Mid-corporate rating model
The “Mittelstandsrating” model aims to provide ratings for exposure to the UCB AG category of companies headquartered in Germany with turnover of €5-500 million. The target has been amended in April 2011 (previously the lower turnover boundary was €3 million) as a result of the “ONE4C” Group’s initiative aimed of getting more focus on the requirements of the
customers and to be closer on the regional markets.
The “Mittelstandsrating” model version currently in use was developed with the aim both simplifying the previous model (the number of models for the automatic assessment of financial statements has been reduced from 12 to 4) and to implementing the improvements suggested during the previous validation stage.
The model is made up of two components: a quantitative and qualitative module. The score resulting from the analysis of financial statements results in the partial rating for operating conditions. The qualitative model instead provides the partial rating for the company’s situation. The final rating is created from a combination of the two partial ratings.
The quantitative module is made up of 4 statistical sub-modules called “Maschinelle Analyse von Jahresabschlüssen”
(automated financial statement analyses) or MAJA. The area of application of each of these sub-modules is dependent upon the company’s industry (Production, Trade, Construction, Services).
In general, the risk factors included in the quantitative module (which were selected using a process including statistical analyses and discussions with experts) cover the following areas of analysis:
• asset structure;
• financial situation;
• growth in production/margins.
The qualitative module covers areas of analysis concerning:
• financial conditions;
• management qualification;
• planning and controlling;
• industry/market/products;
• special risk;
• industry sector rating.
In case of worsening conditions of the debtor (warning signals) already included in a specific section of the qualitative module, the rating is automatically adjusted.
Finally, the final rating can be adjusted manually (overridden) if the additional information indicates that the calculated rating is not appropriate. This practice is subject to specific restrictions and constraints and is closely monitored by the internal validation unit.
The model is also subject to ordinary calibration activities over the time.
The internal validation unit checked the design of the model, the reliability (performance and stability) of its various modules (the quantitative module with its related sub-modules, and the qualitative module) and its calibration resulting in a favorable opinion: the model performances are above internal thresholds and the average PD resulted in line with the observed default rates. The positive assessment of the model was confirmed by the HC revalidation function as well.
This rating model has been adopted also by UniCredit Bank Luxembourg S.A. It was authorized also in UniCredit Leasing GmBh and some of its subsidiaries.
Foreign SME Rating model
Foreign SME rating system is aimed at the assessment of foreign companies or foreign consolidation groups. The rating is assigned to these counterparties based on an external country specific quantitative component, which is integrated with an internally developed qualitative module leveraging on the correspondent module defined for German Mid Corporate segment.
In its first version the Foreign SME rating model has been applied for the 22 countries for which specific RiskCalc models for the analysis of financial statements had been available at the time of development, and for further 45 countries for which Moody’s suggested the use of the models as so-called proxy models. Meanwhile Moody's has developed additional RiskCalc models. Therefore, in December 2011 the application of the model was extended to all countries worldwide. After the use-test phase and based on the outcomes of regulatory inspection, this extension was approved by the Regulator in June 2013.
Also in this case, the validation and revalidation activities let the Group to certify the maintenance of the compliance of this rating model to the regulatory requirements, taking into consideration the application perimeters’ modest sizes that is impeding an internal estimation of the financial modules; on the calibration side the model estimates have been in line with the realized defaults and they have been updated on the enlarged perimeter. A part from this the scope extension has had no significant impact neither on the model nor on the relevant process.
The FSME rating model is applied also in UniCredit Bank Luxembourg SA and is part of the extension application which was submitted to the regulators in 2012.
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These clients are evaluated through models built combining three basic modules:
• a qualitative module that aims to assess the quality and reliability of management, the abilities of the management team, the quality of organizational management and the bank's experience in managing relationships with the company;
• a qualitative module that aims to assess the asset/project to be financed or already financed (by the bank or other lender), including the quality and implicit risk of the portfolio of the company’s properties/projects, its planning capabilities (based on past experience) and cash flows planned/projected in future years;
• a quantitative financial module based on the company’s financial statements supplemented with a qualitative assessment of the quality, reliability and completeness of the financial statements.
The three models (Real estate developers, RE Investors with or without financial statements) all use the same sub-modules.
The main difference is the weighting used to combine the partial scores into the overall score.
During 2011 steps were taken to the extension of the rating system CREF to segment of "Building Societies" and "Real Estate Funds" previously treated with a standardized method. The extension of the application perimeter, was carried out using the model for the segment "RE Investors”, with minor adjustments based on experts judgments to take into account the specific characteristic of the segment. In consequence of the positive outcomes of the regulatory audit, this extensions was formally approval of in June 2013.
Finally, during 2012 the CREF system underwent two further immaterial modifications of the application perimeter to Foreign Reporting Investors and Developers. The regulatory audit of the extensions took place in November 2012 with a positive assessment by local regulators. The regulatory use of the extensions will produce effects after formal approval by the regulators.
A local validation of the model, including the above mentioned extensions, has been finalized in October 2012 and confirmed the suitability of the model for the portfolio including the extensions with respect to rank ordering performance and calibration.
The CREF rating model is applied also in UniCredit Bank Luxembourg SA.
Acquisition and Leveraged Finance transactions rating model (ALF)
The “Acquisition and Leveraged Finance" (ALF) model is used for the assessment of projects to finance/refinance corporate acquisition transactions, in which additional bank liabilities are added to the normal operating debt of the company acquired in order to finance the acquisition.
The debt resulting from the acquisition is repaid out of the future cash flow of the company acquired, and, in certain cases (i.e., acquisitions that involve strategic investors), out of the cash flows of the acquiring company.
Acquisition transactions and their corporate and tax implications (often involving several jurisdictions) demand specific expertise during the audit phase, and require:
• appropriate risk-return relationships in addition to a loan structure based on a realistic cash flow simulation model;
• the adjustment of the acquired company’s financial and debt repayment structure to future cash flows;
• the combined use of highly differentiated borrowing tools (senior debt, junior debt, mezzanine debt, etc.).
In terms of procedural aspects, the "ALF rating" is essentially a financial rating that calculates the acquired company’s probability of default based on equity and financial ratios taken from the provisional financial statements and income statement. There is no qualitative module since in the preparation of the provisional financial statements, a large amount of qualitative information based on experts’ opinions is already implicitly taken into consideration. The provisional financial statements are prepared through models that simulate future cash flows (INCAS, international financial model).
In this case, manual adjustments (overrides) are also allowed with respect to individual financial ratios and the final rating, and these adjustments must be approved by the responsible units and must be closely monitored by the internal validation unit.
The validation unit performed qualitative and quantitative analyses in 2012 which confirmed the model’s reliability.
Basically, the LGD calculation for portfolio ALF in UCB AG follows the same calculation approach as the general LGD model for Retail / Corporates. Based on the specialties of the portfolio, the treatment of cash flows out of collaterals is different, i.e.
collaterals are not explicitly valued in the risk calculation but the cash flows out of collaterals are included in the LGD calculation. A validation of the LGD was done in 2012. As a result, the LGD for senior tranches was adjusted. The new values are used since January 2013 in the risk calculation.
The ALF rating model is applied in UniCredit Bank Luxembourg SA.
Income Producing Real Estate (IPRE) rating model
The IPRE rating model provides an assessment of a particular category of specialized loan related to cash-flow-based real estate transactions in which the bank has direct access to the cash flows produced in the transaction.
The UCB AG IPRE rating model is a transaction based rating model that assigns a PD to a transaction - and not to the corporate customer or fund who initiates and structures the transaction. The model is applied to counterparties/transactions which fulfill a number of strictly defined criteria.
The core of the UCB AG IPRE model is a cash flow simulation (module 1). The main idea behind this approach is that an IPRE transaction defaults if the cash flow (after costs) that is realized in the transaction from rental income or sale of the property will not cover the debt service to repay the loan - or if the value of the real estate property falls below the face value of the loan.
This in turn is assumed to happen if the relevant macroeconomic parameters (such as the rent index and sales price index) are developing negatively. The simulation approach is chosen to explore a diversity of possible scenarios of the development of the relevant macroeconomic parameters probability weighted and thus to understand the likeliness of the transaction to default.
In order to capture additional aspects the result of the simulation can be altered based on qualitative questions (module 2 and 3):
• specific object characteristics module, to take into account the characteristics of the specific objects held by the SPV
• soft facts module, for the qualitative assessment (this component is enriched with specific guidelines for the correct interpretation of the questions, as well as with detailed explanations of the factors contained in the module).
The results of each simulation are combined to produce the one-year probability of default (PD) then calibrated on long-term observed default rates, and the other risk parameters (Market Value), that are subsequently adjusted to take into account the characteristics of the specific objects (position and quality of the building) and the qualitative assessment.
Based on the local validation released in 2012 the rating model resulted as overall compliant, even though minor areas of improvement which had already been identified in the local validation 2011 need to be implemented. The validation, shows generally satisfying results with reference to predictive power, stability and calibration, but results have to be interpreted in the light of a still low number of cases in the validation sample, expected however to increase in next few years.
The LGD calculation for the IPRE portfolio in UCB AG follows the same calculation approach as the general LGD model for Retail / Corporates. The validation of LGD in 2012 shows stable results and therefore no need for parameter adjustments.
The IPRE rating model is applied also in UniCredit Bank Luxembourg SA and is part of the extension application of IRB model which was submitted to the regulators in 2012.
Global Shipping rating model for Ship financing (GLOS)
The principal characteristic of “ship financing” is the granting of loans for the acquisition of ships, principally secured by a mortgage on the financed asset. Ship finance is an asset based credit business which completely depends on the cash flow generating capabilities of those vessels being financed.
The focus of UCB AG's ship finance is the financing of ships for which a liquid, transparent, and efficient secondary market exists. This includes e.g. container vessels, bulk carrier, and tanker.
The GLOS model is mainly related to the transaction and it is characterized by an absent (or limited) use of the sponsor and allows calculating the probability of default (PD) and the loss given default (LGD) of the borrower.
The PD calculation in the quantitative module is based on a Monte Carlo simulation. The development of the quantitative factors (e.g. the ship value) is based on the stochastic process, where the parameters are validated and estimated annually, based on external data. The cash flow is calculated for each quarter of the financing period.
The financial rating based on quantitative factors is adjusted based on the following qualitative factors (upward or downward adaptation of PD by a certain number of notches):
• commercial management (e.g. reputation);
• technical management (e.g. fleet size);
• position of UCB AG (e.g. covenants);
• insurance;
• vessel quality;
• fallback financing.
The validation performed by the local validation unit in 2012 showed the necessity for a recalibration based on most recent default data. The recalibration was performed in March 2013. The time series parameters are updated regularly according to market evolution.
The calculation of the LGD follows the same approach as the general LGD model for Retail / Corporates (see the related paragraph). A special focus for the GLOS portfolio lies on the calculation of the vessels market value for which is used the Monte Carlo Simulation.
In 2012 a local validation of the LGD model including collateral valuation was done. The results has shown the necessity to adjust the LGD unsecured and the recovery rate for vessel liquidation. The recalibrated values are used since January 2013 in the LGD model,
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Regarding LGD parameter, UCB AG developed a method to evaluate the collateral value of the Wind Energy plants. This approach is based on Monte Carlo simulations of future cash flows of the Wind Energy plants. The simulations are consistently used for PD and collateral evaluation. Additionally a LGD on the unsecured exposure is determined. A local validation was done in 2012. The model parameters are overall very stable. Based on the results, it was decided to only adjust the quantile used for collateral evaluation.
The validation function assessed the methodology adopted and the overall model design, both considered in line with portfolio’s characteristics, internal standards and the information completeness. Also the calibration and the statistical definition of the qualitative module were assessed, showing some improvement areas.
Local German LGD model
The scope of application of the UCB AG LGD model is all the facilities related to corporate and retail customers, except for bonds and all specialized lending.
The LGD represents the financial loss suffered by the bank on the individual transaction, and is calculated as a percentage of the exposure to default. The LGD is calculated for each individual transaction and takes account of the fact that different types of default are possible:
• Liquidation: total liquidation and forced recovery of collaterals. The relationship with the customer is terminated and the customer is removed from the portfolio.
• Settlement: the customer re-enters the performing portfolio after reporting a major loss (> €100) to the bank.
• Cure: once the period of difficulty is over, the customer re-enters the performing portfolio without reporting a major loss to the bank (<100 €).
In the case of a Cure, the LGD is set at 0, while in the other two cases the estimation of the LGD follows a work-out approach, with separate estimation of the recoveries deriving from collaterals and those deriving from the unsecured part of the exposure. Personal guarantees and credit derivatives are not taken into account in the models, since the substitution approach is used for this type of guarantees.
In order to determine the final value of the LGD, the following factors are taken into consideration:
• minimum value that the LGD can assume according legislative provisions (e.g. 10% for residential mortgages);
• the Exposure at Default;
• the sum over all collaterals securing the loan;
• estimated rate of non-cure cases;
• discounted expected recovery value of the collaterals, netted by direct costs;
• discounted expected rate of loss of the unsecured portion of the transaction; netted by the costs directly associated to the recovery process;
• percentage of indirect costs;
• any adjustment factor to take into account a potential worsening of the economic cycle.
With regard to the procedure for estimating the rate of recovery from the collateral, this has been obtained on the basis of a historical sample and calculated differently for the following types of collaterals:
• real estate;
• other collaterals.
This value has then been discounted by taking account of the average observed duration of the defaults.
With regard to the procedure for estimating the unsecured part, on the other hand, this has been carried out by rating procedures (the main categories are Mid Corporate Rating, Small Business Customer Rating, Product scoring, Commercial Real Estate) and customer segments.
During 2012 a new parameterization was made mainly to continue the model improvements started in 2011 by adding further rating procedures as risk driver to the model for the LGD unsecured estimation (the Commercial Real Estate rating was added as risk driver in2013). Moreover, the validation unit has examined the model structure, the specific parameters as well as the effect of the economic cycle. Finally, the model calibration and its components have also been assessed with positive results carrying out a new parameterization where necessary. As usual a yearly local validation activity will be performed within 2013, in order to evaluate whether the model is still fully adequate and if there is the need for carrying out a re-parameterization to include more recent data.
Local German EAD model
The model is applied in UCB AG to all the products belonging to local partner that are IRB-A relevant (with the exclusion of the transactions belonging to partners with a groupwide rating).
The EAD is defined as the exposure at the time of default. The exposure is the total outstanding amount
before loan loss provisions and write-offs. The prediction horizon of the EAD model is one year. This means that, when the model is applied, the estimates refer to the expected exposure when default occurs within one year time.
It is estimated for each individual transaction as the sum of two components, Ead On Balance and Ead off Balance, where the parameter that is estimated is obviously the EAD (Off balance).
This parameter depends on the following elements:
• CEQ: Credit Equivalent Factor; this is the credit conversion factor for the credit, and represents the portion of the commitment/guarantee issued by the bank that will be used;
• LEQ: Limit Equivalent Factor; this is the percentage of the amount unused 1,2,…,12 months before the default that is expected to be used at the time of the default;
• LOF (Limit Overdraft Factor) and BO (Base Overdraft) are the parameters that estimate the expected amount of use that, at the time of the default, will exceed the allocated maximum limit (overdraft amount); in the application phase BO is always zero;
• Endorsement: amount of commitments issued to the bank’s customer;
• External line: line of credit;
• External line: line of credit;