come sviluppare sistemi di rating interni alla luce dei nuovi requisiti normativi

Come sviluppare sistemi di rating interni alla luce dei nuovi requisiti normativi Comunicazioni Banda D'Italia, Consob, Ivass e Covip del 22 Luglio 2013

6 MARZO 2014

Programma

1. Il panorama regolamentare

2. Rating Implicito vs Rating Tradizionale: Gestione del Rischio nel Rispetto delle Nuove Normative, a cura di Pablo Barbagallo

3. Uno sguardo al futuro, a cura di Jim Sarrail

4. Q&A

Il Panorama Regolamentare 1

Crescente pressione regolamentare per definire, applicare e mantenere sistemi interni di rating

4

2009 2009 2011

Direttiva UCITS IV “Undertakings for

Collective Investment in Transferable Securities”

Obbligo di istituzione di una funzione

permanente di gestione del rischio

“..non si affidano esclusivamente ai rating del credito”

“…incoraggiano tali

entità a ridurre l’incidenza di tali riferimenti con

l’obiettivo di ridurre l’affidamento

esclusivo ai rating del credito”

2012

CRA III

Regolamento sulla gestione collettiva del risparmio della Banca d’Italia dell’8

Maggio 2012

2013

“… i gestori procedono ad una rivalutazione dei propri sistemi di gestione del rischio, individuando le esigenze di rafforzamento delle

procedure di selezione, monitoraggio e gestione del

rischio di credito…”

Fondi del mercato monetario dovrebbe "sviluppare sistemi

interni di rating" e "[...] condurre stress test annuali"

“...limitare il numero di agenzie di

Rating...che il rating sia short-term...”

“..anche in presenza

di rating valuti la qualitá creditizia

dello strumento...”

Regolamento (CE) n. 1060/2009

relative alle agenzie di rating

del credito

•Minore affidamento sui rating da parte degli

investitori •Regola di rotazione

•Misure aggiuntive per il rating del debito

sovrano •Responsabilità civile

per i CRA

Banca D’Italia: Comunicazione del 22-

07-2013

Proposta della Commissione Europea sui fondi comuni

monetari

Banca D’Italia, Consob, IVASS e COVIP hanno definito i requisiti regolamentari locali

5

“…effettuano la loro valutazione del rischio di credito e non si affidano esclusivamente o meccanicamente ai rating del credito per la valutazione del merito di credito di un’entità o di uno strumento finanziario”;

“Le autorità settoriali competenti incaricate della vigilanza delle entità di cui all’articolo 4, paragrafo 1, primo comma, tenendo conto della natura, della portata e della complessità delle loro attività, controllano l’adeguatezza delle loro procedure di valutazione del rischio di credito, valutano l’utilizzo di riferimenti contrattuali ai rating del credito e, se del caso, incoraggiano tali entità a ridurre l’incidenza di tali riferimenti con l’obiettivo di ridurre l’affidamento esclusivo e meccanico ai rating del credito, in linea con la specifica legislazione settoriale”;

Fonte: Banca D’Italia www.bancaditalia.it/vigilanza/normativa/norm_bi/comunicazioni/Rating22lug.pdf

-“il Regolamento sulla gestione collettiva del risparmio della Banca d’Italia dell’8 maggio 2012, ...prevede ...che il sistema di gestione dei rischi degli OICR definisca le strategie, le politiche, i processi e i meccanismi riguardanti l’individuazione, l’assunzione, la sorveglianza, l’attenuazione dei rischi ...Le SGR sono chiamate a definire, applicare e mantenere disposizioni interne, tecniche di misurazione e procedure per assicurare il corretto funzionamento del sistema di gestione dei rischi degli OICR;

Rating Implicito vs Rating Tradizionale: Gestione del Rischio nel Rispetto delle Nuove Normative 2

Rating Impliciti vs Rating tradizionali 7

Quali sono le differenze fra i Rating impliciti e tradizionali? Ratings delle agenzie

Rating impliciti o EDF

Qualitativo e a volte soggettivo

Ranking per classi per es. Aaa, Aa1, ecc.

Società diverse nello stesso gruppo

Stabile o “through the cycle”

Quantitativo ed oggettivo

Misura anche livelli numerici o “assoluti” di rischio per es. 5.01%

Granulare per es. 5.01% vs. 5.02%

Molto dinamico, aggiornamenti giornalieri

Analisi quantitativo – Possibilià di “What-if Analysis”

Informazioni provenienti da tutte le fonti disponibili: bilanci, prezzi di mercato, ecc.

Rating Impliciti vs Rating tradizionali

Equivalent Median

EDF Credit Measure Lower UpperBa1 0.08% 0.07% 0.09%

Ba2 0.10% 0.09% 0.11%

Ba3 0.14% 0.11% 0.16%

B1 0.19% 0.16% 0.23%

B2 0.27% 0.23% 0.38%

B3 0.52% 0.38% 0.71%

Caa1 0.97% 0.71% 1.32%

Caa2 1.81% 1.32% 2.32%

Caa3 2.97% 2.32% 4.87%

Ca 7.97% 4.87% 35.00%

C 35.00% 35.00% 35.00%

Implied Rating

Bound Range

Equivalent Median

EDF Credit Measure Lower UpperAaa 0.01% 0.01% 0.01%

Aa1 0.02% 0.01% 0.02%

Aa2 0.02% 0.02% 0.02%

Aa3 0.02% 0.02% 0.02%

A1 0.03% 0.02% 0.03%

A2 0.03% 0.03% 0.03%

A3 0.04% 0.03% 0.04%

Baa1 0.05% 0.04% 0.05%

Baa2 0.06% 0.05% 0.06%

Baa3 0.07% 0.06% 0.07%

Implied Rating

Bound Range

Rating implicito aggiornati periodicamente

8

Per società quotate le mapping tables vengono aggiornate mensilmente.

Mapping tables non limitate ai ratings Moody’s.


Expected Default Frequency


Società quotate in Borsa


Modello Quantitativo

CreditEdge e’ un modello econometrico che calcola la probabilita’ di default

(PD) o EDF di società quotate.

CreditEdge si basa sulla teoria delle opzioni (Black-Scholes-Merton) e

l’analisi empirica per calcolare la PD o EDF (Expected Default Frequency)

di societa’ quotate.


EDF Credit Measure for Public Firms

» EDF stands for Expected Default Frequency – the probability that a firm will default within a given time horizon by failing to make an interest or principal payment.

» We provide EDF measures for time horizons of 1 to 10 years.

» EDF Ranges from 0.01% to 35%, i.e., 1 to 3500 basis points.


EDF Methodology Summary

Market Value of Assets

Asset Volatility

Default Point

Distance to Default

DD-EDF Mapping

EDF

Equity is a Call Option on the Assets. Solve for Market Value of Assets and

Asset Volatility.

Market Value of Equity Amount of Short and Long Term Liabilities

Amount of Short/Long Term Liabilities determine Default Point

Distance to Default is the cushion between Market Value of Assets and

Default Point, expressed as a multiple of Asset Volatility.

MKMV’s Default Database is used to empirically map DD to EDF.

EDF is the probability that the firm will default within the specified time horizon.

Implied Rating


When do Firms Default?

14


Default Point

On November 26, 2008, Woolworths Group PLC, a hundred-year-old UK retailer, appointed Deloitte as the administrator and would look for a buyer for all parts of its business.

Default


When do Firms Default?

15


Default Point

Default

Woolworths entered into administration when the AVL reached the DPT. The firm’s EDF started rising in August 2007, 13 months prior to the Default.



Today Time

Value

Calcolo della Distance-to-Default (in breve)

Distribution of Market Value of

Assets at Horizon (1 Year)

EDF™

1 Year

Expected Market Value of Assets

Default Point

Asset Volatility (1 Standard Deviation)

σ

Distance-to-Default (DD)

Distance-to-Default (DD) ≈ The number of Standard Deviations the Market Value of Assets is away from the Default Point


A side-by-side comparison of a strong and a weak company illustrates the interplay between an EDF and its drivers EDFs and key inputs for Johnson & Johnson and RadioShack (as of July 2013)

Metric/Input J&J RadioShack

Default Point (100% of STD +50% of LTD) $37bn $1,095mn

Market Value of Assets $306bn $1,531mn

Market Legerage (DP/MVA) 12% 72%

Asset Volatility 9% 20%

1yr EDF 0.01% 6.96%

EDF-IR Aaa Caa3

MIS Rating Aaa Caa2


J&J is a very low risk company, combining a large gap between its MVA and Default Point and minimal asset volatility

Time $0

$200bn

$100bn

DP = $37bn

MVA $306bn

July 2013 July 2014

$400bn

$500bn

Key drivers of J&J’s EDF

No. of Std. Dev.

% Probability

"Normal Dist" PD

1 68%

2 96% 3 99.7%

4 99.993666% 5 99.9999426697%

6 99.9999998027% <0.01% 6 99.9999998027% <0.01%


RadioShack is a very high risk company, combining a small gap between its MVA and Default Point and excessive asset volatility

Time $0

$1,300mn

$500mn

DP $1,095mn

MVA $1,531mn

July 2013 July 2014

$2,500mn

Key drivers of RSH’s EDF

No. of Std. Dev.

% Probability

"Normal Dist" PD

1 68%

$2,000mn

2 96% 1.X 96% >2%


Come tradurre la Distance-to-Default in un EDF e Rating implicito

» EDFs are derived from an empirical

mapping of DDs to historical default

rates

» Public firm EDFs were calibrated using

US corporates from 1980 to 2007,

including over 8,000 defaults. This is

being extended to take into account the

more recent experience.

DD = 4 maps to a 0.003% PD in

the simple BSM model, but to

a 0.4% EDFTM metric

Note: the EDF-DD curve in the graph is a stylized representation

of the actual DD to EDF mapping function

20


CDS-Implied-EDF

January 2013

The SIEDF model: two model components

The SIEDF model has two components

22

CDS Term Structure for an Entity

Physical Default Probability

Risk Neutral Default Probability

Component 1 a Weibull Hazard Rate Model

Component 2 a One-factor Model for Asset Value Dynamics

January 2013

Estimating Sovereign CDS-I EDFS

23

» CDS-Implied EDF credit measures are estimated in exactly the same manner as described above, with the following two assumptions:

– We assume a 25% recovery rate (75% LGD) in the event of default for sovereigns

– Physical default probabilities for sovereigns are calculated assuming the US investment-grade or high-yield market price of risk

» Although there have been many sovereign defaults in the past several decades, there are very few since the advent of the CDS market

– Realized recovery rate data for the last decade is scarce

– We can look at CDS contracts to infer recovery/LGD rates

» The assumption of using the US investment-grade or high-yield market price of risk for all sovereigns is supported by our own research, as well as some academic research (e.g. Longstaff, Pan, Pederson, and Singleton (2008)*).

* “How Sovereign is Sovereign Credit Risk,” NBER Working Paper No. 13658

January 2013

Italy: CDS-I-EDF

24

January 2013

Spain: CDS-I-EDF

25


Tutti i modelli e le analisi sono trasparenti

26


Uno sguardo al futuro 3


Scenari Macro-economici 28

Stronger Near-Term Rebound S1

S2 Mild Second Recession

S3 Deeper Second Recession

Protracted Slump S4

Baseline / Most Likely BL

Standard

Below Trend Long Term Growth S5

Oil Price Shock S6

Fed Baseline FB

Fed Adverse Scenario FA

EC-EBA Baseline EB

EC-EBA Adverse ES

Regulatory Driven

PRA-BoE Baseline UKB

Fed Severely Adverse FS

PRA-BoE Severe UKS

PRA-BoE Idiosyncratic UKS


29

Firm- level effects

Industry-level effects

Aggregate-level effects

Idiosyncratic risk matters

Industry- & credit quality-specific sensitivities to macro drivers

Rank order of firms varies with economic conditions

Shape of economy-wide distribution of credit risk varies with economic conditions

Stress Testing of PDs


0.00

0.05

0.10

0.15

0.20

0.00 0.05 0.10 0.15 0.20

Baseline vs. recession scenarios

Source: Moody’s Analytics

Current PD

Futu

re P

D

Baseline Scenario Recession Scenario

30

Stress Testing of PDs


Firm-Level Stressed EDF Measure Examples

BL S1 S2 S3 S4

Source: Moody’s Analytics, September 2013


Società non quotate


Analisi Quantitativo e Qualitativo


Output:

1-year e 5-year EDF: probabilità di default a 1 e a 5 anni.

Bond Default Rate Mapping: is the agency rating whose historical average default rate best matches RiskCalc’s EDF

Percentile: values (1-100%) rank the firm’s EDF relative to the EDF of the companies used to develop the model


Analisi indici di bilancio


Moody’s Alpha Factor


EDFs alone don’t equate to credit spreads, of course. However, they are a key component of our modeled bond-level FVS.

Correlation of Co. asset value to market

Market Risk Premium (broad market)

Expected LGD (sector and seniority-based)

Company EDF

FVS

A simplified/stylistic view of the FVS model at the bond level

l ln=

37

Company size

Term of the bond

Expected

Lo

ss M

kt Price o

f R

isk

Co

. S

ize Facto

r


The principal bond selection criterion for the model portfolios is the issues’ Alpha Factors

A Bond’s Alpha Factor = OAS/FVS » The Alpha Factors for a given month are based on values from the previous

month

Investment Universe:

» A member of ML Euro Investment Grade or Sterling Investment Grade Indices

» Sold by a publicly traded company with a Moody’s Analytics EDF credit measure

» Rated by Moody’s or S&P


The euro IG model portfolio had positive excess returns in 64% of the months, with a bias towards strongly positive months

39

Count of Euro investment grade model portfolio excess returns by month (1/07-2/2014)

0

1

2

3

4

5

6

7

8

9

10

<=-21 -20 - -11 -10 - -5 -4 - 0 1 - 4 4 - 7 8 - 10 11 - 15 16 - 24 25 - 32 33 - 80 81>=

Count

← Underperform Outperform →Monthly Excess Return (bp)


The euro IG model portfolio has outperformed strongly on a cumulative basis

40

Euro IG performance vs. the ML Euro IG Corp Index (2007-2014)

80%

100%

120%

140%

160%

80%

100%

120%

140%

160%

Dec06 May08 Oct09 Mar11 Aug12 Jan14

Alpha Factor Portfolio ML EUIG

Jan07

Average Return

Standard Deviation

Sharpe Ratio

AF portfolio 6.6 3.9 1.4

Benchmark 4.9 4.1 0.9


Many new issues are sold with OAS than are less than their FVS

41

FVS vs. OAS for € , CHF new issues, 1Q 2013 (€500 minimum for Euro)

0 10 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0

0

1

2

3

4

5

6

7

8

0

200

400

600

800

0 200 400 600

FI Corp

Fair

Valu

eSp

read

(bp)

OAS(bp)

Q&A

42

Pablo Barbagallo [email protected] Luca D’Amico [email protected] Jim Sarraill [email protected]

moodysanalytics.com

mailto:[email protected]



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come sviluppare sistemi di rating interni alla luce dei nuovi requisiti normativi

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