Liquidity risk indicator

Data Service 07 July 2021

Liquidity risk indicator Offerte

IFRS 9 Kreditrisikodaten

January 2022

Inhalt

1

Introduction

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Approach

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Backtesting

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About us

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Introduction ‣ The COVID

19 crisis has once again increased the awareness of financial institutions around potential liquidity risks embedded in investment portfolios.

Investment portfolios usually contain corporate and sovereign bonds.

Attaching a liquidity risk indicator for the individual positions and the portfolio itself can strengthen the risk management around such portfolios.

SigmaQ Analytics provides a liquidity risk indicator for sovereign and corporate bonds, covering basically the entire global, developed market with more than 250’000 bonds.

‣ Thus, in order to further

strengthen the risk management around these portfolios, they are seeking to implement a liquidity risk indicator for the individual positions and the portfolio itself.

information databases to build a composite liquidity risk measure for bonds in investment portfolios.

‣ The derived liquidity risk

indicators are determined on ISIN level.

‣ An associated indicator on

individual portfolio level can be computed based on the indicators on ISIN level.

‣ SigmaQ Analytics is working with and is applying large bond

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Introduction ‣ A liquidity risk indicator should reflect information on the ability to sell a position at current price levels within a short time frame.

‣ The measure is calculated on position/ISIN level and can be aggregated on portfolio level.

‣ The liquidity risk indicator can be updated on ISIN and portfolio level on up to daily basis.

‣ The solution is pragmatic, transparent and cost-efficient.

The liquidity risk indicator is provided on ISIN level and can be aggregated on portfolio level.

‣ Data transfer can be organised through csv files or API connections.

The update frequency of the corresponding liquidity risk indicators can be up to daily.

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Inhalt

1

Introduction

2

Approach

3

Backtesting

4

About us

5

Approach

Background ‣ Liquidity in bond markets finds its expression in different terms:

• transaction costs • market depth • market breadth • immediacy Many different liquidity risk measures exist in the financial services industry, but most of them focus on liquidity aspects of entire businesses/balance sheets or stocks as an asset class.

There is no industry-wide accepted liquidity risk measure for bonds as such.

‣ They reflect the common

understanding of liquidity as the ability to sell a position at current price levels within a short time frame at low transaction costs.

‣ A liquidity metric should ideally

build on factors that are observable in the market like trade and bond characteristics:

• bid-ask spreads (reflecting transaction costs)

• number of trades (reflecting market depth/activity)

• dealer count (reflecting market breadth/depth)

• turnover (reflecting market depth/activity)

‣ For defining a meaningful liquidity

risk measure on portfolio level it is of equal importance to consider portfolio effects: what is the relative size of the position within the portfolio for which a liquidity score shall be calculated?

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Approach

The indicator 1/2

Key features

‣ SigmaQ uses a composite measure

‣ These parameters are collected

‣ On ISIN level we work with the

‣ On portfolio level we aggregate the

of bond liquidity, which is expressed as a score per ISIN.

SigmaQ’s liquidity risk indicator is a composite measure of liquidity risk for bond portfolios. It focusses on: •observable bond and trade characteristics

following set of parameters as input to the composite liquidity measure:

• bid-ask spreads • dealer count: number of

over the set of dealers for which data are available.

scores per ISIN/position based on market-value weighted averaging.

This leads to a portfolio liquidity score with weighted relative contributions of the individual scores.

market places/dealers with trading activity on a given day

•applicability in practice

• trade activity

•pragmatic and transparent methodology •cost-efficiency

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Approach

The indicator 2/2 Trading related data: Our composite measure takes into account trading related data:

• (average) bid-ask spreads of various dealers/market places on a given day

•(average) bid-ask spreads

• dealer count: number of market places for which bid-ask spreads are reported on a given day

•dealer count •number of market places with trading activity

Liquidity score per bond

Portfolio liquidity score

• number of market places with trading activity on a given day

These parameters are read out on daily basis for the available market places.

Weighted averaging

Subsequently it is aggregated using portfolio information.

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Approach

Examples 1/2 Sample portfolio ISIN XS1147605791

We assume a sample portfolio of 5 bonds from different corporate and sovereign issuers. Time to maturity ranges from close to a year to 7 years.

Issuer

Maturity

Glaxosmithkline 2Dec2024

Issue size

Market value in portfolio [mCHF

Relative exposure

1bn

6.88

7.58%

ES0000011868

Spain

31Jan2029

25.5bn

35

38.55%

US66989HAG39

Novartis

6may2024

2.15bn

17.40

19.17%

CH0228531452

Holcim

09jun2022

250m

11.20

12.34%

IT0005045270

Italy

1dec2024

19.85bn

20.30

22.36%

We attach assumed market values to each position in the portfolio and indicate the relative share of each exposure in order to be able to also calculate a liquidity score for the portfolio itself.

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Approach

Examples 2/2

Our liquidity indicator has a granularity of five scores from 1 to 5 corresponding to a range from highly liquid 1 to illiquid 5 .

Liquidity score on September 20 and September 21, 2021 Average bidNumber of market Portfolio Dealer Liquidity ask spread places with liquidity count score [bps] trading activity score

Date

ISIN

20.09.2021

XS1147605791

21

13

4

1.0

20.09.2021

ES0000011868

29

16

5

1.1

20.09.2021 US66989HAG39

29

10

6

1.0

20.09.2021

CH0228531452

35

5

1

2.1

20.09.2021

IT0005045270

5

9

2

1.5

21.09.2021

XS1147605791

27

12

0

1.0

21.09.2021

ES0000011868

25

15

4

1.1

21.09.2021 US66989HAG39

40

9

6

1.4

21.09.2021

CH0228531452

27

4

0

2.1

21.09.2021

IT0005045270

24

8

2

1.6

1.3

1.4

On portfolio level the score is a weighted average of the scores on ISIN level.

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Approach

Data ‣ SigmaQ Analytics is working with large bond databases from external providers.

‣ These databases cover the entire developed market of bond issues 150’000 from corporates and sovereigns.

‣ Through these databases we have access to trading related data on ISIN

level as applied in the calculation of the liquidity indicator on the slide before.

‣ The data cover up to 25 market places/exchanges per ISIN.

SigmaQ Analytics is working with large bond databases for its proprietary risk models, particularly in the area of credit risk management.

‣ The number of available market places depends on ISIN. ‣ The data are available on daily basis

EOD

‣ The liquidity indicator could in principle be further enriched with bond and

These bond databases also contain trading related data, on which the liquidity risk measure is built.

issuer characteristics like issue size, issuer PD, etc. .

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Inhalt

1

Introduction

2

Approach

3

Backtesting

4

About us

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We calculated the weekly time series of the liquidity score for the portfolio set out before from January 2019 to October 2021, covering the COVID 19 crisis.

0 2021 10 11

2021 08 30

2021 07 19

2021 06 07

2021 04 19

2021 03 01

2021 01 18

2020 12 07

2020 10 26

2020 09 14

2020 08 03

2020 06 22

2020 05 04

2020 03 16

2020 02 03

2019 12 23

2019 11 11

2019 09 30

2019 08 19

2019 07 08

2019 05 20

2019 04 01

2019 02 18

2019 01 07

Backtesting 1/6 Portfolio liquidity score

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Peak of COVID 19 crisis

3.75

2.5

1.25

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We calculated the time series of the liquidity score for the individual ISIN within the portfolio.

2021 10 11

2021 08 30

2021 07 19

2021 06 07

2021 04 19

2021 03 01

2021 01 18

2020 12 07

2020 10 26

2020 09 14

2020 08 03

2020 06 22

2020 05 04

2020 03 16

2020 02 03

2019 12 23

2019 11 11

2019 09 30

2019 08 19

2019 07 08

2019 05 20

2019 04 01

2019 02 18

2019 01 07

Backtesting 2/6 CH0228531452

5

3.75

2.5

1.25

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We calculated the time series of the liquidity score for the individual ISIN within the portfolio.

2021 10 11

2021 08 30

2021 07 19

2021 06 07

2021 04 19

2021 03 01

2021 01 18

2020 12 07

2020 10 26

2020 09 14

2020 08 03

2020 06 22

2020 05 04

2020 03 16

2020 02 03

2019 12 23

2019 11 11

2019 09 30

2019 08 19

2019 07 08

2019 05 20

2019 04 01

2019 02 18

2019 01 07

Backtesting 3/6 ES0000011868

5

3.75

2.5

1.25

15

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We calculated the time series of the liquidity score for the individual ISIN within the portfolio.

2021 10 11

2021 08 30

2021 07 19

2021 06 07

2021 04 19

2021 03 01

2021 01 18

2020 12 07

2020 10 26

2020 09 14

2020 08 03

2020 06 22

2020 05 04

2020 03 16

2020 02 03

2019 12 23

2019 11 11

2019 09 30

2019 08 19

2019 07 08

2019 05 20

2019 04 01

2019 02 18

2019 01 07

Backtesting 4/6 IT0005045270

5

3.75

2.5

1.25

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We calculated the time series of the liquidity score for the individual ISIN within the portfolio.

2021 10 11

2021 08 30

2021 07 19

2021 06 07

2021 04 19

2021 03 01

2021 01 18

2020 12 07

2020 10 26

2020 09 14

2020 08 03

2020 06 22

2020 05 04

2020 03 16

2020 02 03

2019 12 23

2019 11 11

2019 09 30

2019 08 19

2019 07 08

2019 05 20

2019 04 01

2019 02 18

2019 01 07

Backtesting 5/6 US66989HAG39

5

3.75

2.5

1.25

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We calculated the time series of the liquidity score for the individual ISIN within the portfolio.

2021 10 11

2021 08 30

2021 07 19

2021 06 07

2021 04 19

2021 03 01

2021 01 18

2020 12 07

2020 10 26

2020 09 14

2020 08 03

2020 06 22

2020 05 04

2020 03 16

2020 02 03

2019 12 23

2019 11 11

2019 09 30

2019 08 19

2019 07 08

2019 05 20

2019 04 01

2019 02 18

2019 01 07

Backtesting 6/6 XS1147605791

5

3.75

2.5

1.25

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Inhalt

1

Introduction

2

Approach

3

Backtesting

4

About us

19

About us

Analytics tailored for your needs

Our DNA are models and data. The two are inevitably interlinked. We have been working on the full spectrum of models, from pricing to risk, from (re-) insurance to energy trading. We know the data world equally well. We use advanced analytics to explore and understand data to generate actionable insights.

AI/ML driven model validation

Founded in 2019, SigmaQ is an independent specialist lab based in Zurich, Switzerland.

The firm was created with a vision of integrating risk management with modern data science and providing custom advisory services for our clients.

As a competent and reliable partner SigmaQ professionals work side by side with their clients. We follow a goaloriented and entrepreneurial approach.

Our professionals have developed their talents at some of the world’s most prominent financial institutions.

AI/ML driven PD models

Data science R2Python bridges

Credit Risk (PD, LGD, CPM, IFRS9, CECL, IRB, …)

API integration

Stress testing & macro-economic models

Quantitative Analysis Derivatives and structuring

Economic capital

Asset & liability management

We provide a full stack of services covering consulting, technology advisory, advanced data analytics and exploration, and model development and validation.

We have been working for banks (G-SIBS and others), global reinsurers and insurers, leading energy trading companies, leading research laboratories and start-ups in data analytics and artificial intelligence.

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Managing Director & Founder

Dr. Kai Schnee Kai is the Managing Director & Founder of sigmaQ Analytics. He has more than 15 years of experience in the Financial Services Industry with risk management, structuring, portfolio management and consulting experience.

Before founding sigmaQ, he has lead the Swiss legal entity of a quantitative Consulting Boutique for over 4 years. Over the years Kai has been responsible for and successfully completed more than 40 projects for GSIBS, second tier and regional banks, global and regional (Re-)insurers and leading energy traders.

Kai holds a PhD in Physics from ETH Zürich and a Master in Advanced Studies in Quantitative Finance from ETH Zürich/University Zürich.

Kai has held research positions at the California Institute of Technology, Pasadena, and the Erwin Schrödinger Institute in Vienna.

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Contact sigmaQ Analytics GmbH CH 8702 Switzerland T

41 43 543 21 84

M

41 79 263 50 75

contact@sigmaqanalytics.com www.sigmaqanalytics.com

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