structured finance opportunities · 9 • alternatively, when historical data is limited or when...

www.acra-ratings.com

Structured Finance Opportunities Securitization of loans to small and medium-sized businesses

Stefan AugustinGlobal Head of Structured Finance Group October 15 2019, Moscow

2www.acra-ratings.com

What is an SME?

• The European definition of SME follows: "The category of micro, small and medium-sized enterprises (SMEs) is

made up of enterprises which employ fewer than 250 persons and which have an annual turnover not exceeding

50 million euro, and/or an annual balance sheet total not exceeding 43 million euro.“

• In some countries, there are also certain restrictions on the turnover and/or revenue of the enterprise.

Source: European Commission

Company category Staff headcount Turnover or Balance sheet total

Medium-size < 250 ≤ € 50 m ≤ € 43 m

Small < 50 ≤ € 10 m ≤ € 10 m

Micro < 10 ≤ € 2 m ≤ € 2 m


SME classification criteria in different countries

CountrySize of enterprise(number of employees)

Turnover/revenue

Russia(revenue criteria after doubling on July 13, 2015, due to changes in the

exchange rate)

≤250 (≤15, ≤100)

Revenues or assets: RUB 2 bln

(RUB 120 mln,

RUB 800 mln)

Canada <500 Revenues up to CAD 50 mln

Czech Republic <250 (<10, <50) -

Slovakia <250 (<10, <50) Turnover up to EUR 50 mln

Switzerland <250 (<10, <50) -

Great Britain <250 (<10, <50) -

France <250 (<10, <50) Turnover up to EUR 50 mln, asset value up to EUR

43 mln (EUR 2 mln, EUR 10 mln)

South Korea <300 -

US <500 (<50, <100) -


Main Risks of a Typical SME Transaction

• The credit quality is typically driven by

1. the type of contracts securitized (e.g., loans versus short-term facilities, tenure, repayment

profile, etc.),

2. the credit risk related to the obligors (taking into account possible group relationships),

3. the portfolio composition in terms of obligor, regional and industry concentrations,

(i.e. correlation) as well as

4. the type and amount of collateral (e.g., real estate properties) securing the loan receivables.

Furthermore, the originator’s specific underwriting and servicing policies, along with the current and

forecast macroeconomic environment, may affect the credit profile of the pools.

Portfolio Credit Quality: an accurate assessment of the collateral credit quality is the main assumption

to projecting the potential losses on the portfolio/rated notes.


Main Risks of a Typical SME Transaction

• Transaction Structure: Features such as cash flow waterfall, credit enhancement and cash-trapping mechanisms,

have an impact on the expected loss (EL) for each tranche of securities.

− Transactions with a revolving or pre-funding period will add uncertainty to the future portfolio composition

and credit quality.

• Counterparty Risk/Operational Risk: Our assessment focuses on the risks posed by the main counterparties such

as the servicer, cash manager, swap provider, and any associated structural mitigants, such as counterparty

replacement triggers.

• Legal Aspects: We analyze the extent of protection for the transaction against the effects of a bankruptcy of the

sponsor of the transaction or of the issuer of the securities. Potential effects of a bankruptcy include

− delays in payments to investors

− investors becoming unsecured creditors in the estate of the bankrupt entity and

− set off against funds owed by the bankrupt entity to creditors of the entity.


1To project those losses, when

available, we examine historical

data from the originator and

adjust this data for factors that

can drive differing behavior in the

future.

2To be representative, the historical

vintage data should

a) cover a full economic cycle and, ideally, the

longest maturity horizon of the securitized

product,

b) reflect the underwriting/servicing criteria

applied by the originator/servicer for the

securitized portfolio and

c) be granular.

3Recovery rate data should cover a full

economic/real estate cycle and

consider both open positions (i.e.,

work-out process still ongoing) and

closed positions (i.e., workout process

finalized).

Historical Data Analysis

A key element of our analysis is projecting the expected default/recovery and, finally, loss, which is the

projected amount of cumulative net losses on the pool of SMEs loans over the life of the pool.


4For the missing periods, we can

extrapolate by relying on average

changes in the cumulative

observed default rates, either on

an absolute or percentage basis,

in similar pools. When only static

data on cumulative net losses is

available, we may extrapolate the

net loss rate instead of the

cumulative gross default rate.

5Adjustment for Seasoning — seasoned

loan vintages will have already

experienced defaults.

Adjustment to Reflect Securitized Pool

Characteristics.

6To calculate the repayment and

prepayment vectors of a collateral

portfolio for the Cash Flow model

ACRA takes into account:

a) Amortization schedules of each loan;

b) Originator’s historical data on prepayment

rates (CPR) for each particular type of product

which was included in the portfolio.

Historical Data Analysis


Rating Analysis

• The rating analysis of SME-backed securitizations includes both quantitative and qualitative elements.

• The main drivers of our quantitative analysis are projections of the future losses on the underlying assets, which

depends on the asset default rate and the recovery rate on assets that default.

• Typically, we project the probabilities of various pool default rates over the life of the transaction, via a “probability

distribution” of asset default rates, with a separate analysis of recoveries.

• The approach that we take to determine the probabilities of the asset default scenarios depends largely on the

data available and on the granularity and diversification of the pool.

• If the transaction has a well-diversified pool and we have sufficient historical performance data on similar

pools, we typically use a statistical probability distribution (such as the normal inverse) to project the pool’s

defaults.

• The probability distribution would reflect our projections of the pool’s expected default rate and its

variance, which are based on the historical data and are adjusted for differences in the characteristics of the

pools


• Alternatively, when historical data is limited or when the securitized pool has significant asset concentrations,

we determine a transaction-specific default distribution by using a Monte Carlo simulation approach.

• In this case, we typically use individual borrower/loan data (derived from various sources such as credit

estimates, mappings of bank’s internal ratings, the GRASP SME approach or CMBS Style analysis) to

determine the default distribution.

• In some cases, we may approximate the distribution resulting from the simulated loss behavior of the

individual assets with a probability distribution such as a normal inverse (or large homogeneous portfolio

approximation).

• We would choose to do so only in cases when the approximation gives results that are close to the distribution

resulting from the simulation.

Rating Analysis


• Using a model of the transaction’s structure, we calculate the cash flows that investors would receive in the

different scenarios, and weight any shortfalls in investor cash flows (i.e., “investor losses”) by the probability

of occurrence (from the calculated probability distribution).

• That is, we base our rating on the “expected” (i.e., probability-weighted) loss to investors.

• When determining final ratings, rating committees will consider, where appropriate, additional qualitative

and quantitative factors that they deem relevant.

Rating Analysis


A comparative cross-border statistical analysis of factors affecting SME loan delinquencies and its use for the validation of ACRA rating models


Number of project

participantsData collection format

Composition of the

requested information

Support from

development institutions

Key aspects

At the end of 2016, ACRA launched a project to collect

statistical information on various asset classes

The aim of the project was to create Russia’s first unique federal level analytical base.

Collected Statistical Data on the Russian Lending Market


Collected statistical data

on SME loansRussian banks

Statistical validation

using GRASP

European DataWarehouse

Eligibility criteria

Eligibility criteria

European DataWarehouse

Transaction data from

publically available

sources

Research Structure

Representative

statistical sample

Statistical analysis

of PD drivers

Comparative

analysis

of Russian and

European SME loans

Modelling assumptions


Breakdown of securitization transactions in the EDW database by asset class

Collected Statistical Data on the European Lending Market

637

191169

84

39

9 5

0

100

200

300

400

500

600

700

Mortgage loans Car loans SME loans Consumer loan Leasing Credit cards Commercial

mortgage loans


Statistical analysis of factors affecting the delinquency rate for SME loans


Features of the SME segment:

• The SME segment is extremely heterogeneous, both in terms of the types of borrowers and in terms of the

range of lending products present in the market;

• The characteristics, diversification, and composition of the portfolio largely depend on the specifics of the

originator bank;

• SME loan portfolios are characterized by specific products (e.g., credit lines, loans with individual/bespoke

repayment schedules, second lien loans, etc.).

The credit assessment of SME portfolios demands in-depth analytical expertise and world-class statistical databases ensuring a

competent and objective approach to data collection.

Conclusions


Share of new defaults (90+ day delinquencies) by loan age

Eligibility Criteria

-0,1%

0,0%

0,1%

0,2%

0,3%

0,4%

0,5%

0,6%

0,7%

0 10 20 30 40 50 60 70

Sh

are

of

defa

ult

ed

lo

an

s, %

Loan age (months)


Distribution of European SME loans in the statistical sample

by country of issue

Eligibility Criteria

27,79%

25,42% 25,31%

11,80%

8,69%

0,99%

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

30,00%

Italy Russia Spain Belgium Portugal Netherlands


Distribution of defaults (90+ day delinquencies) by borrower segment

Company Size

8,98%

4,30% 4,41%

14,72%

11,64%

10,55%

0,00%

2,00%

4,00%

6,00%

8,00%

10,00%

12,00%

14,00%

16,00%

Micro Small Medium

Europe Russia


Distribution of defaults (90+ day delinquencies) by borrower industry

Borrower Industry

15,03%

10,49%

8,77%

7,62%6,15%

7,80%6,59%

8,23%

5,91%6,71%

21,55% 21,09%

18,32%

15,76% 15,51% 15,30%14,53%

13,57% 13,30%12,10%

-3,00%

2,00%

7,00%

12,00%

17,00%

22,00%

Construction of

buildings

Warehousing and

support activities

for transportation

Real estate activities Specialised

construction

activities

Manufacture of

fabricated metal

products, except

machinery and

equipment

Other personal

service activities

Retail trade, except

of motor vehicles

and motorcycles

Food and beverage

service activities

Manufacture of

food products

Manufacture of

machinery and

equipment n.e.c.

Europe Russia


Comparative analysis of construction industry default rates by country

Comparative Analysis of the Russian and European SME Markets

21,93% 21,55%

19,79%

15,03%

8,21%

3,28%

0,89%

9,31%

14,57%

11,51%

8,33%

4,80%

1,98%

4,05%

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

Spain Russia Italy Europe Portugal Belgium Netherlands

Default rates in construction and real estate Average default rate by country


Distribution of defaults (90+ day delinquencies) by credit product type*

*The share of balloon/bullet loans in the sample of Russian banks is relatively small. The small sample size causes corruption (overestimation) in the default rates on such loans, and therefore they were not included in the final graph.

Type of Amortization

7,19%

10,65%

5,58%

9,37%

19,61%

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

Term loans Credit lines Balloon/bullet loans

Europe Russia


Balloon/bullet loans are subject to refinancing risk

The credit quality of balloon/bullet loans is closely related to the credit quality of the originator bank

Similarly, the credit quality of revolving credit lines is closely related to the credit quality of the

originator bank

When analyzing the credit quality of portfolios containing any of these types of loans, ACRA considers the aspects that

increase the credit risk of the portfolios and applies the appropriate compensatory adjustments to the PD.

Type of Amortization


Distribution of defaults by company age (years)

Company Age

10,02%

8,41%7,91%

7,29%

5,35% 5,10%

4,06% 3,97%

2,95%

7,57%

9,10%

8,03%

7,53%

5,75%

4,91%4,55% 4,44%

4,76%

0,00%

2,00%

4,00%

6,00%

8,00%

10,00%

12,00%

≤ 3 4-6 7-9 10-12 13-15 16-18 19-21 22-24 > 24

Europe Russia


Statistical validation of the structured finance model platform using international statistical data


Key debt, borrower, and collateral metrics:

• Borrower industry (using NACE classification);

• SME entity category classified as per the European Commission's criteria (micro, small, and medium-sized enterprises);

• Country of incorporation;

• Region of incorporation;

• Incorporation date;

• Initial loan amount / limit;

• Principal balance as of each reporting date;

• Loan purpose;

• Principal debt amortization type;

• Loan product;

• Interest basis (fixed, floating, hybrid/conditional variable);

• Default characteristics (according to the Basel III definition);

• Days in delinquency;

• Loan origination date;

• Initial loan repayment date;

• Grace period for interest or principal payments.

Key Selection Criteria


GRASP

SME

GRASP

MC

GRASP

WF

Determining credit quality of

individual assets

SME Loan Portfolio

Credit Rating

Incorporating adjusting assumptions

Determining default distribution

Incorporating transaction structure

profile

Rating Analysis Structure of a SME Loan Secutization Transaction


BRI

Industry

Individual

assessments of

asset credit quality

Company age

Company size

Payment frequency

Underwriting quality

Special incentives

Ad

just

men

ts

Basic Rating Indicator is an

assumption regarding the

quality of a standard asset in

the pool

GRASP

MC

Correlation assumptions

Cross-default assumptions

Monte Carlo simulation

modelling

Default distribution

GRASP SME

Asset Portfolio Quality Analysis


GRASP

WF

PD mean

Default

distribution

Individual

characteristics of

portfolio

Transaction

structure specifics

Standard deviation

Default distribution and

amortization vectors

Expected loss assumptions

Replenishment portfolio quality

assumptions

Interest rates

Prepayment assumption

Issue structure

Amortization triggers

Allocation of payments

Credit Rating

Transaction Structure Analysis


Transaction Moody's expected default rate ACRA’s expected default rate Difference Observed default rate

Italy 1 21.2% 22.7% -1.6% 3.9%

Italy 2 17.7% 17.8% -0.1% 7.3%

Italy 3 17.6% 17.4% 0.2% 17.6%

Italy 4 18.8% 17.4% 1.4% 0.5%

Italy 5 16.5% 15.2% 1.2% 20.9%

Italy 6 20.8% 17.6% 3.2% 1.9%

Italy 7 15.0% 21.8% -6.8% 0.9%

Italy 8 13.0% 19.4% -6.4% 1.4%

Italy 9 15.9% 15.7% 0.2% 3.8%

Italy 10 17.3% 23.0% -5.7% 3.4%

Italy 11 24.5% 21.2% 3.3% 0.0%

Italy 12 22.0% 17.0% 5.0% 14.3%

Italy 13 14.0% 19.7% -5.7% 0.5%

Spain 1 16.9% 19.6% -2.6% 0.8%

Spain 2 9.1% 11.1% -2.0% 0.5%

Spain 3 9.1% 9.6% -0.5% 0.8%

Spain 4 8.0% 10.2% -2.3% 1.1%

Spain 5 12.4% 12.0% 0.4% 0.7%

Spain 6 13.0% 13.2% -0.2% 0.7%

Spain 7 10.6% 14.7% -4.1% 0.8%

Spain 8 7.1% 7.7% -0.6% 0.1%

Spain 9 9.3% 9.7% -0.4% 2.8%

Portugal 1 31.7% 17.0% 14.8% 15.0%

Portugal 2 16.0% 12.1% 3.9% 0.1%

Netherlands 12.0% 15.4% -3.4% 4.0%

Germany 9.0% 9.1% -0.1% 0.0%

Projected Default Probabilities


Modelling results:

• ACRA's findings are overall consistent with the modelling results of international rating agencies.

• The absolute average deviation of estimated PD values calculated by ACRA and Moody’s was around -0.3%.

• In the course of analyzing transactions originated in Portugal, ACRA identified that international rating

agencies applied additional stress adjustments to offset the relatively low credit ratings of originating banks

and the increased sovereign risk of Portugal. A substantial deviation of PD assessments by ACRA and

Moody’s in specific transactions (14.8%) is attributable to these factors.

• The average observed deviation of PD assessments in transactions originated in Italy and Spain was -0.9%

and -1.5%, respectively. The overwhelming majority of ACRA's assessments were found to be more

conservative.

Projected Default Probabilities


TransactionMoody's Ratings* АCRА’s Ratings** Difference in notches

Tranche A Tranche B*** Tranche A Tranche B Tranche A Tranche B

Italy 1 Aaa Aaa 0

Italy 2 Aaa Aaa 0

Italy 3 Aaa Baa2 Aaa A3 0 2

Italy 4 Aaa Aa2 Aaa Aaa 0 2

Italy 5 Aaa Aaa Aaa Aaa 0 0

Italy 6 A2 Aa3 2

Italy 7 Aaa Aa2 -2

Italy 8 Aaa Aa2 Aaa Aaa 0 2

Italy 9 Aaa Aaa 0

Italy 10 A2 A2 0

Italy 11 A2 A2 0

Italy 12 A2 A3 -1

Italy 13 A1 A3 -2

Average absolute difference in notches for

transactions in Italy 0.5 1.5

Rating Comparison

* Moody’s ratings are given without country ceiling.

** ACRA’s test ratings were assigned based on Moody’s idealized table.

*** The absence of a rating in the table is due to the fact that that a rating was not assigned (was not solicited by the originator).


TransactionMoody's Ratings* АCRА’s Ratings** Difference in notches

Tranche A Tranche B*** Tranche A Tranche B Tranche A Tranche B

Spain 1 Aaa Baa3 Aaa Baa3 0 0

Spain 2 A1 Baa1 A1 Ba2 0 -4

Spain 3 A3 Ba1 A1 Ba3 2 -2

Spain 4 Aaa Caa1 Aa2 Caa1 -2 0

Spain 5 A3 B1 A2 Caa1 1 -3

Spain 6 Aaa Caa2 Aaa Caa1 0 1

Spain 7 Aa3 B2 Aa3 Caa3 0 -4

Spain 8 Aaa B2 Aaa B3 0 -1

Spain 9 Aaa Ba3 Aaa B2 0 -2

Average absolute difference in notches for

transactions in Spain 0.6 1.9

Portugal 1 A3 B1 Aa2 Caa1 4 -3

Portugal 2 A3 Baa3 Aa2 Baa3 4 0

Netherlands Aaa Aaa 0

Germany A2 Baa2 -3

Average absolute difference in notches for all

transactions 0.9 1.7

Rating Comparison

* Moody’s ratings are given without country ceiling.

** ACRA’s test ratings were assigned based on Moody’s idealized table.

*** The absence of a rating in the table is due to the fact that that a rating was not assigned (was not solicited by the originator).


TransactionACRA’s Ratings* Total credit enhancement

Observed default rateYear transaction was

closedTranche A Tranche B Tranche A Tranche B

Italy 1 AAA 40.33% 3.9% 2014

Italy 2 AAA 40.69% 7.3% 2014

Italy 3 AAA A- 46.00% 31.00% 17.6% 2014

Italy 4** AAA 47.20% 0.5% 2014

Italy 5 AAA A- 61.30%*** 48.30% 20.7% 2011

Italy 6 AA- 32.90% 3.90% 1.9% 2012

Italy 7 AA 42.10% 0.9% 2012

Italy 8** AAA A+ 34.66% 23.38% 1.4% 2016

Italy 9 AAA 35.12% 3.8% 2014

Italy 10 A 28.30% 3.4% 2012

Italy 11 A 33.00% 0.0% 2012

Italy 12 A- 36.20% 14.3% 2012

Italy 13 A- 39.34% 0.5% 2016

Spain 1 AAA BBB- 43.50% 20.00% 0.8% 2014

Spain 2 A+ BB 35.00% 20.00% 0.5% 2014

Spain 3 A+ BB- 40.00% 20.00% 0.8% 2013

Spain 4 AA CCC 25.00% 3.00% 1.1% 2015

Spain 5 A CCC 30.00% 10.00% 0.7% 2013

Spain 6 AAA CCC 22.00% 4.00% 0.7% 2015

Spain 7 AA- C 22.00% 4.75% 0.8% 2016

Spain 8 AAA B- 22.00% 7.00% 0.1% 2014

Spain 9 AAA B 35.00% 10.00% 2.8% 2013

Portugal 1 AA CCC 46.60% 42.15% 15.0% 2015

Portugal 2 AA BBB- 40.00% 30.00% 0.1% 2016

Netherlands AAA 34.29% 4.0% 2014

Germany BBB 22.88% 0.0% 2017

* ACRA’s test ratings were assigned based on Moody’s idealized table.

** In the mentioned transactions, ACRA considered senior tranches the ones that ranked on the top following the Interest waterfall.

*** The high level of total credit enhancement is due to portfolio credit quality and originator assessment.

ACRA’s International Rating Scale for Structured Finance


Statistical validation performed using the GRASP modelling platform revealed that:

• The rating analysis of the sample of European SME loan securitization transactions performed in compliance with the current

methodology (using the GRASP modelling platform) showed stable and sufficiently conservative results

• Validation results prove the validity and reliability of ACRA's approach as well as its applicability in the European SME loan

securitization market

• Credit enhancement levels, which are incorporated in the transaction structure and confirmed by ACRA’s calculations, ensure the

protection required to offset actual recorded losses with respect to all rated tranches including subordinated tranches.

The model platform and ACRA’s analytical approach can be applied to assign ratings to Russian and European issues of

structured finance notes backed by SME loans.

Conclusions


Тренинги АКРА по кредитному анализу

Для записи и информации:

Максим ИвакаевМенеджер по работе с клиентами

[email protected]

+7 (495) 139 04 80, доб. 164

На сайте АКРАwww.acra-ratings.ru/trainings

• Основы кредитного анализа суверенного риска

(открыт набор на 24 октября 2019 г.)

• Основы корпоративного кредитного анализа

(открыт набор на 29-30 октября 2019 г.)

• Основы анализа сделок структурированного финансирования

(открыт набор на 7-8 ноября 2019г.)

• Основы кредитного анализа страховых компаний

(открыт набор на 14-15 ноября 2019 г.)

• Прогнозирование в кредитном анализе.

Курс 1: основы построения макроэкономических и отраслевых моделей

(открыт набор на 20-21 ноября 2019 г.)

• Основы кредитного анализа банков и небанковских финансовых организаций

(открыт набор на 30-31 января 2020 г.)

• Основы кредитного анализа региональных и муниципальных органов власти

(открыт набор на 12-13 февраля 2019г.)

• Прогнозирование в кредитном анализе.

Курс 2: практические аспекты экономического моделирования

(открыт набор на 7-8 апреля 2020г.)

• Углубленный анализ сделок структурированного финансирования

(открыт набор на 15-16 июня 20120г.)

АКРА видит своей миссией

развитие лучших практик

на российском финансовом

рынке, дающих основу для его

устойчивого функционирования

АКРА обладает уникальным

профессиональным опытом

и глубоким пониманием

кредитного риска

Тренинги АКРА призваны

способствовать повышению

квалификации участников

финансового рынка, увеличению

эффективности управленческих

и инвестиционных решений


Контакты

Получение кредитного рейтинга

Наталия СусленниковаРуководитель Дирекции по развитию бизнеса[email protected]

тел.: +7 (495) 139 04 80, доб. 148

моб.: +7 (962) 988 00 70

Головной офис АКРА

Россия, Москва, 115035

Садовническая набережная, 75

[email protected]

тел.: +7 (495) 139 04 80

Международное сотрудничество

Андрей Бобовников

Старший директор по международному развитию

[email protected]

тел.: +7 (495) 139 04 80, доб. 183

моб.: +7 (965) 118 25 05

Нерейтинговые услуги

Андрей Королев

Генеральный директор АКРА РМ

[email protected]

тел.: +7 (495) 287 70 55, доб. 500

Филиал АКРА в МФЦА, Казахстан

Аскар ЕлемесовГлава филиала[email protected]

Группа рейтингов

структурированных финансовых

инструментов

Штефан Аугустин

Управляющий директор - руководитель Группы

+7 495 139 03 02 (145)

[email protected]

СПАСИБО ЗА ВНИМАНИЕ


Ограничение ответственности

(С) 2019

Аналитическое Кредитное Рейтинговое Агентство (Акционерное общество), АКРА (АО)

Москва, Садовническая набережная, д. 75

www.acra-ratings.ru

Аналитическое Кредитное Рейтинговое Агентство (АКРА) создано в 2015 году. Акционерами АКРА являются 27 крупнейших компаний России, представляющие финансовый и корпоративный сектора, а уставный капитал

составляет более 3 млрд руб. Основная задача АКРА — предоставление качественного рейтингового продукта пользователям российского рейтингового рынка. Методологии и внутренние документы АКРА разрабатываются в

соответствии с требованиями российского законодательства и с учетом лучших мировых практик в рейтинговой деятельности.

Представленная информация, включая, помимо прочего, кредитные и некредитные рейтинги, факторы рейтинговой оценки, подробные результаты кредитного анализа, методологии, модели, прогнозы, аналитические обзоры

и материалы и иную информацию, размещенную на сайте АКРА (далее — Информация), а также программное обеспечение сайта и иные приложения, предназначены для использования исключительно в ознакомительных

целях. Настоящая Информация не может модифицироваться, воспроизводиться, распространяться любым способом и в любой форме ни полностью, ни частично в рекламных материалах, в рамках мероприятий по связям с

общественностью, в сводках новостей, в коммерческих материалах или отчетах без предварительного письменного согласия со стороны АКРА и ссылки на источник. Использование Информации в нарушение указанных

требований и в незаконных целях запрещено.

Кредитные рейтинги АКРА отражают мнение АКРА относительно способности рейтингуемого лица исполнять принятые на себя финансовые обязательства или относительно кредитного риска отдельных финансовых

обязательств и инструментов рейтингуемого лица на момент опубликования соответствующей Информации.

Некредитные рейтинги АКРА отражают мнение АКРА о некоторых некредитных рисках, принимаемых на себя заинтересованными лицами при взаимодействии с рейтингуемым лицом.

Присваиваемые кредитные и некредитные рейтинги отражают всю относящуюся к рейтингуемому лицу и находящуюся в распоряжении АКРА существенную информацию (включая информацию, полученную от третьих лиц),

качество и достоверность которой АКРА сочло надлежащими. АКРА не несет ответственности за достоверность информации, предоставленной клиентами или связанными третьими сторонами. АКРА не осуществляет аудита

или иной проверки представленных данных и не несет ответственности за их точность и полноту. АКРА проводит рейтинговый анализ представленной клиентами информации с использованием собственных методологий.

Тексты утвержденных методологий доступны на сайте АКРА по адресу: www.acra-ratings.ru/criteria.

Единственным источником, отражающим актуальную Информацию, в том числе о кредитных и некредитных рейтингах, присваиваемых АКРА, является официальный интернет-сайт АКРА — www.acra-ratings.ru. Информация

представляется на условии «как есть».

Информация должна рассматриваться пользователями исключительно как мнение АКРА и не является советом, рекомендацией, предложением покупать, держать или продавать ценные бумаги или любые финансовые

инструменты, офертой или рекламой.

АКРА, его работники, а также аффилированные с АКРА лица (далее — Стороны АКРА) не предоставляют никакой выраженной в какой-либо форме или каким-либо образом непосредственной или подразумеваемой гарантии

в отношении точности, своевременности, полноты или пригодности Информации для принятия инвестиционных или каких-либо иных решений. АКРА не выполняет функции фидуциария, аудитора, инвестиционного или

финансового консультанта. Информация должна расцениваться исключительно как один из факторов, влияющих на инвестиционное или иное бизнес-решение, принимаемое любым лицом, использующим ее. Каждому из

таких лиц необходимо провести собственное исследование и дать собственную оценку участнику финансового рынка, а также эмитенту и его долговым обязательствам, которые могут рассматриваться в качестве объекта

покупки, продажи или владения. Пользователи Информации должны принимать решения самостоятельно, привлекая собственных независимых консультантов, если сочтут это необходимым.

Стороны АКРА не несут ответственности за любые действия, совершенные пользователями на основе данной Информации. Стороны АКРА ни при каких обстоятельствах не несут ответственности за любые прямые, косвенные

или случайные убытки и издержки, возникшие у пользователей в связи с интерпретациями, выводами, рекомендациями и иными действиями третьих лиц, прямо или косвенно связанными с такой информацией.

Информация, предоставляемая АКРА, актуальна на дату подготовки и опубликования материалов и может изменяться АКРА в дальнейшем. АКРА не обязано обновлять, изменять, дополнять Информацию или уведомлять кого-

либо об этом, если это не было зафиксировано отдельно в письменном соглашении или не требуется в соответствии с законодательством Российской Федерации.

АКРА не оказывает консультационных услуг. АКРА может оказывать дополнительные услуги, если это не создает конфликта интересов с рейтинговой деятельностью.

АКРА и его работники предпринимают все разумные меры для защиты всей имеющейся в их распоряжении конфиденциальной и/или иной существенной непубличной информации от мошеннических действий, кражи,

неправомерного использования или непреднамеренного раскрытия. АКРА обеспечивает защиту конфиденциальной информации, полученной в процессе деятельности, в соответствии с требованиями законодательства

Российской Федерации.

structured finance opportunities · 9 • alternatively, when historical data is limited or when...

Documents