarding data requirements from models’ perspective. Results of the research confirm the higher dependence of extreme values in the crisis period (e.g., tail-dependence values in 2009–2014 range from 0.4820/0.4933 to 0.7039/0.6083, and from 0.5002/0.5369 to 0.7296/0.6623), and low dependence of extreme values in stabilization periods (e.g., tail-dependence values in 2017–2019 range from 0.1650 until 0.6283/0.4832, and from 0.1357 until 0.6586/0.5002). Diversification benefits vary in time, and there is a need to separately analyze crisis and stabilization periods.

Measuring Spatial Distribution in the Banking System in Hungary

Over the past decade, due in part to the global economic crisis, a significant part of the bank branches have been closed in the European banking system, but in Hungary this proportion has been significantly higher than the European average. Therefore, the aim of the present study is to explore what aspects of commercial banks are taken into account when deciding where to be present within bank branches. This will also reveal the spatial dimension of public access to financial services. The present study seeks to answer the question of which socio-economic factors and in what form they affect the spatial structure of bank branches. The settlement-level examination can also provide additional indication of which settlements may be affected by further bank branch closures. Linear regression based on Ordinary Least Squares (OLS) parameter estimation was used to explore the factors influencing the location of bank branches. In addition, the possible clustering of bank branches was observed, i.e., whether spatial autocorrelation was present at certain stages of the analysis. Geographically Weighted Regression (GWR) was also estimated in the present study. Based on the results of the research, the resident population, the proportion of enterprises per capita, the average income, the number of neighbouring bank branches and the type of settlement all proved to be significant factors that may encourage decision-makers to establish a bank branch.

New Definition of Default—Recalibration of Credit Risk Models Using Bayesian Approach

After the financial crisis, the European Banking Authority (EBA) has established tighter standards around the definition of default (Capital Requirements Regulation CRR Article 178, EBA/GL/2017/16) to increase the degree of comparability and consistency in credit risk measurement and capital frameworks across banks and financial institutions. Requirements of the new definition of default (DoD) concern how banks recognize credit defaults for prudential purposes and include quantitative impact analysis and new rules of materiality. In this approach, the number and timing of defaults affect the validity of currently used risk models and processes. The recommendation presented in this paper is to address current gaps by considering a Bayesian approach for PD recalibration based on insights derived from both simulated and empirical data (e.g., a priori and a posteriori distributions). A Bayesian approach was used in two steps: to calculate the Long Run Average (LRA) on both simulated and empirical data and for the final model calibration to the posterior LRA. The Bayesian approach result for the PD LRA was slightly lower than the one calculated based on classical logistic regression. It also decreased for the historically observed LRA that included the most recent empirical data. The Bayesian methodology was used to make the LRA more objective, but it also helps to better align the LRA not only with the empirical data but also with the most recent ones.

MACROPRUDENTIAL SUPERVISION AND AGENTS’ INFORMATION: WHAT STRESS TESTS REALLY TELL THE MARKETS

Central bank’s macroprudential supervisory activities have to fulfill three distinct tasks: (i) assessing the banking system’s vulnerability to exogenous adverse turbulence, (ii) evaluating the risk of systemic crisis originating from idiosyncratic shocks, and (iii) measuring financial market’s sensitivity to policy stimuli. Given that macroprudential stress tests are the centerpiece of this policy approach, it is important to establish whether they are up to the task. We study how the 2011–2018 European Banking Authority stress tests affected market risk perception and show that they provided agents with valuable information on the policy stances and the vulnerabilities of the banking system, carrying out the above tasks successfully, especially the second and third tasks.

Common Ownership Patterns in the European Banking Sector—The Impact of the Financial Crisis1

We provide a description of ownership patterns in the top 25 European banks for the period 2003–2015, where we especially focus on the global financial crisis. Investment managers, such as Blackrock, are dominant in terms of number of blockholdings in different banks, maintaining fairly stable “common ownership” networks throughout our sample. However, the financial crisis led to capital injections by governments in several banks in trouble, which in turn led to a jump in holdings by governments, which typically are “non-common owners” (i.e., they hold only shares in only one bank). This jump translated into these investors temporarily being the top investor with a large share, and non-common owners being the majority among large shareholders. A brief comparison with US banks uncovers large ownership differences between the European and US banking sectors. We briefly discuss what these ownership patterns might imply for competition, stability and performance in the banking industry.

Modeling Non-Maturing Demand Deposits: A Proposed Methodology to Determining the Idiosyncratic Confidence Level Used for Separating Stable Deposit Volumes From Volatile Deposit Volumes

This paper aims to develop a methodology for the estimation of the idiosyncratic confidence level inherent within the process of determining the threshold of separation between volatile and stable deposit volumes. The idiosyncratic confidence level must be reflective of the institution’s specific risk preferences and liquidity risk management policies as anchored into the Principle 9 of the European Banking Authority and Basel Committee for Banking Supervision recommendations. We illustrate the proposed methodology by including liquidity constraints from the Basel III regulatory recommendations introduced in 2013. Furthermore, we point to other ancillary applications of such procedures in the financial risk management practice.