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An Empirical Study of the Mexican Banking Systems Network and its Implications for Systemic Risk Martnez Jaramillo, Alexandrova Kabadjova, Bravo Bentez & Solrzano Margain, August/13 ill l d b dj & l

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An Empirical Study of the Mexican Banking System’s Network and its Implications for Systemic Risk

í ill l d b dj í & ló i / Martínez‐Jaramillo, Alexandrova‐Kabadjova, Bravo‐Benítez & Solórzano‐Margain, August/13

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Outline

  • Motivation

Relevant Concepts and literature

  • Data

Data

Interbank exposures’ data

Payment system’s data

  • Topological and other metrics
  • Centrality
  • Summary

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Interconnectedness

  • The GHOS, the oversight body of the BCBS, agreed on a

consultative document setting out measures for G‐SIBs, updated July 2013 http://www.bis.org/press/p130703.htm.

  • The document includes:

h d l f i i i

 methodology for assessing systemic importance  additional required capital  arrangements by which they will be phased in

arrangements by which they will be phased in

  • Objectives:

j

 strengthen the resilience of G‐SIBs  create incentives to reduce systemic importance

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Interconnectedness

  • Assessment methodology based on an indicator‐based

approach:

size i t t d

interconnectedness

lack of substitutability

global (cross‐jurisdictional) activity global (cross jurisdictional) activity

complexity

  • Additional loss absorbency requirements are to be met with a

progressive CET1 ranging from 1% to 2.5%.

  • An additional 1% surcharge could be applied.

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BCBS assessment methodology for G‐SIBs

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Network models and payment systems.

  • Studies describing payment systems around the world:

 Soramki et al. (2006)  Bech & Atalay (2008)  Becher et al. (2008)

Rordam & Bech (2008)

 Rordam & Bech (2008)  Propper et al. (2008)  Wetherilt et al. (2010)

( )

  • Other related works

 Empirical analysis of the Italian interbank market, Iori et al. (2008)

Si l i d l i b k l di d d i I i l

 Simulation to model interbank lending and study contagion, Iori et al.

(2006)

 Coupled stochastic processes, Battiston et al. (2012)  Cascade processes on networks, Lorenz et al. (2009)  Core‐periphery model Craig and von Peter (2010)  DebtRank Battiston et al (2012)  DebtRank, Battiston et al. (2012)

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Data

  • D il d t f

J 2005 d

  • Daily data from January 2005 onwards

 A time window contemplating data from the 3rd of January 2005 to

31st December 2010;

  • I t

b k’ d t

  • Interbank’s data:

 Comprises exposures derived from deposits & loans , derivatives

(counterparty risk), cross holding of securities (issuer risk) and foreign exchange transactions (settlement risk) exchange transactions (settlement risk).

 Three type of networks:

  • Interbank (complete network);

I t b k CLS (FX t ti l d th h CLS b k t

  • Interbank – CLS (FX transactions cleared through CLS bank are not

considered);

  • Interbank – FX (All the FX related exposures are not considered).
  • SPEI’ d t
  • SPEI’s data

Network built accumulating the daily payments between each pair of banks in both directions;

Three types of networks:

Three types of networks:

  • Low value (payments below 10 million MXN);
  • Large value (payments above 10 million MXN);

T t l l ( ll t )

  • Total value (all payments).

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Network measures and systemic risk

  • Topological measures

 Degree  Clustering coefficient  Reciprocity  Affinity  Affinity  Completeness Index

  • Other measures

 Flow  Herfindahl‐Hirschman Index (HHI)  Preference Index

St th

 Strength

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Overview of the networks’ topology

SPEI Interbank Market

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Overview of the networks’ topology

SPEI Interbank Market

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Overview of the networks’ topology

SPEI Interbank Market

  • Both networks present dissasortative mixing; namely, nodes with

high degree have connections to nodes with low degree

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g g g

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Comparison among networks

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Centrality

  • Concept commonly used in social networks
  • Several important interpretations

p p

 power  influence  independence  control

  • Characteristics of a relevant financial institution (Henggeler Müller
  • Characteristics of a relevant financial institution (Henggeler‐Müller

(2006)):

 possesses many linkages to other members (degree)  Amount of assets, liabilities or flow is very large (strength)  its failure could transmit contagion rapidly (closeness)

i i l l ( & k)

 its counterparties are also relevant (eec & pagerank)  there are many paths which passes through it (betweenness)

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Centrality measures

  • Degree centrality

A node is more important if it is connected to many other vertices.

  • Strenght centrality
  • Strenght centrality

A node is important depending on the sum of its interbank assets and liabilities.

  • Betweeness centrality
  • Betweeness centrality

A node with high betweeness centrality can stop or distort the information that passes through it.

  • Closeness centrality
  • Closeness centrality

A node with high centrality would depend less on others but can transmit problems to others easily.

  • Entropic Eigenvector Centrality (Bonacich (1972))
  • Entropic Eigenvector Centrality (Bonacich (1972))

Based on Perron’s eigenvector (ePF);

Considers the relevance of its neighbors.

k li ( l ( ))

  • PageRank centrality (Page et al. (1999)):

Based on the Google’s algorithm;

Considers the relevance of its neighbors.

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A principal components unified measure of centrality

  • Different measures contain relevant information;
  • Preserve most information provided by such measures;
  • From the policy making perspective, it is important to have
  • nly one;
  • Measure of importance enabling to rank vertices.

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Comparison between Ranks

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DebtRank

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Debt rank considers possible cascade effects (contagion) in addition to the network topology.

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PC vs Contagion and Assests ranking (Interbank)

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Centrality and asset size are not the same but centrality and contagion are very similar.

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Congruence

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Congurence: SPEI vs Interbank

Table : 95% confidence intervals for the congruence measures in the interbank exposures and total SPEI networks

20

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Principal Component Ranking SPEI 2005‐ 2010

Total Large Low

27 22 22 27 30 13 14 15 16 17 21 19 20 16 14 15 13 22 21 13 14 16 22 5 5 5 7 8 9 10 11 12 13 5 5 6 7 9 11 12 10 8 13 5 5 5 12 7 8 10 9 11 13 1 2 3 1 2 3 1 2 3 Bank 1 Bank 2 Bank 3 Bank 4 Bank 5 Bank 6 Bank 7 Bank 8 Bank 9 Bank 10 Bank 11 Bank 12 Bank 13 Bank 14 Bank 15 Bank 16 Bank 17 Bank 18 Bank 19

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Overveiw of the SPEI Network

Large value network Low value network 01.03.2005 15.12.2010

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Summary

  • The payments system network is more connected than the interbank

exposures network;

  • Importance (centrality) in the payments network is different than in the

exposures network;

  • The unified centrality measure could be a suitable option for the methodology

proposed by the BCBS to determine G‐SIBs;

  • B

k’ l (i t t d ) h d di th t f

  • Bank’s relevance (interconnectedness) changes depending on the type of

payment (low or large) and depending of they are actina as lenders of borrowers;

  • Bank’s relevance change over time;
  • Determining systemc importance based only on asset’s size could be

Determining systemc importance based only on asset s size could be misleading;

  • Topology of the network is not enough to characterize systemic importance.

p gy g y p

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Future work

  • Network formation models;
  • Studying other financial networks, like the securities settlement network,

the repo network; the repo network;

  • Bank’s behavior in distress;
  • Bank’s funding strategies;
  • Link to economic variables

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Thank you

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