COVID 19 IMPLICATIONS ON CREDIT LOSS
PROVISIONING RULES UNDER IFRS 9
PRO-CYCLICALITY CONCERNS

Djelil Bilal, Saidj Faiz

► To cite this version:

Djelil Bilal, Saidj Faiz. COVID 19 IMPLICATIONS ON CREDIT LOSS PROVISIONING RULES UNDER IFRS 9 PRO-CYCLICALITY CONCERNS. International Journal of Economic Performance - , In press, 07 (01). hal-04678411

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COVID 19 IMPLICATIONS ON CREDIT LOSS PROVISIONING
RULES UNDER IFRS 9: PRO-CYCICALITY CONCERNS

<table><tr><td>DJELIL Bilal* ID</td><td>SAIDJ Faiz ID</td></tr><tr><td>b.djelil@univ-boumerdes.dz</td><td>f.saidj@univ-boumerdes.dz</td></tr><tr><td>Algeria's future economy outside<br>hydrocarbons Research laboratory (Algeria)<br>University of Boumerdes(Algeria)</td><td>Algeria's economic and human development<br>laboratory (Algeria)<br>University of Boumerdes(Algeria)</td></tr></table>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

Submitted:14/02/2024

Accepted:28/04/2024

Published:30/06/2024

**Abstract:**

The objective of this paper is to analyse the procyclicality behavior of Expected Credit Loss (ECL) model introduced by IFRS 9, during the economic downturn due to the Covid 19 pandemic. A regression analysis was conducted on a dataset gathered from euro era in order to investigate the hypothesis suggesting that the new model does not exhibit a procyclical behavior. Our findings indicate that, despite the IASB's expectation that the ECL model would have a countercyclical impact, it still demonstrates procyclical behavior.

**Key words:** IFRS9, ECL model, Procyclicality, Covid 19 pandemic.

**JEL Classification Codes:** E32, G21, M41.

<footnote>* Corresponding author</footnote>
**INTRODUCTION :**

Since the global subprime financial crisis in 2008, many studies have been focusing on the stability of the financial system and its procyclicality behavior which might undesirably impact the stability of the whole financial system. In retrospect to the subprime financial crisis in 2008, the incurred loss model introduced by IAS 39 was considered as been a main factor contributing in the global financial crisis by its procyclicality behavior.

In response to this critique, the IASB has set a new collection of rules for accounting for credit loss provisioning in order to eliminate procyclicality. A new international accounting standard, IFRS 9 Financial instruments, is one of them.

The outbreak of COVID 19 pandemic has been the first economic crisis affecting the credit impairment model ECL introduced by IFRS 9, and renewed concerns about Procyclicality and the effectiveness of new approach of credit impairment ECL in limiting procyclicality effects due to the economic downturn.

**General problematic**

The general problematic of this paper can be formulated as follows:

Does IFRS 9 behave procyclically in the economic downturn induced by COVID 19 pandemic?

**Hypothesis**

According to IASB, the new ECL model would have a countercyclical impact. Thus, IFRS 9 does not have a procyclical impact.

**Objectives**

The aim of this study is to examine the effectiveness of the new impairment methodology ECL model in provisioning for credit loss, and find out whether the new forward-looking provisioning rules yield a countercyclical behavior as intended, as well as making a
contribution to the dialogue concerning the effects of the IFRS 9 standard on the stability
of financial system.

**Importance of the study**

The significance of this study lies in the role played by accounting rules for credit loss provisioning in maintaining financial stability. It also drives its importance from the subject of the study as being a topic of the hour in accounting.

**Methodology applied**

For the purpose of this paper, an empirical study based on a regression model will be
performed on dataset gathered from the euro era. we will look at the interaction between
the economic sector which is represented by the Gross Domestic Product (GDP), and the
financial sector which is represented by credit impairment volume.

# 1. LITERATURE REVIEW

This part of the study provides insights about the disease and its consequences on the
overall economic situation, and highlights the conceptual framework of IFRS 9 financial
instruments and its new impairment methodology ECL. It also explains procyclicality
and the role of accounting rules in the financial system.

1.1 COVID 19 Pandemic

Coronaviruses are a cluster of viruses that pertain to the lineage of Coronaviridae, effectively infecting both animal and human hosts. Human coronaviruses can cause mild illness resembling to a common cold, whereas others cause more grave disease. In late 2019, A novel variant of the coronavirus that has not been previously identified in human population emerged in province of Wuhan in China (World Health Organization, 2020, p. 1). This novel Coronavirus was later designated as corona virus disease 2019 (COVID-19) by World Health Organization (WHO).
The new infectious disease has spread rapidly with a very high death-rate, the number of people who have been infected by the virus around the world has reached a point where it become exceedingly challenging to track. (Pete Kinross et al, 2020, p. 4). Due to the rapid dissemination of the new virus across the globe, on the 11th of March 2020, the Director-General of the WHO formally proclaimed the Covid-19 virus as a global pandemic. (World Health Organization, 2020). Following a span exceeding three years from its initial emergence, and until the time of writing of this paper, the number of confirmed cases of COVID-19 worldwide, as reported by the official statistics of the World Health Organization, has attained a significant level of 768 983 095 confirmed cases of COVID-19, including 6 953 743 deaths (World Health Organization, 2023).

1.2 Covid-19 pandemic's impact on the global economy

In Addition to the aforementioned health consequences, COVID 19 pandemic has
massively affected the global economy and financial markets. disruptions in the
transportation and services, significant reductions in income and manufacturing
industries, a rise in unemployment .... etc, are among the consequences of restriction of
activities due to lockdowns and distancing measures adopted by governments around
the world to contain the rapid spread of the virus (Anton et al, 2020, p. 3).

The COVID-19 Pandemic has caused the worst economic situation in the recent decades.
The International Monetary Fund characterized the unprecedented economic decline
resulting from the pandemic as "a crisis like no other", while identifying the strict
confinement measures as "the worst economic downturn" since the Great famous
Depression in 1929 (International Monetary Fund A, 2020).

The impact of COVID 19 on the international GDP is massive, the global recession is the deepest since 1945 (the end of World War two). In accordance with the International Monetary Fund (IMF) report released in April 2021, there was a 7 percent contraction in comparison to the earlier 3.4 percent growth projection made in October 2019. Notably,
virtually all nations surveyed by the IMF experienced negative economic growth in the
year 2020; however, the downturn was particularly accentuated in the most economically
disadvantaged regions across the globe. (International Monetary Fund, 2021).

The disease spread across the globe at lightning speed causing immense disruptions in trade of goods and services especially at the beginning of the crisis, many essential goods came to be in short supply (United Nations, 2022).

The rapid spread of COVID-19 has dramatically affected the financial markets all over
the world, the effects of the pandemic on the general economic situation and the
uncertainty associated to the pandemic have caused disruptions in many financial
markets worldwide and made these markets witness substantial changes following the
global diffusion of COVID-19 (Stefan Cristian Gherghina, 2023, p. 1). The outbreak of
the COVID-19 pandemic significantly increased the probability of a severe decline in the
stock market. Consequently, both the S&P500 and S&P Europe 350 experienced a
depreciation of over a third of their value on 23 March 2020, compared to their highest
recorded levels on 19 February 2020. Additionally, there was a notable decrease of 12%
in a single day in mid-March. The Dow Jones Industrial Average witnessed a decrease of
7.79% on 9 March 2020, followed by a further decline to 9.9% on 12 March 2020, denoting
the largest drop in US history. (Stefan Cristian Gherghina, 2023, p. 1).

1.3 Credit Loss and credit loss Provisioning

Provisioning for potential credit losses is the estimation of future losses that might occur
in the future due to risk of default. this indicates that losses from bad debts or other
credits that are probably going to be irrecoverable are anticipated. Credit losses are
accounted for as expenses on the company's financial statements (investopedia, 2023).

Within the accounting literature, there exist a variety of credit loss impairment models
including , the Fair Value model, the Expected Credit Loss model , the dynamic
provisioning, and the Incurred Loss model. (Dirk Beerbaum et al, 2015, p. 2).
Banks trade financial instruments to enable the transfer of funds between savers and
borrowers, which is a crucial function they do for the economy. Banks are required to set
aside a portion of their financial instrument's value as a provision for the expected default
when there is a probability that the borrower will default, leading to a financial
instrument's expected yield falling short of what was promised. This provision is known
as expected credit loss. On their balance sheets, banks should record the projected credit
loss at the time the financial instruments are first recognized. (Merjona Lamaj, 2022, p. 5).

1.4 IFRS 9 and The New Impairment Methodology ECL

In this section, we highlight the essential amendments brought by IFRS 9 on accounting
for financial instruments, and the new impairment methodology ECL.

1.4.1 IFRS 9

In the aftermath of the subprime crisis in 2008, fundamental weaknesses were identified
in IAS 39 Financial instruments: Recognition and Measurement, receiving substantial
criticism for its belated and incomplete recognition of impairments “Too little, too late”.
(Arndt G & Daniel R, 2021, p. 3). In order to improve policies and procedures related to
credit loss provisioning, related parties such as investors, the G20, prudential authorities,
and regulatory agencies, have advocated for decisive measures from accounting standard
setters. Responding to these calls, the International Accounting Standards Board (IASB)
and the Financial Accounting Standards Board (FASB) have collaborated to revise
accounting rules based on forward-looking information in the direction of an expected
credit loss methodology in response to these complaints. (Benjamin H et al, 2017, p. 2).

Due to the complicated requirements of IAS 39 , the standard was considered by
accountants to be complex and too difficult to understand ,the main objective of the new
standard is to simplify the requirements of IAS 39 by using an increasingly principle-
based model on purpose to reduce complexities revolving around recognition,
classification and reclassification, and a detailed methodology for recognition of impairment losses (Salim Alibhai et al, 2018, p. 613).

IFRS 9 introduces significant amendments to IAS 39, effectively superseding it in two pivotal aspects. First, it incorporates an expected credit loss approach for the calculation of credit losses. Second, it provides more robust guidelines for the classification, reclassification, and measurement of financial instruments compared to the existing framework (IFRS 9 : Financial instruments, 2022).

All financial instruments, assets and liabilities, must be valued at fair value or amortized cost depending on their classification which is in turn based on two criteria, business model and characteristics of contractual cash flows of the financial asset. Therefore, under IFRS 9, the business model and the characteristics of cash flows relating to financial instruments plays a significant part in determining the classification of the financial asset (Salim Alibhai et al, 2018, p. 626).

The three types of financial instruments that are distinguished by the new standard are debt instruments, equity instruments, and derivatives. Bonds are an example of a debt instrument, which is a contractual obligation of the issuer to repay the lender in line with the terms of the agreement and at a set maturity. An example of a derivative is an option. Derivatives are financial contracts whose value is derived from one or more underlying assets. Equity instruments comprise the final category. These contracts serve as legally acknowledged proof of an individual's ownership stake in a business. (Grant Thornton, 2017, p. 2).

The primary area of difference between IAS 39 and IFRS 9 is how financial instruments are classified as shown in (Table:01). The models' frameworks vary greatly as a result of the classification differences. (IFRS 9 : Financial instruments, 2022).
<table><caption>**Table 01:** key differences between IFRS 9 and IAS 39</caption><thead><tr><th>Key differences<br>Standards</th><th>IAS 39</th><th>IFRS 9</th></tr></thead><tbody><tr><th scope="row">Test</th><td>1. Held for trading test</td><td>2. Business model test<br>3. SPPI test</td></tr><tr><th scope="row">Categories of financial instruments</th><td>1. Available for sale<br>2. Loans and receivables<br>3. Held to maturity<br>4. Fair value option</td><td>1. Debt instruments<br>2. Derivatives<br>3. Equity instruments</td></tr><tr><th scope="row">Valuation methods</th><td>1. Amortized cost<br>2. Fair value through OCI<br>3. Fair value through profit and loss</td><td>1. Amortized cost<br>2. Fair value through OCI<br>3. Fair value through profit and loss</td></tr></tbody></table>

**Source:** own elaboration based on IAS 39 and IFRS 9

### 1.4.2 The Expected Credit Loss (ECL) Model

In July 2014, the international accounting standard board (IASB) released IFRS 9 Financial Instruments bringing with it a new credit impairment methodology Expected credit loss model (ECL). The key innovation brought by IFRS 9 is a shift from a backward incurred losses approach towards a forward-looking paradigm ECL.

ECL are “a probability-weighted estimate of credit losses”. The difference between the cash flows that the business expects to receive discounted at the initial effective interest rate and those that are due to the entity is known as a credit loss. (PwC, 2014, p. 3).

Under IFRS 9, impairment of credits is recognized in three stages as follows:

- **Stage 1:** Upon the origination or acquisition of a loan, the Expected Credit Loss (ECL) stemming from potential default within the subsequent 12 months is acknowledged, leading to the establishment of a corresponding loss allowance. In subsequent reporting periods, the 12-month ECL framework is similarly applied to existing loans with no significant escalation in credit risk since their initial recognition. Interest
revenue is then computed based on the gross carrying amount of the loan (Dirk Beerbaum et al, 2015, p. 2).

- **Stage 2:** This stage includes any financial asset with a notable rise in its credit risk since its first recognition without objective signs of impairment. For this category of financial assets, lifetime ECL are recorded, and the gross carrying amount is still used to compute interest revenue. (PwC, 2014, p. 3).

- **Stage 3:** The third stage of ECL impairment methodology contains financial assets with confirmed signs of impairment (incurred losses). The net carrying amount is used to compute interest revenue, and the lifetime ECL must be recognized (PwC, 2014, p. 3).

<table><caption>**Figure 01:** Three-stage approach in the expected credit loss</caption><thead><tr><th>STAGE 1</th><th>STAGE 2</th><th>STAGE 3</th></tr></thead><tbody><tr><td>Unchanged credit risk</td><td>Significant increase in credit risk</td><td>Credit impaired (incurred loss)</td></tr><tr><td>12-months expected credit losses</td><td>Lifetime expected credit losses</td><td>Lifetime expected credit losses</td></tr><tr><td>Interest accrued on gross carrying amount</td><td>Interest accrued on gross carrying amount</td><td>Interest accrued on net carrying amount</td></tr></tbody></table>

Source: (IASB, 2022).
According to IFRS 9, a multifactor and comprehensive approach should be used in
determining the level of credit risk deterioration. The standard offers a set of non-
exhaustive conditions, which can be summed up as follows:

- A considerable change in the internal prices;

- Other modifications to an existing financial instrument's term rate;

- Notable variations in external market indicators;

- Notable variations in the credit rating;

- An erosion in internal credit rating;

- Notable variations in the collateral's value.

The increase in the likelihood of default since the first detection is the basis to determine
if there has been a notable increase in credit risk. The new standard allows entities to
determine if the credit risk has risen significantly using a variety of methods which must
be applied consistently. (Dirk Beerbaum et al, 2015, p. 2).

According to IFRS 9, entities must provide information outlining how they assess ECL and evaluate the changes in credit risk, as well as the rationale behind their ECL calculations. Additionally, for each category of ECL, they must offer a reconciliation of the initial and final ECL amounts as well as the carrying values of the related assets in their financial statements (IFRS 9 : Financial instruments, 2022).

ECL for financial assets is a probability-weighted sum that is objectively determined by taking into account the risk of credit loss, even in cases where the probability is small.

(Rajosik Banerjee et al, 2017, p. 9).

According to definitions provided by IFRS 9, the ECL formula can be written as follows:

$$
ECL = \sum_{t=1}^{T} EAD \ t * LGD \ t * PD \ t
$$

<figure><img src="image_6.png" /></figure>
- **Exposure at default (EAD):** This is the principal amount to which the loss given default and the probability of default are applied. Based on a historical review of repayments made during the period leading up to default, a repayment rate is determined. (Rajosik Banerjee et al, 2017, p. 9).

EAD = The principal amount outstanding x (1- the estimated rate of repayment during the default period).

- **Loss Given Default (LGD):** LGD is represented by a portion of unrecovered amount of a credit when default occurs (Rajosik Banerjee et al, 2017, p. 10).

- **Probability of Default (PD):** PD is the likelihood that debtors will eventually fall behind on their payments. If an asset is in stage 1, a 12-month PD is necessary. Stage 2 requires a lifetime PD which necessitates the construction of a PD term structure. (Rajosik Banerjee et al, 2017, p. 10).

**1.5 ECL Model and Procyclicality concerns due to COVID-19 Pandemic**

Before going into details, a first question to be answered relates to the definition of procyclicality. An initial attempt would refer to the fact that some variables move together with the cycle, standing in clear opposition to countercyclical variables which move in the opposite direction. Therefore, procyclicality refers to the tendency of financial variables to fluctuate around a trend during the economic cycle (Jean-Pierre Landau, 2009, p. 1). When discussing provisions for loan losses, procyclicality usually refers to how credit loss provisioning rules lead bank lending to fluctuate in a way that mirrors changes in real economic activity. (Basel Committee on Banking Supervision, 2021, p. 8).

Given to the very recent introduction of IFRS 9 and ECL model, the first global economic crisis that has an impact on the implementation of ECL approach is the downturn associated with the COVID-19 pandemic. A further concern has been raised again regarding IFRS 9 and ECL procyclicality in the wake of the Covid-19-induced
economic downturn, in the first quarter of 2020. Forecasting an exogenous shock with
such uncertainty appears to have reinforced the pre-implementation beliefs that new
prospective ECL techniques could exacerbate procyclicality. For example, (Dirk
Beerbaum et al, 2015, p. 4) documented that the possible procyclical behavior of IFRS 9
may be produced by shifting exposures from stage 1 to stage 2, using PDs and LGDs in
projected credit loss models, and increasing stage 3 exposures during a downturn. (Harry
Huizinga et al, 2019) also found that provisioning in the euro area is procyclical by
determining the negative correlation between GDP growth and the volume of loan loss
provisions. The estimate they provided was based on projected statistics and was shown
to be more important for larger banks.

Financial authorities and accounting standard setters are both well aware of the
pandemic's possible procyclical impact on bank loan loss provisions and implications for
bank capital adequacy. In this regard, Recommendations and measures have been
announced to avoid procyclicality effects of Covid 19 pandemic. On 1 April 2020, the
Chairman of the Supervisory Board of European central bank Andre Enria recommended
big European banks and financial institutions not to use procyclical assumptions when
reporting for credit losses. (European Central Bank A, 2020) . the European Central Bank
(ECB) has also offered banks a list of capital and operating guidelines to help them
withstanding the pandemic's stressful conditions and continue funding businesses who
are having problems. (European Central Bank B, 2020).

2. DATA AND METHODOLOGY:

In this section, we will explain the selection of data used to assess the pro-cyclicality
implications of covid 19 on credit loss provisioning rules under IFRS 9, and the
methodology adopted. The dataset used for the purpose of this study covers publicly
available data about the euro era, the empirical analysis is based on data on impairment
volume from consolidated banking data obtained from ECB Data portal (European
Central Bank, 2023), and data gathered from Eurostat statistics on GDP growth in the
Euro area (Eurostat, 2023).

The data about GDP growth rate for 28 EU member countries for the period going from
2018 to 2022 were obtained from the Eurostat database (Figure 02).

<figure><img src="image_7.png" /><figcaption>**Figure 02:** Development of GDP Growth rate in EU 28 from 2018 to 2022</figcaption></figure>

Source: Eurostat database (Eurostat, 2023).

The annual data on impairment of financial assets of banks were downloaded from the
ECB Data portal for the 28 EU member countries for the same period going from 2018 to
2022 (Figure 03).
<figure><img src="image_8.png" /><figcaption>**Figure 03:** Consolidated banking Impairment In EU 28 from 2018 to 2022.</figcaption></figure>

The **Source:** European Central Bank Data portal (European Central Bank, 2023).

empirical test was performed on the comprehensive data set via a regression analysis.

Relative variables, the dependent and independent variables, were used for the
impairment volume derived from European consolidated banking data as (a dependent
variable), and the European GDP growth rate as (an independent variable).

The regression can be expressed as follows:

$$IMPit = \alpha + \beta_0 * GDPit$$

Where:

IMPit represents the dependent variable(impairment);

GDPit represents the independent variable (gross domestic product);

β0 represents the slope coefficient that measures the effect of GDP on IMP;

α represents the intercept.
The relationship between the two variables can be clearly observed in (Figure 04), The total impairment scale is represented by the vertical axis on the left, while the EU28 GDP growth rate is represented by the vertical axis on the right.

<figure><img src="image_9.png" /><figcaption>Figure 04: Development Of Impairment and GDP in EU 28 From 2018 to 2022</figcaption></figure>

Source: Own elaboration.

Based on (Figure 04), the development of variables (impairment under IFRS 9 and the GDP growth rate) indicates that there is a negative correlation between the two variables suggesting that there is a procyclical behavior resulting from the provisioning rules under IFRS 9 standard. The graphical relationship is supported by the negative value of correlation coefficient (-0,94), which represents a reasonably strong negative correlation as a result of the current economic slowdown brought on by COVID restrictions. (an obvious and notable slowdown in GDP growth for the year 2020).

### 3. RESULTS

Based on the results of the regression analysis, The coefficient's value indicates that there is a negative correlation between the GDP growth and the impairment volume. The impairment decreases by 0.94 basis points for every percent increase in GDP, and vice
versa. The regression's outcome also lines up with the variables' graphical progression
and the negative correlation coefficient.

The findings reject the hypothesis suggesting that credit loss provisioning rules under IFRS 9 standard do not have a procyclicality impact, despite the expectation of the standard-setting bodies IASB and FASB, as well as regulatory and financial authorities, who anticipated that the new expected credit loss model induced by IFRS 9 would have a countercyclical impact.

4. CONCLUSION

This paper sets out to examine the effectiveness of ECL impairment methodology in
limiting pro-cyclicality effects resulting from economic downturn induced by Covid-19
pandemic. To achieve this purpose, we first provided a literature review of the study by
giving insights about the pandemic and its consequences on the overall economy, it has
been evidence that the Pandemic has caused the worst economic situation in the recent
decades which was described as "a crisis like no other", and that the Great Lock-down
as the worst economic downturn since the Depression in 1929. We also highlighted the
conceptual framework of IFRS 9 financial instruments and the new impairment
methodology ECL which is based on e forward-looking information. Finally, we gave a
theoretical review of pro-cyclicality and the interaction between the economic cycle and
credit cycle and the importance of credit loss provisioning in such a matter.

Secondly, in order to test the hypothesis suggesting that the new provisioning rules
under IFRS 9 standard, do not have a procyclicality impact, a regression analysis was
performed on the 28 EU member countries, with Consolidated banking Impairment from
2018 to 2022 as a dependent variable, and Development of GDP Growth for the same
period as an independent variable.

The results indicate that there is a negative relationship between the two variables
supported by a negative coefficient, a modest growth in the Gross Domestic Product
(GDP) results in a decrease of 0.94 basis points in the level of impairment, and conversely.
Hence, it is possible to deduce that our initial hypothesis was rejected based on statistical
evidence, and the outcomes of this research provide support for the prevailing
evaluations on the procyclical consequences of the International Financial Reporting
Standard (IFRS) 9. However, these findings are contradictory to the previously held belief
in the countercyclical impact of this new accounting standard.

5.RECOMMENDATION

Our suggestions would be to conduct a more thorough examination of the requirements
of IFRS 9. This examination could provide clarity on whether there are any potential
factors that could make loan loss provisions under ECL model to exhibit procyclical
behavior. In such a scenario, our policy recommendation would be to release regulatory
guidance documents that can help mitigating the procyclical behavior of IFRS 9.
Additionally, it would be beneficial to carry out further research to determine whether
these findings are applicable in other geographical regions.

Referrals and references:

- Anton & al, (2020), Economic Consequences of the COVID-19 Outbreak: the Need for Epidemic Preparedness. *Front Public Health, Volume 8*. doi:https://doi.org/10.3389/fpubh.2020.00241

- Arndt G & Daniel R. (2021). Does IFRS 9 increase financial stability ?,Retrieved from : SSRN https://ssrn.com/abstract=3282509

- Basel Committee on Banking Supervision (2021), *The procyclicality of loan loss provisions:a literature review*. Bank for International Settlements. Retrieved from https://www.bis.org/bcbs/

- Benjamin H et al. (2017). The new era of expected credit loss provisioning. *BIS Quarterly Review* (March 2023). Retrieved from https://www.bis.org/publ/qtrpdf/r_qt1703f.htm
- Dirk Beerbaum et al. (2015). Credit Risk according to IFRS 9: Significant increase inCredit Risk and implications for Financial Institutions. Risk Management Journal. doi:https://dx.doi.org/10.2139/ssrn.2654120

- European Central Bank. (2023). ECB Data Portal Consolidated banking Data. Retrieved from https://data.ecb.europa.eu/data/data-categories/supervisory-and-prudential-statistics/consolidated-banking-data/consolidated-banking-data

- European Central Bank A. (2020, April). Focus on ECB supervisory measures in reaction to the coronavirus. Retrieved from European Central Bank: https://www.bankingsupervision.europa.eu/press/publications/html/ssm.faq_ECB_supervisory_measures_in_reaction_to_the_coronavirusa631697a4.en.html

- European Central Bank B. (2020). *Supervisory review 2020*. Retrieved from European Central Bank, https://www.bankingsupervision.europa.eu/banking/srep/2021/html/ssm.srep202107_outcomesrepitriskquestionnaire.en.html

- Eurostat. (2023, September). Data Browser GDP Growth rate. Retrieved from https://ec.europa.eu/eurostat/datab browser

- Grant Thornton. (2017). *IFRS 9:Financial instruments*. Grant Thornton. Retrieved 8 15, 2023, from www.gt.nl

- Harry Huizinga et al. (2019). *The procyclicality of banking: evidence from the euro area*. Retrieved from EUROPEAN CENTRAL BANK: www.ecb.europa.eu

- IASB. (2022). IFRS 9 : Financial instruments. Retrieved from http://www.ifrs.org

- IFRS Foundation. (2021). IFRS Foundation.

- International Monetary Fund. (2021, April). *World economic outlook: Recovery during a pandemic*. Retrieved from https://www.imf.org/en/Publications/GFSR/Issues/2021/04/06/globalfinancial-stability-report-april-2021

<figure><img src="image_10.png" /></figure>
- International Monetary Fund A. (2020, December). Retrieved from World Economic Outlook: https://www.imf.org/en/Publications/WEO/Issues/2020/09/30/ worldeconomic-outlook-october-2020

- investopedia. (2023). Risk Management. Retrieved from invesotpedia: https://www.investopedia.com/terms/p/provision-for-credit-losses.asp

- Jean-Pierre Landau. (2009). Procyclicality – what it means and what could be done. *conference on Procyclicality and the Role of Financial Regulation*. Madrid 04 May 2009 Spain.

- Merjona Lamaj. (2022). The Effect of Covid-19 on Loan Loss Provisions and Earnings Management of European Banks. PHD thesis Wirtschaftsuniversität. Wien, Austria.

- Organization, W. H. (2020). CORONAVIRUS DISEASE (COVID-19) OUTBREAK: RIGHTS, ROLES AND RESPONSIBILITIES. World Health Organization.

- Pete Kinross et al. (2020). Rapidly increasing cumulative incidence of coronavirus disease (COVID-19) in the European Union. *Euro Surveill, Volume 25*(Issue 11). Retrieved from www.eurosurveillance.org

- PwC. (2014). *IFRS 9: Expected credit losses*. PwC. Retrieved 08 15, 2023, from inform.pwc.com

- Rajosik Banerjee et al. (2017). *Demystifying Expected Credit Loss (ECL)*. KPMG. Retrieved from KPMG.com/In

- Salim Alibhai et al. (2018). *Interpretation and application of IFRSs standards*. Cornwall, UK: John Wiley & Sons.

- Stefan Cristian Gherghina. (2023). The Impact of COVID-19 on Financial Markets and the Real Economy. *MDPI Journals Awarded Impact Factor, Volume 11*.

- United Nations. (2022). The Impact of the COVID-19 Pandemic on Trade and Development: Lessons Learned. UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT. Geneva 2022: United Nations. Retrieved from https://unctad.org/system/files/official-document/osg2022d1_en.pdf

<figure><img src="image_11.png" /></figure>
- World Health Organization. (2020, MARch 11). CORONAVIRUS DISEASE (COVID-19) OUTBREAK: RIGHTS, ROLES AND RESPONSIBILITIESOF HEALTH WORKERS, INCLUDING KEY CONSIDERATIONS FOR OCCUPATIONAL SAFETYAND HEALTH.
  World Health Organization. Retrieved from https://www.who.int/:
  https://www.who.int/docs/default-source/coronaviruse/who-rights-roles-respon-hw-covid-19.pdf

- World Health Organization. (2023). WHO Coronavirus (COVID-19) Dashboard. Retrieved from https://covid19.who.int/: https://covid19.who.int/