fixed income risk engine - euronext.com

Fixed Income Risk Engine

Process Overview

│ 3

Fixed Income Risk EngineOverall process and next steps

Submission to College of Regulators for

approvalTests with Clients

Indicative Go-Live date (subject to approval by CoR)

SEPT/OCT 2021 OCT 2021 to FEB 2022 Q1 2022

• Instruments in scope of the methodology change include Italian, Spanish, Portuguese and Irish government bonds cleared in bonds and ICSD bonds clearing sections

• Current SPAN-like margining methodology will continue to apply to all the remaining bond instruments listed in the above-mentioned clearing sections (i.e. corporate bonds, as well as government bonds issued by countries other than those specifiedabove)

Tests with Clients will take place starting from October 2021 up to February 2022

NEXT STEPS

Relevant documentation and further info will be made available on CC&G website in a dedicated VAR section under Risk Management menu

General Framework

│ 5

Fixed Income Risk EngineChange in the framework

SPAN (Standardized Portfolio Analysis of Risk) VaR (Value at Risk)

Industry standard for a long time.

Its adoption is currently declining as it hardly conforms to increasingmarket complexity

Identified as the next-to-come market best practice (CC&G is workingtoward the implementation of a VaR-like model for the FI Section)

Industry Adoption

MainCharacteristics

INITIAL MARGINS

MARGIN INTERVAL

INITIAL MARGINS

SCENARIOS

PORTFOLIO PnL

Based on each underlying’s time series

Usually 10 to 20 (range based on defined Margin Interval)

Re-evaluation based on definedscenarios

SCENARIOS

PORTFOLIO PnL

Usually more than 1000 (based on lookback period)

Re-evaluation based on definedscenarios

Pros/Cons

+ Proven track record of efficiency during stressed periods

- Scenarios at instrument level

-Correlations between products are not directly managed

through scanning losses procedures

+ Scenarios at portfolio level

+Correlations between products are directly managed

through developed scenarios

+ Standardized across CCPs - Every CCP is developing its own (different) methodology

+/-Updated at discrete points in time (every time Margin Intervals are). Pro-cyclicality can be directly controlled

+/-Dinamically updated. Pro-cyclicality is not directly

controlled

│ 6

Fixed Income Risk EngineRisk Framework

Margin Component Risk Factor/sModel Engine

Mark-to-Market margin

Expected Shortfall margin

Unscaled ES

Scaled ES

Spectral Risk Measures

SS1 margin

Repo-concentration margin

Decorrelation margin

Idiosyncratic-concentration margin

CORE ENGINE

ADD-ONs ENGINE

Market Price Risk (from trade date to evaluation date)

Market Price Risk (what-if scenarios)

Under/Overestimation of Market Price Risk

(pro-cyclicality concerns vs adequate reactiveness of the model)

Tail Dilution Risk

Break-in-correlations Risk

Specific ISIN liquidity / concentration Risk

Settlement Risk

Repo Rate Risk / Repo Term Concentration Risk

Core Engine

│ 8

Fixed Income Risk EngineCore Engine: Mark-to-Market Margins

Mark-to-Market Margins are defined according to the typology of the trades to be settled (cash, repo or forward starting repo) and their

respective close-out procedure that would be put in place by the CCP in case of default of one of the two counterparties

MtM Margins computation for cash trades1

A B

p

bond

trade date settlement datet

(evaluation date)

Trade between A e B

TRADE EXAMPLE

MARGINS CALCULATION DATE

│ 9


CLOSE-OUT PROCEDURE IN CASE OF DEFAULT OF CTP A

CLOSE-OUT PROCEDURE IN CASE OF DEFAULT OF CTP B

CCP

Bp

bond

CCP

Cbond

p 2

t

(default A)

settlement date 2settlement date

Trade between CCP and C

ACCP

p

bond

CCP

Cp 2

bond

t

(default B)

settlement date 2settlement date

Trade between CCP and C

Operations put in place

by the CCP in order to close the original positions

│ 10


As the difference between the settlement date of the original trade and the settlement date of trade originated by the CCP is negligible (usually 1

business day), Mark-to-Market Margins for cash trades can be defined through the following formula:

MtMmargin = N ∗Pmarket+ AISD

100−

Ptrade+ AISD100

∗ ps

MTM MARGINS FORMULA FOR CASH TRADES

N Nominal value of the original trade

P_market Price of the traded security at evaluation date

P_trade Original trading price of the security

AI_SD Accrued interest of the security at settlement date of the original trade

ps Position sign (+ long / - short)

│ 11


MtM Margins computation for repo trades2

A B

p + R1

bond

A B

bond

p

spot termt

(evaluation date)

TRADE EXAMPLE


In the example above spot refers to the settlement date of the spot leg of the repo trade and term refers to the settlement date of the term leg of the repo

trade. The repo interest paid by the cash borrower is represented by R1

│ 12




CCP

Bp + R1

bond

CCP

Cp 2

bond

t

(default A)

term

CCP

Dbond

p 2

CCP

Cbond

p 2 + R2cash

spot 2

term 2

term 1

CCP

Abond

p + R1

CCP

Cbond

p 2

t

(default B)

term

CCP

Dp 2

bond

CCP

Cp 2 + R2

bondcash

spot 2

term 2

term 1


by the CCP in order to close the original

positions

│ 13


The net outflow for the CCP in t amounts to zero as the single flows are perfectly offset. The difference between the original settlement price (p)

and the settlement price of the close-out trade put in place by the CCP (p2) is realised at term date, as well as the difference between the repo

interest to be paid by original the cash borrower (R1) and the repo interest that refers to the close-out trade (R2), therefore MtM margins for repo

trades can be defined as follows:

MTM MARGINS FORMULA FOR REPO TRADES




AI_ED1 / AI_SSD Accrued interest of the security at evaluation date + 1 BD / spot settl. date

R1 / R2 Original repo interest / repo interest on the close-out trade

MtMmargin = (N ∗Pmarket+ AIED1

100−

Ptrade+ AISSD100

− (R1 − R2)) ∗ OISdiscount_factor ∗ ps

│ 14


MtM Margins computation for forward starting repo trades3

Forward starting repo trades differ from repo trades as the spot leg has yet to settle

TRADE EXAMPLE


A B

p + R1

bond

A B

bond

p

t

(evaluation date)

termspot

│ 15



by the CCP in order to close the original positions



CCP

B

p + R1

bond

CCP

B

bond

p

t

(default A)

termspot

CCP

C

bond

p 2 + R2

CCP

C

p 2

bond

ACCP

p + R1

bondA

CCP

bond

p

t

(default B)

termspot

CCCP

bond

p 2 + R2C

CCP

p 2

bond

│ 16


The net outflow for the CCP in t amounts to the difference between the original settlement price (p) and the settlement price of the close-out trade

(p2) discounted from spot to t as well as the difference between p and p2 and R1 and R2 (repo interest of the original trade and repo interest of

the close-out trade) discounted from term to t. MtM margins for forward starting repo trades can therefore be defined as follows:

MTM MARGINS FORMULA FOR FWD STARTING REPO TRADES




AI_ED1 / AI_SSD Accrued interest of the security at evaluation date + 1 BD / spot settl. date

MtMmargin = ((N∗Pmarket+ AIED1

100−

Ptrade+ AISSD100

)∗(OISdf2−OISdf1)−(R1−R2)∗OISdf2)∗ps

R1 / R2 Original repo interest / repo interest on the close-out trade

OISdf1 / OISdf2 Discount factor from spot / term date to evaluation date

│ 17

Fixed Income Risk EngineCore Engine: Expected Shortfall – Cashflow Mapping

MARGINED PORTFOLIO

Clearing Member

Security 1

Security 2

Security n

CASHFLOWS

Security 1

Cashflow 1.1

Cashflow 2.1

Cashflow n.1

Security n

Cashflow 1.n

Cashflow 2.n

Cashflow n.n

…

CASH FLOW MAPPING

3M

6M

1Y

2Y

3Y

Cashflow 1.x

Cashflow 2.x

Cashflow n.x

Cashflows are mapped according to their

duration and to the statistical parameters of

the vertices of the respective contiguous

verticesDuration of the cashflow

Duration of the cashflow

Duration of the cashflow

│ 18

Fixed Income Risk EngineCore Engine: Expected Shortfall – Price Scenarios

Once each cashflow has been assigned to its respective vertex of the issuer curve, for each of those vertices a time series of

price scenarios is generated to be used for the Expected Shortfall computation. EWMA volatility can be used in order to produce

scaled price scenarios time series. Price scenarios are generated on the basis of the holding period (hp) and lookback period

parameters (lp) applied to the margining model:

Scenariot =Pricet

Pricet−hp

Scenariot > 0

Unscaled scenariosScaled scenarios

│ 19

Fixed Income Risk EngineCore Engine: Expected Shortfall – Price Scenarios (scaled)

SCALED PRICE SCENARIOS (hp = holding period, lp = lookback period)

Date 0Date 1

.

.

.Date n

Price 0Price 1

.

.

.Price n

Vertex x prices

time series of prices for vertex x Computation of relative returns based on chosen hp and lp

Return 1Return 2

.

.

.Return lp

Price 1 / Price 1 – hp - 1Price 2 / Price 2 – hp - 1

.

.

.Price n / price n – hp - 1

Vertex x returnsApplication of EWMA volatility

σi = λσi−12 + 1−λ ri

2

Application of scaling factor

σlp + σi

2σi

Scaled time series of price scenarios

For each return the EWMA volatility is computed and the respective scaled factor is defined. Scaled price scenarios

are then defined as follows:

Scaled Scenariot = Returnt ∗ Scaling factort + 1

│ 20

Fixed Income Risk EngineCore Engine: Expected Shortfall – Price Scenarios (scaled)

Through the application of EWMA volatility and the subsequently defined scaling factor different weights are applied to

different scenarios. In particular, the farther the distance in time from evaluation date the smaller the weight that will be

applied to that particular scenario:

σi = λσi−12 + 1−λ ri

2 = λ(λσi−22 + 1−λ ri−1

2 ) + 1−λ ri2

Since the smoothing factor (λ) is a number between 0 and 1:

ri−12 weight in i ri

2 weight in iλ(1- λ) 1- λ<

Recent returns have higher weights than older ones

│ 21

Fixed Income Risk EngineCore Engine: Expected Shortfall – Price Scenarios (unscaled)

Unscaled price scenarios time series are defined so that the same weight is assigned to every scenario regardless of their

distance in time from the evaluation date. Unscaled price scenarios can be thus retrieved directly from the time series of the prices

of the particular vertex:

UNSCALED PRICE SCENARIOS (hp = holding period, lp = lookback period)

Date 0Date 1

.

.

.Date n

Price 0Price 1

.

.

.Price n

Vertex x prices

time series of prices for vertex x

Computation of price scenarios based on chosen hp and lp

Scenario 1Scenario 2

.

.

.Scenario lp

Price 1 / Price 1 – hpPrice 2 / Price 2 – hp

.

.

.Price n / price n – hp

Vertex x scenarios

│ 22

Fixed Income Risk EngineCore Engine: Expected Shortfall

The sum of the cashflows mapped onto the appropriate vertices of the issuer curve are re-evaluated for each one of the computed

scenarios. The subsequantial distribution of gain / losses is drawn and the tails are identified. Based on the approach chosen (single

tail / double tail) the Expected Shortfall is the mean value of the gain / losses lying on the tail(s)

TotalCashflowrevaluated = TotalCashfloworiginal ∗(scenariot −1)

For each scenario t and each vertex of the curve

Distribution of gain / lossesAverage of worst losses

Average of greatest variations

SINGLE TAIL

DOUBLE TAIL

│ 23

Fixed Income Risk EngineCore Engine: Expected Shortfall – Spectral Risk Measures

The Expected Shortfall is computed by using unevenly weighted Spectral Risk Measures (SRM): increasing weights are assigned as the

losses increase

SRM are balanced in order to:

• Ensure the risk appetite of CC&G is satisfied as the losses move further into the distribution

• Correctly tackle pro-cyclicality concerns

│ 24

Fixed Income Risk EngineCore Engine: Overview of the process

CASHFLOWS (MARGINED PORTFOLIO)

Security 1

Cashflow 1.1

Cashflow 2.1

Cashflow n.1

Security n

Cashflow 1.n

Cashflow 2.n

Cashflow n.n

…

MA

PP

IN

G

MAPPED CASHFLOWS

IR NODE 1

IR NODE 2

IR NODE n

Total mapped flows on node 1

Total mapped flows on node 2

Total mapped flows on node n

SCENARIOS

MAPPED CASHFLOWS

IR NODE 1

IR NODE 2

IR NODE n

N° lp scenarios on node 1

N° lp scenarios on node 2

N° lp scenarios on node n

MAPPED CASHFLOWS

IR NODE 1

IR NODE 2

IR NODE n

Re-evaluated cashflows on node 1

Re-evaluated cashflows on node 2

Re-evaluated cashflows on node n

ES

1

2

3

Gains / losses of the entire mapped

portfolio

ES computation (with SRM)

│ 25

Fixed Income Risk EngineCore Engine: Parameters

MODEL PARAMETERS

Tail/s approach

Cross margining

Scaling factor

Holding period

Confidence level

Lookback period

Weighting

Single tail

Intra country

99.9%

5 days

From 99.5% to 99.8% (based on CR of issuer) for scaled approach. 99.5% for unscaled floor

Anchored to 2004

Spectral Risk Measures (step 1.35)

Add-ons Engine

│ 27

Fixed Income Risk EngineAdd-on Engine: Decorrelation Margin

Starting Framework

To tackle possible break-in-correlation among nodes of the same issuer Sovereign

Curve

Potential Extension

To tackle possible break-in-correlation also among

different issuers Sovereign Curves

extendable

Relevant based on current trades volumes (almost completely on the

same country – Italy)

Not relevant based on current trades volumes

Decorrelation Margin computation

Decorrelation Margin 20% ∗(UndiversifiedES −DiversifiedES)Compliant with ESMA art. 27

(portfolio margin)

│ 28

Fixed Income Risk EngineAdd-on Engine: Repo Term Concentration Margin

Closing out a repo/forward starting repo position implies performing (at least) one repo/forward starting repo

operation of the opposite sign of the original one

Hypothetical repo interest = EUR OIS curve + spread original repo rate vs EUR OIS curveMark-to-market margin

Repo-concentration margin add-on

• What-if: EUR OIS curve scaled/unscaled

• Parameter set function of

✓ Issuer Country

✓ Amount

✓ Maturity

in order to manage concentrated repo exposures

│ 29

Fixed Income Risk EngineAdd-on Engine: Repo Term Concentration Margin - Parameters

Risk measure Expected Shortfall(takes into account tail risk)

Tail approach Double tail

Confidence level 99.5%-99.8%(aligned to core country parameter)

Lookback period All available data

Tail weighting Tail weighting to contrast the dilution of tail due to the long time series with 1.35 SRM parameter aligned to core country parameter

Holding period Function of country and repo concentration(see next slides)

│ 30


Repomaturity

bands

-7d

7d – 1m

1m – 3m

3m – 1y

1y+

Repoamountbands

-500€mln

500€mln – 1€bn

1€bn – 5€bn

5€bn+

Holding periods

5

6

7

8

9

10

Country matrix

Repomaturity

band

Repoamount

band

HP set

Repo maturity(1)

Repo amount(1)

HP set (1,1)

Repo maturity(1)

Repo amount(2)

HP set (1,2)

Repo maturity(1)

Repo amount(3)

HP set (1,3)

Repo maturity(1)

Repo amount(4)

HP set (1,4)

Repo maturity(2)

Repo amount(1)

HP set (2,1)

... ... ...

│ 31


Country matrix

Repo maturity band Repo amount band HP set

-7d -500€mln - *

-7d 500€mln – 1€bn - *

-7d 1€bn – 5€bn - *

-7d 5€bn+ - *

7d – 1m -500€mln 5

7d – 1m 500€mln – 1€bn 5

7d – 1m 1€bn – 5€bn 5, 6

7d – 1m 5€bn+ 5, 6, 7

1m – 3m -500€mln 5, 6

1m – 3m 500€mln – 1€bn 5, 6

1m – 3m 1€bn – 5€bn 5, 6, 7

1m – 3m 5€bn+ 5, 6, 7, 8

3m – 1y -500€mln 5, 6, 7

3m – 1y 500€mln – 1€bn 5, 6, 7,8

3m – 1y 1€bn – 5€bn 5, 6, 7, 8

3m – 1y 5€bn+ 5, 6, 7, 8, 9

1y+ -500€mln 5, 6, 7, 8

1y+ 500€mln – 1€bn 5, 6, 7, 8,9

1y+ 1€bn – 5€bn 5, 6, 7, 8, 9

1y+ 5€bn+ 5, 6, 7, 8, 9, 10

│ 32

Fixed Income Risk EngineAdd-on Engine: Idiosyncratic-Concentration Margin

Sovereign curves risk (standard, liquid, on-the-run bonds)

• Simulated price returns of each ISIN are produced on the basis of the relevant benchmark curve

• A series of deltas among actually observed price returns and the simulated once are produced

• ES on the series of deltas is calculated and applied as add-on

• The parameter set used in ES calculation is (also) function of the ISIN concentration (vs its

outstanding) in order to take into account the potential risk associated to particularly concentrated

positions

Core Model

Idiosyncratic-Concentration add-on

Specific risk of a bond type (in particular, linkers and floaters) and/or

the specific liquidity of a particular issue

│ 33

Fixed Income Risk EngineAdd-on Engine: Idiosyncratic-Concentration Margin - Parameters

Risk measure Expected Shortfall(takes into account tail risk)

Tail approach Double tail

Confidence level 99.6%-99.7%(aligned to core country parameter)

Lookback period 1y (allows to tackle most part of ISINs without recurring to approximations)

Multiplier 25%(in order to compensate for potential absence of stress events in the LP and for regulatory-compliant anti-procyclicality purposes)

Holding period Function of bond issuer/type and Clearing Memberconcentration(see next slide)

│ 34

Fixed Income Risk EngineAdd-on Engine: Idiosyncratic-Concentration Margin - Parameters

Country matrix

Bond type Concentration HP set

Bullet/Zc -5% -

Bullet/Zc 5% - 10% -

Bullet/Zc 10% - 15% 5, 6, 7

Bullet/Zc 15% - 20% 5, 6, 7, 8

Bullet/Zc 20% - 25% 5, 6, 7, 8, 9

Bullet/Zc 25%- 5, 6, 7, 8, 9, 10

Floater -5% 5

Floater 5% - 10% 5, 6

Floater 10% - 15% 5, 6, 7

Floater 15% - 20% 5, 6, 7, 8

Floater 20% - 25% 5, 6, 7, 8, 9

Floater 25%- 5, 6, 7, 8, 9, 10

Linker -5% 5

Linker 5% - 10% 5, 6

Linker 10% - 15% 5, 6, 7

Linker 15% - 20% 5, 6, 7, 8

Linker 20% - 25% 5, 6, 7, 8, 9

Linker 25%- 5, 6, 7, 8, 9, 10

│ 35

Concentration Component SubsetExceptions to Regular

Framework

Repo-Term Italian DebtNo stressed hp as a function of

concentration is used

ISIN-Concentration Italian DebtNo stressed hp as a function of

concentration is used

CCP

Overall goal of ensuringsystemic stability

Strategic Participants (Bankit and MEF) may be exemptedfor the application of Concentration add-ons on Italian debt

Fixed Income Risk EngineAdd-on Engine: Concentration Margin - Exceptions

│ 36

Fixed Income Risk EngineRisk Framework Map

│ 37

Fixed Income Risk EngineAdd-on Engine: Settlement Margin

• Margins are computed and collected at discrete points in time, while trading and settlement are almost

continuous throughout the day

• The settlement of cash and repo positions in t+1 may disrupt possible long-short position offsettings,

increasing the risk exposure of the CCP

Positions in portfolio at the moment of calculationCore Model

Settlement Margin add-on (SS1)

Max among:

• Current portfolio (intraday/end of day)

• Current portfolio assuming all t+1 trades have settled

│ 39

Fixed Income Risk EngineFIRE vs MVP: Margins Comparison

6.00

8.00

10.00

12.00

14.00

16.00

18.00

24/05/2018 24/07/2018 24/09/2018 24/11/2018 24/01/2019 24/03/2019 24/05/2019 24/07/2019 24/09/2019 24/11/2019 24/01/2020 24/03/2020 24/05/2020

Tota

l M

arg

in r

eq

uir

em

ent

(bln

€)

New FI Model (Core)

New FI Model (Core + Addons)

Current Model

│ 40

Fixed Income Risk EngineFIRE vs MVP: Margins Comparison – Focus on March 2020

6.00

8.00

10.00

12.00

14.00

16.00

18.00

02/01/2020 02/03/2020 02/05/2020

Ma

rgin

re

quir

em

ent

(bln

€

)

New FI Model (Core)

Current Model

New FI Model (Core + Addons)

│ 41

Fixed Income Risk EngineFIRE vs MVP: Margins Comparison – Delta

-40.00%

-30.00%

-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

24/05/2018 24/07/2018 24/09/2018 24/11/2018 24/01/2019 24/03/2019 24/05/2019 24/07/2019 24/09/2019 24/11/2019 24/01/2020 24/03/2020 24/05/2020

% c

ha

ng

e v

s C

urr

ent

Mo

de

lNew FI Model (Core) vs Current Model

New FI Model (Core + Addons) vs Current Model

│ 42

Fixed Income Risk EngineFIRE vs MVP: Portfolio Backtest

│ 43

Fixed Income Risk EngineFIRE vs MVP: Portfolio Backtest – Focus on May 2018

│ 44

Fixed Income Risk EngineFIRE vs MVP: Portfolio Backtest – Focus on March 2020

│ 46

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