Proceedings of 5th Asia-Pacific Business Research Conference

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Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
Impact of Individual Banking Agents’ Decision Rules on
Interbank Network
Iris Lucas1, Nicolas Schomberg and Vadim Turpyn
Understanding precursors of interbank network stability has become a race
against the clock since financial crisis of 2007 which has opened the doors
to endless quantitative easing policies In order to better understand
interbank network behaviors, a method is proposed to highlight the
structure-property relationships at different scales by using the structure of
an agent-based model in a two-class environment. First class is composed
of existing biggest and systemic banks of the network, the data of which
are collected from their actual balance sheets. To model an actual network
interbank market, a large enough number of fictitious banks smaller than
the ones belonging to first class has been implemented in second class,
and further split for faithful representation of real bank network into two
subgroups of small and medium ones. All banks in the constructed network
have their individual dynamical behavior, but micro data is only considered
for core systemic banks while aggregate values are used for other banks.
The agent-based model is marked by a set of behavioral descriptions:
repayment of matured loans, liquidation of deposits, income from
securities, collection of new deposits, new demands of credit, and
securities sale. The model proposes three kinds of securities with different
levels of risk exposure bought and sold by agents with heterogeneous
liquidity preferences. Central Bank action is designed to support interbank
liquidity according to (simplified) quantitative easing policies developed in
the paper. Applied to the European interbank network, the present cases
introduce different liquidity allocation in kind of securities scenarios and
show that system’s stability is directly related to individual assets portfolio
structure.
JEL Codes: G17, E58 and C13
1. Introduction
In the current post banking and financial crisis situation it is important to evaluate impact of
individual banks behavior on banking system. The presently invested amount in banks by
Central Banks seems to maintain a superficial equilibrium but how long can this last? More
significantly, how can Central Banks reduce and stop their quantitative easing policies
being sure system will not collapse. These questions have lead us to work to better
understand how individual bank‟s investment choices impact on liquidity of interbank
market.
Through Agent-based model approach the paper introduces the results of individual
investment behavior scenarios where banking agents choose different liquidity allocations
between low-risk and high-risk securities.
2. Literature Review
Most empirical economics and financial studies provide or develop a theory rather than a
method. Until recently, authors focused their work on Dynamic Stochastic General
Equilibrium (DSGE) model to analyze impact of financial risks (Yano [1], 2009; Christiano
1
Undergraduate students of Financial Engineering Department, ECE Paris, France. [email protected]
Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
et Al. [2], 2010; Nowobilski [3], 2012; Carrera et Al. [4], 2012). However, one has to
recognize that DSGE model is not the best framework to observe market behavior in
response to different scenarios. Indeed, DSGE models study the impact related to state
deviating from the proposed equilibrium whereas real economy never remains in
equilibrium. Furthermore, in today globalized world one can ask whether a balance
concerning global financial and banking markets could be described. To several authors,
DGSE models do not permit anticipating financial instability because they do not allow
highlighting the interactions between system‟s agents (Bezemer [5], 2011). In fact, the
representation of agents‟ interactions can be compared with emergency propriety of
complex systems (Glattfelder [6], 2012). The concept of emergence supports the idea that
components‟ individual properties do not permit to predict how the whole system will
behave and one has to pay attention to system‟s self-organization. The parallel with
complex systems‟ properties is an important point to raise because this involves economy
and finance studies need new approaches (Bouchaud [7], 2008).
The classical economic theory is based on strong assumptions that have become axioms:
the rationality of economic agents, the invisible hand, etc. This would seem appropriate to
get a sense of trends over time if economy were not marked by cycles and by crises
marked themselves by collective irrationality and panic. One has to recognize that
economy is not an exception to the complex system rule: “although the system behaved
correctly when operating within its design assumptions, small perturbations sometimes led
to the violation of these assumptions, which in turn lead to system-wide failure” (Gribble
[8], 2001). Most empirical studies have dealt on how a sudden failure of an individual
institution turns into systemic risk (Lehar, 2006 [9]; Furfine [10], 2003; Gai et al., 2010 [11],
Ratnovski, 2008 [12] ; Mistrulli, 2011 [13], Cont et al., 2010 [14] Bessis, 2010 [15]) or how
contagion is occurring or how different network structures affect the global level of
systemic risk (Nier and al., 2007 [16]; Battiston et al., 2009 [17]). But, only most
contemporary authors treat the importance of contagion phenomenon (Cont et al., 2010
[14]; Bessis, 2010 [15])
Regulators focus their directives on systemic risks (Basel Committee on Banking
Supervision (BCBS) [18], December 2010). But even though the Bank International for
Settlement (BIS) have made conclusions about subprime crisis‟s factors for defining Basel
III (additional capital buffers, minimum leverage ratio…), measures concerning systemic
risk are not efficient : there can be no real differentiation according to country or
establishments and there is no real requirements based on dynamical interactions of
institutions. Besides, a number of authors are giving thought to the Basel III efficiency and
some of them call into question the true Basel III impact on economy (Dermine [19], 2013;
Went [20], 2010; Allen et Al. [21], 2010).
Recent banking crises have highlighted two major points. First, the possibility of grasping
and controlling the default appears as a very important concern and second, for saving
default banks the Central Banks have been in turn ready to ease their monetary policies.
This last element assumes that banks would pay less attention to liquidity risk. The liquidity
risk already existed before 2007 crisis but the fragility of the financial system state and the
quantitative easing could encourage a lax behavior of banks with regard to liquidity risk. In
the light of the difficult post-crisis years, it is worth understanding and quantifying the
impact of banking institutions‟ behavior on interbank liquidity. Moreover, the study of loss
mechanism related to liquidity risk is important because default risk and insolvency can
result in liquidity crisis.
To date, the literature on liquidity risk has focused on various studies to analyze the
problem with different models. In (Diamond et Al. [22], 1983), the model was the first to
show how banks' mix of illiquid assets and liquid liabilities (deposits which may be
Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
withdrawn at any time) may give rise to panics among depositors where all depositors
“run” to the bank to withdraw their deposits which can lead to a disaster.
As an extension to Diamond‟s model where only one region is considered for the whole
system, we find in (Allen et Al. [23], 2000] a model in which we find four regions (banks are
grouped by geographical investments etc....) with a network of interbank deposits between
regions where the default of a given entity can lead to domino-like series of subsequent
defaults based on exposures to the defaulting entity. Another way to address this problem,
is in (Estrada et Al. [24], 2006) where the model consists of a computer simulation of a
macroeconomic model that captures bank treasury behavior in uncertain environment
surrounding depositors liquidity needs and institution investment possibilities.
The Organization for Economic Co-operation and Development (OECD) has provided a
report demonstrating there is a class of models which allows understand the complicated
dynamics of financial markets including systemic risks: the agent-based models (ABM)
(Thurner [25], 2011). ABMs allow running repeated simulations. By running and analyzing
thousands or millions of scenarios, insight is gained into information about millions of
potential interactions between agents and so about behavior of whole market (such to
assess the impact of new regulators‟ policies on banks activities). The OECD‟s report also
lists limits of ABM: efforts should be undertaken in financial and economic data collection,
unrealistic proposed assumptions in model could lead to unrealistic effects in simulations
results, ABMs should not be expected to predict crises but they may highlight relevant
mechanisms which can give precursors to a crash.
In order to develop the most reliable model as possible, we have chosen to fit actual data
into a model to represent the “systemic” agents of interbank networks and to generate
fictitious balance sheets for the others. Moreover, so as to get trusted results we have
decided to define a simplified model for failing to consider too strong of assumptions which
could lead to skewed results. This choice has also been supported by argument given in
(Lebaron [26], 2005) related to the fact that rules of thumb need to be defined from the
basis of simple behaviors.
3. The Methodology and Model
a. Structure of Network
We suppose the interbank system describable by two classes: a core and a periphery. The
core represents the largest and most systemic institutions or to put the matter differently
their failure can individually turn into system wide failure. The periphery represents the rest
of institutions: medium and small banks which cannot individually create perturbation but
whose set of these banks could do it. In this way, we have chosen to represent agents
belonging to core from actual data extracted out of balance sheets of active banks in
European interbank market, and to generate fictitious and unique balance sheets for the
other agents2.
All agents are represented by simplified balance sheet as below:
Assets
Securities
Loans
Cash
2
Liabilities
Deposit
Borrowings
Equity
Table 1 Simplified Balance Sheet
Fictitious balance sheets are simulated from aggregate values obtain upon the extrapolation of actual data.
Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
We consider three kinds of security
: free risk
, hybrid
and risky
. All of
them present different market risk exposures and so they are remunerated in accordance
with. Furthermore, we assume that a risky asset which needs to be sold immediately will
be sold at 60% of its facial value, a hybrid security will be sold at 80% and a free-risk asset
at 100%.
Loans
contains interbank loans
but also customer loans
. Interbank loans are
dynamic represented throughout simulation whereas customer loans are supposed to be a
random variable remunerated to a rate close to rate for housing loans. Borrowings
are only borrowings contracted on interbank market and deposit represents households
saving. The equity
is only increased by provision for risk that mean 8% of nominal
value for granted loan or 8% of total value of bought securities.
Finally, cash
is inherited cash from previous period plus intraperiod cash flows and is
represented available liquidity. In order to differentiate individual behavior we have chosen
to categorize agents in three different strategies. These strategies reflect the agent‟s
appreciation of liquidity with regard to gain. For the agents who belong to the strategy “free
risk”, they prefer maximize their liquidity instead of their gain, the agents who belong to the
strategy „hybrid” attempt to maximize their gain for a fixed value of liquidity, finally the
agents who belong to the strategy “risky” prefer maximize their gain instead of their
liquidity. Strategies mainly intervene in refinancing process and investment choices.
B. First Step of Refinancing Process
According to the value of
, each agent is declared liquid or illiquid. In the case where
that mean agent cannot meet its commitments and has to refinance itself. In the
second case where
, the agent has the ability to invest. Both in cases, the agent
resonates for ensure its solvability and its futures liquidities. For this happened:
First we allocate a lending capacity to each liquid agent:
(1)
where
is the random component of aggregate portfolio demand,
is the aggregate
value of portfolio demand and
is the portion of random portfolio demand that remains in
bank k.
The refinancing process is marked by the dichotomy between the agents with the seekers
and the investors. To decide the position of each we analyse its cash
and the
weighting of three last available liquidities and the forecast available liquidities in the next
three periods
.
Finally we consider all agents belonging to one of the cases presented below will get in
refinancing process as liquidity seekers:
1.
2.
In case 2, the agent may have two positions in refinancing process: liquidity seeker and
investor. Agents which are liquidity seekers have two possibilities of refinancing: to borrow
or to sell securities. To choose between these two options the agent will decide according
to the decision rule below:
D = Max{P1, P2}
(2)
Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
with P1 = ( S)1, P2 = ( B)1, and where S and B are respectively the sum of all
securities items variation and the sum of all borrowing items variation since the beginning
of simulation. In this way an agent having sold more securities than it has borrowed will
choose to contract a loan to refinance itself, whereas an agent having borrowed more than
it has sold securities will choose to sell its securities. Investor agents will proceed in
opposite way: an agent having bought more securities than it has lent will choose to loan
whereas an agent having lent more than it has bought securities will choose to buy.
Thus to formulate their demand liquidity seekers have to evaluate how much they want to
borrow or they want to sell:
(3)
One all liquidity seekers have formulated their demand; agents who have available liquidity
will select in first all demands which respect the criterions defined in (2) and then they
choose the best transaction(s) according to their strategy and their available liquidity
.
Once seekers and investors no longer agree on the transactions, the refinancing process
between banking agents stops and institutions which still have available liquidity can
choose between preserve a part of their cash for next periods and invest another part by
buying new securities.
c. Central Bank Interventions
When seekers agents could not refund themselves on interbank market they could try to
find liquidity from their central banks. There are two cases:
1) They have enough low-risk securities to borrow at REPO rate from Central Bank
2) They have not enough low-risk securities for buying liquidity and they are thrown
out of the system
An illiquid bank whose securities are pledged makes it a priority to redeem its securities. If
it needs to borrow again before redeeming its securities already pledged, it is thrown out of
the network. Taking into account the short horizon term of present model‟s simulation we
assume all counterparties of ejected agents will not be refunded.
3. The Findings
In [27] it has been observed that differentiation of banks‟ behavior due to strategies deeply
impacts the stability of system. At this moment in our working process, the strategies main
influence the investment choices of agents and that is why we looked if it is rather the
nature of investment or rather the allocation between low-risk and high-risk securities
which causes this instability in the market. The studied scenarios here only impact the
investment choices of agents which still have available liquidity at the end of period.
Directions for further to improve these results will be given in the last section of this
papers.
a. Extreme Scenarios
First we have checked that a case where banks prefer preserve their cash instead of
invest it does not an ideal scenario underlying of the model. The results permit us to be
sure that the interests that the agents of system perceive from their assets are essential,
Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
but also securities are definitively a way for banks to capitalize their liquidity for next
periods. The results (cf. Fig 1) confirm the need of banks to investment for liquidity
requirements.
Figure 1: Case "No Investment" – Percentage of Ejected Agents by Class
We also test the extreme scenario where all banks uniquely decide to invest in risky
securities which offer the highest yield. As seen in (Fig 2), this case is a sure way to see
that the high yield perceived by risky assets could not permit agents to maintain an unsafe
behaviour while paying liquidity at higher prices thanks to their high interests.
Figure 2: Case "Risky Investment Only" – Percentage of Ejected Agents by Class
Finally, given the fact that the model authorizes liquidity seekers to borrow from Central
Bank only in the case where they can provide enough free-risk securities, we have
simulated a scenario where all agents invest their available liquidity in free-risk assets.
However, even if (Fig. 3) shows better results as previously observed, we see in (Fig. 4)
that flow representing agents who stay liquid between two periods is pretty low.
Furthermore, in comparison with the best studied scenario (presented at the end of the
section), we notice not only in (Fig. 4) that the system is in weak state of stability but also
invested liquidity in system by the Central Bank doubled
of monthly monetary
mass brewed in the interbank market.
Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
Figure 3: Case “Free-risk Investment Only” – Percentage of Ejected Agents by Class
Figure 4: Case “Free-risk Investment Only” – Flows of Changing States Agents Between 2 Periods
b. Cushion Investment Scenario
Following the failure of attempt to develop ideal scenario through global threshold values
of ratios low-risk assets holding versus high-risk securities holding for all agents, we have
started thinking that these values of threshold must depend on intrinsic property of agent.
Inspired from the Value-at-Risk concept we observe what is happened when agents try to
maintain a free-risk securities cushion whose value equals to their worst illiquid scenario
they knew since the beginning of simulation. As (Fig. 9 & 10) show, this is the best
scenario with find, and with
of monetary mass invested by Central Banks the
cushion investment policy is the less costly for regulators (N.B : at the end of the
simulation this value tend to
).
Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
Figure 5: Case “Cushion Investment” – Percentage of Ejected Agents by Class
Figure 6: Case “Cushion Investment” – Flows of Changing States Agents Between 2 Periods
5. Summary and Conclusions
From all observed cases, in term of system‟s stability and cost for regulator the Cushion
Investment scenario is the best. But taking into account the result of “Free-risk Investment
Only” case, we suppose this is not just due to the value of cushion but also because by
constituting their free-risk asset cushion the agents increase their low-risk asset holding
ratio versus their high-risk securities ones. That is why an extensive study will be done in
next paper about equilibrium between values of these ratios.
Furthermore, we are willing to expand the strategy to refinancing process choice as
guidance tool for agents.
Finally, as the matter has already been raised in [28], we record in several scenarios that
banks belonging to class B are the most impacted. Preliminary reflections lead us to
believe that they are the one who lend the more in the simulations. That might means the
ability of lend should be reviewed according other criterions.
Proceedings of 5th Asia-Pacific Business Research Conference
17 - 18 February, 2014, Hotel Istana, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-44-3
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