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This paper compares the dynamics of the financial integration process as described by different empirical approaches. To this end, a wide range of measures accounting for several dimensions of integration is employed. In addition, we evaluate the performance of each measure by relying on an established international finance result, i.e., increasing financial integration leads to declining international portfolio diversification benefits. Using monthly equity market data for three different country groups (i.e., developed markets, emerging markets, developed plus emerging markets) and a dynamic indicator of international portfolio diversification benefits, we find that (i) all measures give rise to a very similar long-run integration pattern; (ii) the standard correlation explains variations in diversification benefits as well or better than more sophisticated measures. These Findings are robust to a battery of robustness checks.
In this study, we unpack the ESG ratings of four prominent agencies in Europe and find that (i) each single E, S, G pillar explains the overall ESG score differently,(ii) there is a low co-movement between the three E, S, G pillars and (iii) there are specific ESG Key Performance Indicators (KPIs) that are driving these ratings more than others. We argue that such discrepancies might mislead firms about their actual ESG status, potentially leading to cherry-picking areas for improvement, thus raising questions about the accuracy and effectiveness of ESG evaluations in both explaining sustainability and driving capital toward sustainable companies.
The centrality of the United States in the global financial system is taken for granted, but its response to recent political and epidemiological events has suggested that China now holds a comparable position. Using minute-by-minute data from 2012 to 2020 on the financial performance of twelve country-specific exchange-traded funds, we construct daily snapshots of the global financial network and analyze them for the centrality and connectedness of each country in our sample. We find evidence that the U.S. was central to the global financial system into 2018, but that the U.S.-China trade war of 2018–2019 diminished its centrality, and the Covid-19 outbreak of 2019–2020 increased the centrality of China. These indicators may be the first signals that the global financial system is moving from a unipolar to a bipolar world.
In the aftermath of the global financial crisis, both resolution planning, i.e. contingency planning by both regulated institutions and public authorities in order to prepare their actions in financial crisis, and concepts for structural bank reform have been identified as possible solutions to ending “Too Big To Fail” and foster market discipline among bank owners, bank managers and investors in bank debt. Both concepts thus complement the global quest for reliable procedures and tools for bank resolution that would minimise systemic implications once large and complex financial institutions have reached the stage of insolvency. Given the complex task of orchestrating swift and effective resolution actions, especially with regard to cross-border banking groups and financial conglomerates, planning ahead in good times has since been widely recognised as crucial for enhancing resolvability. At least part of the impediments to resolution will be found in organisational, financial and legal complexity that has evolved in banks and groups over time. To remove these impediments, interference with existing corporate and group structures is all but inevitable. However, in both international standard setting and at the European Union level, issues related to resolution planning (within the context of bank resolution reform) and structural banking reforms to date have been discussed rather separately. This lack of consistency is questionable, given the obvious need to reconcile both approaches in order to facilitate effective implementation and enforcement especially with regard to large, complex banking groups. Based on an analysis both of the Bank Recovery and Resolution Directive and the SRM Regulation, this paper explores how these problems could be dealt with within the context of the European Banking Union.
The creation of the Banking Union is likely to come with substantial implications for the governance of Eurozone banks. The European Central Bank, in its capacity as supervisory authority for systemically important banks, as well as the Single Resolution Board, under the EU Regulations establishing the Single Supervisory Mechanism and the Single Resolution Mechanism, have been provided with a broad mandate and corresponding powers that allow for far-reaching interference with the relevant institutions’ organisational and business decisions. Starting with an overview of the relevant powers, the present paper explores how these could – and should – be exercised against the backdrop of the fundamental policy objectives of the Banking Union. The relevant aspects directly relate to a fundamental question associated with the reallocation of the supervisory landscape, namely: Will the centralisation of supervisory powers, over time, also lead to the streamlining of business models, corporate and group structures of banks across the Eurozone?
The loan impairment rules recently introduced by IFRS 9 require banks to estimate their future credit losses by using forward-looking information. We use supervisory loan-level data from Germany to investigate how banks apply their reporting discretion and adjust their lending upon the announcement of the new rules. Our identification strategy exploits a cut-off for the level of provisions at the investment grade threshold based on banks’ internal rating of a borrower. We find that banks required to adopt the new rules assign better internal ratings to exactly the same borrowers compared to banks that do not apply IFRS 9 around this cut-off. This pattern is consistent with a strategic use of the increased reporting discretion that is inherent to rules requiring forward-looking loss estimation. At the same time, banks also reduce their lending exposure to exactly those borrowers at the highest risk of experiencing a rating downgrade below the cutoff. These loans would be associated with additional provisions in future periods, both in the intensive and extensive margin. The lending change thus mitigates some of the negative effects of increased reporting opportunism on banks’ crisis resilience. However, when these firms with internal ratings around the investment grade cut-off obtain less external funding through banks, the introduction of IFRS 9 will likely also be associated with real economic effects
European banks have substantial investments in assets that are
measured without directly observable market prices (mark-to-
model). Financial disclosures of these value estimates lack
standardization and are hard to compare across banks. These
comparability concerns are concentrated in large European
banks that extensively rely on level 3 estimates with the most
unobservable inputs. Although the relevant balance sheet
positions only represent a small fraction of these large banks’
total assets (2.9%), their value equals a significant fraction of core
equity tier 1 (48.9%). Incorrect valuations thus have a potential to
impact financial stability. 85% of these bank assets are under
direct ECB supervision. Prudential regulation requires value
adjustments that are apt to shield capital against valuation risk.
Yet, stringent enforcement is critical for achieving this objective.
This document was provided by the Economic Governance
Support Unit at the request of the ECON Committee.
Accounting for financial stability: Bank disclosure and loss recognition in the financial crisis
(2020)
This paper examines banks’ disclosures and loss recognition in the financial crisis and identifies several core issues for the link between accounting and financial stability. Our analysis suggests that, going into the financial crisis, banks’ disclosures about relevant risk exposures were relatively sparse. Such disclosures came later after major concerns about banks’ exposures had arisen in markets. Similarly, the recognition of loan losses was relatively slow and delayed relative to prevailing market expectations. Among the possible explanations for this evidence, our analysis suggests that banks’ reporting incentives played a key role, which has important implications for bank supervision and the new expected loss model for loan accounting. We also provide evidence that shielding regulatory capital from accounting losses through prudential filters can dampen banks’ incentives for corrective actions. Overall, our analysis reveals several important challenges if accounting and financial reporting are to contribute to financial stability.
The purpose of the data presented in this article is to use it in ex post estimations of interest rate decisions by the European Central Bank (ECB), as it is done by Bletzinger and Wieland (2017) [1]. The data is of quarterly frequency from 1999 Q1 until 2013 Q2 and consists of the ECB's policy rate, inflation rate, real output growth and potential output growth in the euro area. To account for forward-looking decision making in the interest rate rule, the data consists of expectations about future inflation and output dynamics. While potential output is constructed based on data from the European Commission's annual macro-economic database, inflation and real output growth are taken from two different sources both provided by the ECB: the Survey of Professional Forecasters and projections made by ECB staff. Careful attention was given to the publication date of the collected data to ensure a real-time dataset only consisting of information which was available to the decision makers at the time of the decision.
This paper provides an overview of how to use "big data" for economic research. We investigate the performance and ease of use of different Spark applications running on a distributed file system to enable the handling and analysis of data sets which were previously not usable due to their size. More specifically, we explain how to use Spark to (i) explore big data sets which exceed retail grade computers memory size and (ii) run typical econometric tasks including microeconometric, panel data and time series regression models which are prohibitively expensive to evaluate on stand-alone machines. By bridging the gap between the abstract concept of Spark and ready-to-use examples which can easily be altered to suite the researchers need, we provide economists and social scientists more generally with the theory and practice to handle the ever growing datasets available. The ease of reproducing the examples in this paper makes this guide a useful reference for researchers with a limited background in data handling and distributed computing.