Credit risk variable selection

Table 1 below presents and comments the main credit risk variables used in the research on the Romanian banking system (at different levels of aggregation), including studies on credit risk determinants. The table is structured similar to Ferrari et al. (2010) presentation and comments on advantages /disadvantages are based generally on Ferrari et al. 2011, Schechtman and Gaglianone (2010) for NPL ratio and stock variable disadvantages and Misina et al. (2006) for bankruptcy rates.

While a lagged indicator versus PD, NPL ratio seem to hold an important role in assessment of credit risk research, as its definition and treatment of its secondary components is similar across countries (Jakubik and Reininger, 2013). Given its direct impact on banks’ profitability, NPL ratio is part of the macro-prudential financial soundness indicators of I.M.F. and a focus of Romanian central bank’s stability reports (N.B.R. 2013a).

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Table 1 Credit risk variables available for Romanian banking system

Credit risk variable and type

Definition Content Advantages Disadvantages Studies

I.BANK ACCOUNTING DATA VARIABLES Non-performing loan

(NPL) ratio

Type: stock variable

Ratio of NPLs to total loans. As per legal definition (N.B.R. 2013a), NPLs are loans overdue for more than 90 days and/or for which legal proceedings have been initiated (forced sale procedure or

bankruptcy/insolvency procedure).

PD Broadly used in credit risk and stress testing studies. Definition harmonized on bank level by the regulator in Romania (N.B.R. 2013a). Publicly available.

Being a stock measure, it captures performance of loans granted in different periods of time and thus is affected by changes in credit portfolio not necessarily related to credit risk (total granted volumes, maturities, collateral treatment).

Affected by write-offs.

Moinescu (2012) – system level NPL determinants study

Loan loss provision ratio (LLP) ratio. Known also as credit risk ratio.

Type: stock variable 16

LLPs to total loans ratio. Banks can register new provision following an increase in expected loss, potentially before actual defaults.

PD, LGD Harmonized definition by regulations. Usually publicly available at aggregated level.

Although the definition is harmonized, banks have some discretion regarding provisioning and thus LLP ratios across banks could lack comparability.

Similar disadvantages to NPL – stock variable; also affected by write-offs.

Vogiazas and Nikolaidou (2011) and Nikolaidou and Vogiazas (2012) – system level LLP determinants.

II. DEFAULT DATA Default rate

Type: flow variable (ratio of numbers) or stock variable (volume ratio of defaulted loans in total loans)

Ratio of number of default borrowers to total number of borrowers.

Sometimes expressed as volumes ratio. Following Basel II framework a debtor is considered to be in default in case of more than 90 days overdue on any material credit obligation or when the bank considers that the borrower is unlikely to repay the credit in full.

PD (LGD when measured in volumes)

Harmonized definition. Sometimes loans and no of borrowers with overdue amounts of over 90 days are available in central

banks’ credit register.

Usually not publicly available.

Usual disadvantages as described above when used as stock variable

Chiriacescu (2010) and Chiriacesu et al (2012) – flow variable per sector of activity (separately for households).

Moinescu and Codirlasu (2012a) – sectoral stock volume ratio for companies as proxy for sectoral NPL.

Bankruptcy rate Type: flow variable

Ratio of numbers of companies filing for bankruptcy (entering insolvency proceedings). PD Harmonized legal definition. Usually publicly available at sectoral disaggregated levels.

Broadly used in stress testing studies.

Usually available only for companies. Complicated net effect on actual PD in banking system. Banks’ credit portfolios may not reflect entire sector distribution (credit selection criteria lead to rejection of likely to go bankrupt companies), but, on the other hand, credit default is not always followed or preceded by bankruptcy.

Trenca and Benyovszky (2008)

16

As Ferrari et al (2011) note LLP ratio can be available as flow variable (new provisioning to a measure of stock of total loans), but this is not the case for Romanian banking system.

44 | P a g e The studies mentioned above had access to data which are not currently publicly available (e.g. Moinescu and Codirlasu, 2012a; Chiriacescu, 2010) or the series have been discontinued (industry- specific bankruptcy rates used by Trenca and Benyovszky, 2008).

Still, due to data restriction, Moinescu and Codirlasu (2012a) and Chiriacescu (2010) actually use a proxy of the formal default rate as their data series are based on data of loans and number of borrowers, respectively, that register overdue amounts of more than 90 days as reported by National Bank of Romania’s credit register and not on actual defaulted borrowers / loans formally declared by banks. Their series however exclude only borrowers / loans for which the bank consider that repayment is unlikely (potentially in advance of any 90 days arrears), which should constituted only exception cases.

While Moinescu and Codirlasu (2012a) and Chiriacescu (2010) used sectoral disaggregated data, Romanian central banks’ credit register has publicly available data only for volumes of overdue credit obligations (overdue principal, without interest and other penalties) disaggregated for household and companies17. Data on number of borrowers with overdue amounts of more than 90 days are not available (only on number of total borrowers and number of borrowers registering delays of any number of days).

Table 2 below presents the credit risk variables data available for the Romanian banking system, their sample period and level of disaggregation:

17

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Table 2 Credit risk variable data availability for Romanian banking system

Credit risk variable Level of disaggregation Available sample period (continuous series)

Data frequency Source

NPL ratio System level March 2008 – present Quarterly N.B.R. data base and financial stability reports Disaggregated NPL ratio Companies and household level February 2009 – August 2013 for companies Monthly N.B.R. financial stability reports September 2008 – June 2013 for households Quarterly N.B.R. financial stability reports LLP ratio (credit risk

ratio)

Exclusively available at system level

December 2007 – present

Quarterly N.B.R. data base Default rates Companies and

household level (proxied by overdue of more than 90 day)

February 2005 – present

Monthly N.B.R. data base (credit register)

Bankruptcy rate Main object of activity (usually they are aggregated for main economic sectors of activities)

March 2010 – present Monthly National Trade Register Office database

Given the data availability restriction noted above, this study will focus on default rates (in terms of volumes) separately for corporate and household loans. The National Bank of Romania uses the same main disaggregation level for its stress testing procedure (N.B.R. 2013a; Melecki and Podpiera, 2010), of course, complemented by more detailed granular disaggregation based on data that are not publicly available.

Unexpected loss cannot be directly computed based on volume-based default rates simulations (as usually done in such cases as discussed in the literature review); nevertheless, useful macro stress testing can be performed on default rate values directly.

This study will use the full sample available, with quarterly frequency, i.e. 2005 Q1 to 2013 Q3 period (35 observations).

46 | P a g e The available samples have the advantage of capturing different business and credit cycle, in a balanced manner: the 2005-2008 upward period (high GDP and credit growth rates, following Romania’s accession to N.A.T.O. and E.U.), the late-2008 – 2009 shock generating important GDP downturns and NPL build up, as well as the recent slight macroeconomic improvement (N.B.R. 2013a).

This study complements thus the independent macro stress testing research for credit risk in Romanian banking system since Trenca and Benyovszky (2008) use only pre-crisis data (2002-2007), and although Chiriacescu (2010) and Chiriacescu et al (2012) include also the 2008-2009 macroeconomic shock effects (both studies use 2006-2010 data series), as Chiriacescu (2010) explain, the model could still be biased towards pre-2009 macroeconomic conditions.