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Mathematical modeling and optimal control of the impact of rumors on the banking crisis

  • Calvin Tadmon and Eric Rostand Njike-Tchaptchet EMAIL logo
Published/Copyright: May 4, 2022
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Abstract

The bank run phenomenon, mostly due to rumor spread about the financial health of given financial institutions, is prejudicious to the stability of financial systems. In this paper, by using the epidemiological approach, we propose a nonlinear model for describing the impact of rumor on the banking crisis spread. We establish conditions under which the crisis dies out or remains permanent. We also solve an optimal control problem focusing on the minimization, at the lowest cost, of the number of stressed banks, as well as the number of banks undergoing the restructuring process. Numerical simulations are performed to illustrate theoretical results obtained.

MSC 2010: 91G80; 34D05; 34H05

1 Introduction

Banking crisis contagion is the situation in which liquidity or insolvency risk is transmitted from one bank to another. Events in the 2007–2009 financial crisis and the recent European sovereign debt crisis highlighted the potential contagion of deposit withdrawals across banks and the resulting implications for financial stability. More often, the bank run phenomenon comes from a propagation of rumors about the financial health of an institution. Financial institutions are often linked to each other through direct portfolio or balance sheet connections. Hence, during a crisis, the failure of one institution can have direct negative payoff effects upon stakeholders of institutions with which it is linked. In the absence of rumors, the transmission of shocks from one financial institution to another occurs through three main channels. (i) Losses from counterparty exposures, where one entity is directly linked to a failing institution due to direct lending [1]; (ii) the inability to roll-over debt and the unavailability of short-term financing [2]; and (iii) portfolio devaluations due to common asset holding [3]. Beyond the transmission channels mentioned earlier, the rumor can trigger a phenomenon of generalized panic, which pushes a large number of depositors to withdraw simultaneously their money from the branches of the incriminated banks. As the results obtained by [4] suggest, the panic-based deposit withdrawals can be strongly contagious across economically related banks.

In this paper, by using an epidemiological approach, we propose a model of banking crisis contagion exacerbated by the bank run phenomenon. Mathematical epidemiology is widely developed, as described in [5,6], and has many applications in various fields of science. For instance, it has been successfully used to explain the diffusion of computer viruses [7], advertising or rumors spread [8,9,10], and spread of a financial distress to the financial system [11,12,13]. To study the crisis spread, we extend the SIR model (susceptible-infected-recovered) proposed in [12] to a SIRS model (susceptible-infected-recovered-susceptible), in which susceptibles are healthy banks, infected are banks in distress, and removed are banks undergoing the restructuring process. In our model, we take into account the fact that bank failure is followed by some legislation issues. Some issues relate to the interests of savers who will not always be guaranteed to be reimbursed up to their deposits. Other issues concern the fate of the failing bank. In some countries, there are procedures to rescue the bankrupt banks, which may lead to the resumption of activities [14,15]. We also assume that stress spreads in the financial system via the interbank network and only a contract with distressed banks can lead to stress of a healthy bank. Banks undergoing the restructuring process are out of the interbank market and cannot contribute to the spread of stress. Beyond the epidemiological approach, there are other ways to investigate the banking crisis problem. Of great relevance are those referred to as network-based studies, which mainly rely on the network structure of the banking system. These have been largely dealt with in [16,17, 18,19,20, 21,22]. Network-based studies explicitly model the links between banks by trying to micro-found their behaviors and their interactions, whereas epidemiological studies take an aggregative approach to model the interactions between specific bank subgroups without modeling the behavior of any individual bank. In the epidemiological case, the simplification of the structure of the banking industry allows to derive analytical results that provide a neat characterization of the possible outcomes.

To model the spread of rumors, we use the same approach proposed in [10] in which we assume that the rumor on the distress of given banks is a temporary phenomenon and fades quite quickly; therefore, we neglect recruitment and death in the population of ignorant individuals, spreaders and stiflers. Due to the importance of the banking system on the stability of the entire financial system, the central bank of the country where stressed banks are located can intervene to reduce the impact of the financial crisis. It was the case during the 2007–2009 financial crisis. In this paper, we propose an optimal control problem focusing on the minimization, at the lowest cost, of the number of stressed banks, as well as the number of banks undergoing the restructuring process. Our contribution to the literature on banking crisis spread is two folds: (1) the mathematical study of the impact of rumors on the banking crisis spread (to the best of our knowledge, such a research work does not exist in the literature); (2) the optimal control of the model that we propose can be useful for regulatory authorities to contain the propagation of the crisis.

The remainder of this paper is structured as follows. In Section 2, we present the model under appropriate hypotheses and make its mathematical analysis. In Section 3, we propose an optimal control problem associated with the model and prove the existence and uniqueness of the optimal solution. Section 4 is devoted to numerical simulations in order to illustrate the theoretical results obtained. In Section 5, we give the conclusion and perspectives pertaining to this work.

2 The model and its analysis

2.1 The mathematical model of rumor spread

To model the spread of rumors, we use the same approach as in [10] except that we do not include recruitment and death. This is because in our approach, we assume that the rumors about the financial health of some given banks are often temporary phenomena. The population of human being is subdivided into three categories: ignorant individuals ( S ) , spreaders ( I ) , and stiflers ( R ) with numbers at time t 0 denoted by S ( t ) , I ( t ) , and R ( t ) , respectively. The rumor spreads in the population through contact between ignorant individuals and spreaders. We assume that the number of random contacts per unit of time between ignorant individuals and spreaders is S ( t ) I ( t ) .

Once the rumor spreads in a social network, the government then has the authority to use inhibiting and adjusting mechanisms, whose strength and resources are quantified through the use of the inhibitor variable U , to contain the various damages caused by the rumor. We denote by Γ the allotted budgeting rate by the government for adjusting and inhibiting mechanisms and by e the decay rate of those mechanisms. Let U ( t ) be the level of the inhibitor variable at time t 0 .

The dynamics of U ( t ) is given by the following ordinary differential equation (ODE):

(1) U ˙ ( t ) = Γ e U ( t ) δ 1 I ( t ) U ( t ) K + U ( t ) .

The positive numbers δ 1 and K quantify the usage of the inhibitor, δ 1 being the maximal uptake rate of I , and K being a half saturation parameter. The resulting uptake rate of the inhibitor δ 1 I ( t ) U ( t ) K + U ( t ) is increasing (the more the inhibitor is available, the higher the uptake rate is) and saturates for large U .

After hearing a rumor on the financial health of some given banks, the ignorant individuals, who have no previous information about that rumor may subsequently display two different attitudes. Specifically, some individuals will choose not to spread the rumor or not to believe it (stiflers), while other individuals will actively spread it (spreaders). We denote by θ 1 the probability that an ignorant individual becomes a spreader, and by 1 θ 1 , the probability that he (she) becomes a stifler. At any time, the government’s actions allow to attenuate the influence of spreaders on ignorant individuals. The effect of the attenuation mechanism is measured by a C 1 function f : [ 0 , ) ( 0 , 1 ] satisfying

(2) f ( 0 ) = 1 and f ( U ) 0 .

The assumption f ( 0 ) = 1 reflects the fact that if the government does not take any action to limit the rumor spread, then the influence of spreaders on ignorant individuals will not be reduced. The assumption f ( U ) 0 represents the fact that the greater the attenuation effect is, the higher the inhibition variable U is.

The dynamics of S ( t ) is governed by the following ODE:

(3) S ˙ ( t ) = β 1 S ( t ) I ( t ) f ( U ( t ) ) ,

where β 1 is the influence rate of spreaders on ignorant individuals.

At any time, as a result of government action to improve the mechanisms for clarifying and inhibiting rumors, some spreaders adjust their attitude toward spreading rumors and thereby becoming stiflers at a rate g ( U ) . We assume that the function g : [ 0 , ) [ 0 , 1 ) is a C 1 function satisfying

(4) g ( 0 ) 0 and g ( U ) 0 .

The assumption g ( 0 ) 0 represents the fact that the use of rumor control mechanisms does indeed change the attitude of spreaders. g ( U ) 0 means that a large inhibitor U leads to more spreaders changing their attitudes. From the aforementioned assumptions, the respective dynamics of I ( t ) and R ( t ) are given by the following nonlinear ODEs:

(5) I ˙ ( t ) = θ 1 β 1 S ( t ) I ( t ) f ( U ( t ) ) g ( U ( t ) ) I ( t )

and

(6) R ˙ ( t ) = ( 1 θ 1 ) β 1 S ( t ) I ( t ) f ( U ( t ) ) + g ( U ( t ) ) I ( t ) .

2.2 The mathematical model of the impact of rumor on banking crisis spread

We assume that we are given a local banking system, under the authority and the supervision of the local central bank, where each bank can belong to only one of the following classes: healthy banks ( H ) , distressed banks ( D ) , and banks in crisis ( C ) with numbers at time t 0 denoted by H ( t ) , D ( t ) , and C ( t ) , respectively. Healthy banks are those with positive indicators in accordance with banking authority regulator. Distressed banks are those suffering of losses due to default of a counterparty in loan’s portfolio, losses coming from the insufficiency of liquidity necessary to pursue the financial intermediation process, losses due to earlier selling of owned securities, and losses coming from bank runs. Banks in crisis are those that didn’t survive from a distressed period and are under temporary administration or undergoing the restructuring process. We assume that stress spreads in the banking system via the interbank network and only a contract with distressed banks can lead to a stress of a healthy bank. That is, banks undergoing restructuring process are out of the interbank market and cannot contribute to the spread of stress. To measure the impact of rumor spread on the banking crisis, we assume that a fraction of those who decide to spread the rumor (spreaders) will withdraw their money in incriminated banks. In what follows, we describe the dynamics of H ( t ) , D ( t ) , and C ( t ) .

2.2.1 The rate of change of the number of healthy banks H ( t )

It depends on four factors:

  • The number of banks created and the proportion of healthy banks that merged per unit of time, modeled by Λ ( t ) and a ( t ) , respectively. We assume that

    1. The functions t Λ ( t ) and t a ( t ) are nonnegative, bounded, and continuous on [ 0 , ) .

    2. There exist real constants ω 1 , ω 2 > 0 such that

      lim inf t t t + ω 1 a ( s ) d s > 0 and lim inf t t t + ω 2 Λ ( s ) d s > 0 .

    3. There exist a real constant ω 3 > 0 such that lim inf t t t + ω 3 β ( s ) d s > 0 .

  • The number of healthy banks that become distressed per unit of time β ( I ( t ) ) H ( t ) D ( t ) . Here, the number of random matchings (contracts) per unit of time between healthy and distressed banks is H ( t ) D ( t ) and β ( I ( t ) ) is the contagious rate of stressed banks at time t . We also assume that the function β : [ 0 , ) R is a C 1 function of I and satisfies the following conditions:

    1. β ( 0 ) > 0 , meaning that in the absence of rumors spreaders, there is a possibility of stress contagion coming from other channels of banking crisis spread as losses from counterparty exposures or the inability to roll-over debt and the unavailability of short-term financing.

    2. β ( I ) I > 0 . That is, the infectious rate of stressed banks increases with the number of rumors’ spreaders. This can be interpreted by the fact that if there are more spreaders, each of them can instantly decide to withdraw their money from the banks that are subject to rumors about their financial health. The excessive demand for funds coming from these depositors may drag initially healthy banks into stress.

    3. lim sup I β ( I ) < . The infectious rate of stressed banks is bounded.

  • The number of recovered banks per unit of time δ ( I ( t ) ) D ( t ) . In fact, after a period of stress a bank can equilibrate its ratios and becomes healthy at rate δ ( I ( t ) ) . We assume that the function δ : [ 0 , ) R is a C 1 function of I and satisfies the following conditions:

    1. δ ( 0 ) > 0 . That is, in the absence of rumors spreaders, the per capita rate of leaving the distress stage doesn’t vanish.

    2. δ ( I ) I < 0 . That is, the average time spent in distress stage before recovering healthy one is an increasing function of I . This can be interpreted by the fact that if some spreaders decide to withdraw their money from a given bank that is subject to rumors about their financial health, then its recovery can take more time.

    3. δ ( I ) ( 0 , 1 ) for all I 0 .

  • The number of rehabilitated banks per unit of time θ ( I ( t ) ) C ( t ) . After the restructuring period, bank can become healthy and reintegrates the banking system at rate θ ( I ( t ) ) . We assume that the function θ : [ 0 , ) R is a C 1 function of I and satisfies the following conditions:

    1. θ ( 0 ) > 0 . That is, in the absence of rumors spreaders, the per capita rate of leaving the restructuring stage to healthy one doesn’t vanish.

    2. θ ( I ) I < 0 . That is, the average time spent in the restructuring stage before recovering healthy one is an increasing function of I . This can be interpreted by the fact that if some spreaders decide to withdraw their money from a given bank that is subject to rumors about their financial health, damages on its balance sheet can be such that it necessitates more time spent in the restructuring process to recover the healthy stage.

    3. θ ( I ) ( 0 , 1 ) for all I 0 .

The total change of the number of healthy banks per unit of time is summarized as follows:

(7) H ˙ ( t ) = Λ ( t ) β ( I ( t ) ) H ( t ) D ( t ) a ( t ) H ( t ) + δ ( I ( t ) ) D ( t ) + θ ( I ( t ) ) C ( t ) .

Remark 2.1

The function t a ( t ) is such that there are real constants a 1 , a 2 > 0 , and t > 0 sufficiently large such that, for all t t 0 t , we have

t 0 t a ( s ) d s a 1 ( t t 0 ) a 2 .

In fact, by ( H 2 ) , there are real constants a > 0 and t such that 0 t + ω 1 a ( s ) d s 1 2 a ω 1 for t t . Given t 0 t , we have

t 0 t + ω 1 a ( s ) d s t 0 t 0 + t t 0 ω 1 ω 1 a ( s ) d s 1 2 a ω 1 t t 0 1 2 a ω 1 t t 0 ω 1 1 1 2 a ( t t 0 ) 1 2 a ω 1 ,

where for x R , x is the floor of x . Set a 1 = 1 2 a and a 2 = 1 2 a ω 1 .

2.2.2 The rate of change of the number of distressed banks D ( t )

It depends on four factors:

  • The number of healthy banks that become distressed per unit of time β ( I ( t ) ) H ( t ) D ( t ) .

  • The fraction δ ( I ( t ) ) D ( t ) of distressed banks that recover their stability per unit of time.

  • The fraction γ ( I ( t ) ) D ( t ) of distressed banks that didn’t survive after a period of stress and are placed under temporary administration. We assume that the function γ : [ 0 , ) R is a C 1 function of I and satisfies the following conditions:

    1. γ ( 0 ) > 0 . That is, in the absence of rumors spreaders, the per capita rate of leaving the distress stage to restructuring one doesn’t vanish.

    2. γ ( I ) I > 0 . That is, the average time spent in the distress stage before leaving to restructuring one is a decreasing function of I . This can be interpreted by the fact that if some spreaders decide to withdraw their money from a given bank that is subject to rumors about its financial health, damages on its balance sheet can be such that after a short period of time, it is admitted in the restructuring stage.

    3. γ ( I ) ( 0 , 1 ) for all I 0 .

  • The fraction μ ( I ( t ) ) D ( t ) of distressed banks that close or are admitted to liquidation due to bank run phenomena, per unit of time. We assume that the function μ : [ 0 , ) R is a C 1 function of I and satisfies the following conditions:

    1. μ ( 0 ) > 0 . That is, in the absence of rumors spreaders, the per capita rate of leaving the distress or restructuring classes to the liquidation stage doesn’t vanish.

    2. μ ( I ) I > 0 . That is, the average time spent in distress or restructuring stages before leaving to liquidation one is a decreasing function of I . This can be interpreted by the fact that if some spreaders decide to withdraw their money from a given bank that is subject to rumors about its financial health, damages on its balance sheet can be such that after a small period of time, it is admitted to liquidation.

    3. μ ( I ) ( 0 , 1 ) for all I 0 .

The total change in the number of distressed banks per unit of time is summarized as follows:

(8) D ˙ ( t ) = β ( I ( t ) ) H ( t ) D ( t ) ( δ ( I ( t ) ) + μ ( I ( t ) ) + γ ( I ( t ) ) ) D ( t ) .

2.2.3 The rate of change of the number of banks in crisis C ( t )

It depends on three factors:

  • The fraction γ ( I ( t ) ) D ( t ) of distressed banks that didn’t survive after a period of stress and are put under temporary administration, per unit of time.

  • The number of rehabilitated banks per unit of time θ ( I ( t ) ) C ( t ) .

  • The fraction μ ( I ( t ) ) C ( t ) of banks in crisis that didn’t survive after the restructuring process and are definitely removed from the banking system, per unit of time.

The total change of the number of banks in crisis per unit of time is summarized as follows:

(9) C ˙ ( t ) = γ ( I ( t ) ) D ( t ) ( θ ( I ( t ) ) + μ ( I ( t ) ) ) C ( t ) .

Putting together equations (1), (3), (5), (6), (7), (8), and (9), we obtain the following system of ODEs modeling the impact of rumors spread on the banking crisis.

(10) H ˙ ( t ) = Λ ( t ) β ( I ( t ) ) H ( t ) D ( t ) a ( t ) H ( t ) + δ ( I ( t ) ) D ( t ) + θ ( I ( t ) ) C ( t ) , D ˙ ( t ) = β ( I ( t ) ) H ( t ) D ( t ) ( δ ( I ( t ) ) + μ ( I ( t ) ) + γ ( I ( t ) ) ) D ( t ) , C ˙ ( t ) = γ ( I ( t ) ) D ( t ) ( θ ( I ( t ) ) + μ ( I ( t ) ) ) C ( t ) , S ˙ ( t ) = β 1 S ( t ) I ( t ) f ( U ( t ) ) , I ˙ ( t ) = θ 1 β 1 S ( t ) I ( t ) f ( U ( t ) ) g ( U ( t ) ) I ( t ) , U ˙ ( t ) = Γ e U ( t ) δ 1 I ( t ) U ( t ) K + U ( t ) , R ˙ ( t ) = ( 1 θ 1 ) β 1 S ( t ) I ( t ) f ( U ( t ) ) + g ( U ( t ) ) I ( t ) .

2.3 The mathematical analysis of the model

The Model (10) can be split into two submodels. The submodel of rumor spread and that of banking crisis spread given the number of spreaders ( I ) . In fact, the last fourth equations of (10) do not depend on the variables H , D , and C . The submodel of rumor spread is given by the following system of ODEs:

(11) S ˙ ( t ) = β 1 S ( t ) I ( t ) f ( U ( t ) ) , I ˙ ( t ) = θ 1 β 1 S ( t ) I ( t ) f ( U ( t ) ) g ( U ( t ) ) I ( t ) , U ˙ ( t ) = Γ e U ( t ) δ 1 I ( t ) U ( t ) K + U ( t ) , R ˙ ( t ) = ( 1 θ 1 ) β 1 S ( t ) I ( t ) f ( U ( t ) ) + g ( U ( t ) ) I ( t ) .

Given a solution ( S ( t ) , I ( t ) , U ( t ) , and R ( t ) ) for the submodel (11), the functions β ( I ( t ) ) , δ ( I ( t ) ) , θ ( I ( t ) ) , γ ( I ( t ) ) , and μ ( I ( t ) ) in the first three equations of (10) become functions of time and we obtain the following (nonautonomous) model for the dynamics of banking crisis spread:

(12) H ˙ ( t ) = Λ ( t ) β ( t ) H ( t ) D ( t ) a ( t ) H ( t ) + δ ( t ) D ( t ) + θ ( t ) C ( t ) , D ˙ ( t ) = β ( t ) H ( t ) D ( t ) ( μ ( t ) + δ ( t ) + γ ( t ) ) D ( t ) , C ˙ ( t ) = γ ( t ) D ( t ) ( μ ( t ) + θ ( t ) ) C ( t ) .

The following Proposition is about the positivity, the boundedness, and the global existence of a solution to (11).

Proposition 2.1

Let S 0 , I 0 , U 0 , and R 0 be given initial conditions such that S 0 0 , I 0 0 , U 0 0 , and R 0 0 .

  1. A solution of (11) remains positive for all t [ 0 , t f ) , where t f ( 0 , ) .

  2. The set

    Ω = ( S , I , U , R ) R 4 : 0 S N 0 , 0 I N 0 , 0 R N 0 , 0 U Γ e

    is positively invariant for system (11). Here, N 0 = S 0 + I 0 + R 0 .

  3. The solution of (11) is unique and is defined for all t 0 .

Proof

Let S 0 , I 0 , U 0 , and R 0 be given initial conditions such that S 0 0 , I 0 0 , U 0 0 and R 0 0 .

  1. Let t 0 . From the first equation of (11), we have

    S ( t ) = S 0 exp 0 t β 1 I ( s ) f ( U ( s ) ) d s 0 .

    From the second equation of (11), we have

    I ( t ) = I 0 exp 0 t ( θ 1 β 1 S ( s ) f ( U ( s ) ) g ( U ( s ) ) ) d s 0 .

    By using the third equation of (11), we obtain

    U ( t ) = U 0 exp e t + 0 t δ 1 I ( s ) K + U ( s ) d s + Γ 0 t exp e ( s t ) + t s δ 1 I ( u ) K + U ( u ) d u d s 0 .

    Using assumptions (2) and (4), and the positivity of S ( t ) and I ( t ) , we deduce the positivity of R ( t ) .

  2. Let t 0 . Adding the right-hand side of the first, second, and fourth equation of (11) leads to

    S ˙ ( t ) + I ˙ ( t ) + R ˙ ( t ) = 0 .

    That is, S ( t ) + I ( t ) + R ( t ) = S 0 + I 0 + R 0 .

    From the positivity of S ( t ) , I ( t ) , and R ( t ) , we deduce that

    0 S ( t ) S 0 + I 0 + R 0 , 0 I ( t ) S 0 + I 0 + R 0 and 0 R ( t ) S 0 + I 0 + R 0 .

    By using the third equation of (11) and the positivity of U ( t ) and I ( t ) , we get

    (13) U ˙ ( t ) Γ e U ( t ) .

    Thus, by using the standard comparison theorem [25] leads to

    U ( t ) Γ e + Γ e U 0 e e t .

    Hence, if U 0 Γ e , then lim sup t U ( t ) Γ e . That is, U ( t ) Γ e for all t 0 .

  3. Consider the function Ψ defined from R + 4 to R 4 by

    Ψ ( S , I , U , R ) = β 1 S I f ( U ) , θ 1 β 1 S I f ( U ) g ( U ) I , Γ e U ( t ) δ 1 I U K + U , ( 1 θ 1 ) β 1 S I f ( U ) + g ( U ) I ,

    where R + 4 = { ( X , Y , W , Z ) R 4 : X 0 , Y 0 , W 0 , Z 0 } .

    The function Ψ is continuously differentiable on R + 4 and therefore is locally Lipschitz. Using the Cauchy-Lipschitz theorem [23], the Cauchy problem for system (11) admits a unique local solution. From item (ii) of Proposition 2.1, the solution is contained in a compact subset of R 4 . Therefore, it is globally defined.□

It is straightforward that system (11) admits two equilibria E 0 = 0 , 0 , Γ e , 0 and E = 0 , 0 , Γ e , N 0 . The equilibrium E 0 is not realistic as it means a total absence of human beings.

The following Proposition is about the stability of the equilibrium E .

Proposition 2.2

The equilibrium point E = 0 , 0 , Γ e , N 0 , of system (11), is globally asymptotically stable in Ω .

Proof

Let ( S ( t ) , I ( t ) , U ( t ) , and R ( t ) ) be any positive solution of system (11) in Ω . Recall that N 0 = S 0 + I 0 + R 0 . Define the following Lyapunov function:

(14) L ( t ) = S ( t ) + I ( t ) + N 0 R ( t ) N 0 1 ln R ( t ) N 0 .

Clearly, the function L is nonnegative definite in Ω with respect to E . Calculating the time derivative of the function L along the solution of system (11), we obtain

d L ( t ) d t = d S ( t ) d t + d I ( t ) d t + 1 N 0 R d R ( t ) d t = β 1 S ( t ) I ( t ) f ( U ( t ) ) + θ 1 β 1 S ( t ) I ( t ) f ( U ( t ) ) g ( U ( t ) ) I ( t ) + 1 N 0 R [ ( 1 θ 1 ) β 1 S ( t ) I ( t ) f ( U ( t ) ) + g ( U ( t ) ) I ( t ) ] = N 0 R [ ( 1 θ 1 ) β 1 S ( t ) I ( t ) f ( U ( t ) ) + g ( U ( t ) ) I ( t ) ] .

Since θ 1 1 , then d L ( t ) d t 0 , for all S , I , R > 0 , and d L ( t ) d t = 0 holds if and only if S = 0 , I = 0 and R = N 0 . Thus, L is a Lyapunov function on Ω . So, by LaSalle’s invariance principle [24], it follows that

(15) lim t ( S ( t ) , I ( t ) , R ( t ) ) = ( 0 , 0 , N 0 ) .

It follows from the third equation of (11) that

U ˙ ( t ) Γ e U ( t ) ,

and so, by the comparison theorem [25],

(16) U = lim sup t U ( t ) Γ e .

Now, from (15), we have lim sup t I ( t ) = 0 . This implies that for a sufficiently small ε > 0 , there exists a constant P 1 > 0 , such that lim sup t I ( t ) ε , for all t > P 1 .

Thus, from the third equation of system (11), it follows that, for t > P 1 ,

d U ( t ) d t Γ ( δ 1 ε + e ) U ( t ) .

Thus, by using the standard comparison theorem [25], we obtain

(17) U ( t ) U ( 0 ) e ( δ 1 ε + e ) t + Γ δ 1 ε + e ( 1 e ( δ 1 ε + e ) t ) .

From (17), we obtain

(18) U = lim inf t U ( t ) Γ δ 1 ε + e ,

so that, by letting ε 0 , in (18), we obtain

(19) U = lim inf t U ( t ) Γ e .

It then follows from (16) and (19) that

U Γ e U .

This infers that

(20) lim t U ( t ) = Γ e .

Thus, we have from (15) and (20) that,

lim t ( S ( t ) , I ( t ) , U ( t ) , R ( t ) ) = 0 , 0 , Γ e , N 0 .

Moreover, Ω is an invariant and attracting set of R + 4 . It follows that the largest compact invariant subset in { ( S , I , U , R ) Ω : d L d t = 0 } is the singleton { E } . So, by LaSalle’s invariance principle [24], it follows that every solution of system (11), with initial conditions in R + 4 approaches the equilibrium point { E } as t . This completes the proof.□

We have the following important result about the existence, uniqueness, positivity, and boundedness for the solution to problem (12).

Lemma 2.1

  1. Let t 0 0 . For the given initial conditions H t 0 , D t 0 and C t 0 , the system (12) admits a unique maximal solution defined on an open interval ( t 0 ξ , t 0 + ξ ) , where ξ is a positive real number. This solution is a continuous function of time.

  2. All solutions ( H ( t ) , D ( t ) , and C ( t ) ) of (12) with H t 0 0 , D t 0 0 , and C t 0 0 verify H ( t ) 0 , D ( t ) 0 , and C ( t ) 0 for all t t 0 .

  3. Let N ( t ) = H ( t ) + D ( t ) + C ( t ) , where ( H ( t ) , D ( t ) , and C ( t ) ) is a solution of (12) with nonnegative initial conditions. Then, there are constants m and M such that

    (21) 0 < m lim inf t N ( t ) lim sup t N ( t ) M < .

  4. Given initial conditions H t 0 , D t 0 , and C t 0 , the solution of system (12) is defined on [ t 0 , ) .

  5. There are constant k and T such that, if D ( t ) < ν for t > T , then

    (22) C ( t ) < k ν

    for t sufficiently large.

Proof

  1. Let X = ( H , D , C ) . System (12) can be rewritten as X ˙ ( t ) = h ( t , X ( t ) ) , where h is the function defined on [ 0 , ) × R 3 by

    (23) h ( t , H , D , C ) = Λ ( t ) β ( t ) H D a ( t ) H + δ ( t ) D + θ ( t ) C β ( t ) H D ( μ ( t ) δ ( t ) + γ ( t ) ) D γ ( t ) D ( μ ( t ) + θ ( t ) ) C .

    The function h is continuously differential on R 3 with respect to the variable ( H , D , C ) and therefore is locally Lipschitz. By using the Cauchy-Liptschitz theorem [23], we obtain the existence and uniqueness of a local solution for problem (12) for given initial conditions.

  2. Let t 0 0 . We assume that H t 0 0 , D t 0 0 and C t 0 0 . From the second equation of (12), we have

    D ( t ) = D t 0 exp t 0 t ( β ( s ) H ( s ) ( δ ( t ) + μ ( t ) + γ ( s ) ) ) d s ,

    that is, D ( t ) assumes only nonnegative values.

    From the third equation of (12), we have

    d d t C ( t ) exp t 0 t ( μ ( s ) + θ ( s ) ) d s = exp t 0 t ( μ ( s ) + θ ( s ) ) d s D ( t ) > 0 ,

    which implies that t C ( t ) exp t 0 t ( μ ( s ) + θ ( s ) ) d s is an increasing function of t , and therefore, C ( t ) 0 .

    From the first equation of (12), we have

    H ˙ ( t ) H = 0 = Λ ( t ) + δ ( t ) D ( t ) + θ ( t ) C ( t ) 0 ,

    that is, H ( t ) cannot assumes negative values.

  3. Adding the three equations of system (12), we have

    H ˙ ( t ) + D ˙ ( t ) + C ˙ ( t ) = Λ ( t ) a ( t ) H ( t ) μ ( t ) D ( t ) μ ( t ) C ( t ) .

    From a ( t ) μ ( t ) for all t 0 , we have

    (24) Λ ( t ) μ ( t ) N ( t ) N ˙ ( t ) Λ ( t ) a ( t ) N ( t ) .

    Let t N ( t ) be a solution to the ODE

    (25) N ˙ ( t ) = Λ ( t ) a ( t ) N ( t )

    with initial condition N ( t 0 ) > 0 , t 0 0 . We have

    N ( t ) = N ( t 0 ) e t 0 t a ( s ) d s + t 0 t e u t a ( s ) d s Λ ( u ) d u .

    Since t Λ ( t ) is bounded, by ( H 2 ) , we obtain for t 0 sufficiently large and t t 0 ,

    N ( t ) N ( t 0 ) e a 1 ( t t 0 ) + a 2 + t 0 t e a 1 ( t u ) + a 2 Λ ( u ) d u < N ( t 0 ) e a 1 ( t t 0 ) + a 2 + e a 2 a 1 sup t t 0 Λ ( t ) ( 1 e a 1 ( t t 0 ) ) ,

    and therefore, lim sup t N ( t ) e a 2 a 1 sup t t 0 Λ ( t ) ( 1 e a 1 ( t t 0 ) )

    Let t N ( t ) be a solution to the ODE

    (26) N ˙ ( t ) = Λ ( t ) μ ( t ) N ( t )

    with initial condition N ( t 0 ) > 0 , t 0 0 . We have

    (27) N ( t ) = N ( t 0 ) e t 0 t μ ( s ) d s + t 0 t e u t μ ( s ) d s Λ ( u ) d u .

    Since t μ ( t ) is bounded, by (27) and ( H 2 ) , we obtain for t sufficiently large

    N ( t ) = N ( t ω 2 ) e t ω 2 t μ ( s ) d s + t ω 2 t e u t μ ( s ) d s Λ ( u ) d u > e ω 2 sup t 0 μ ( s ) t ω 2 t Λ ( u ) d u Λ ω 2 e ω 2 sup t 0 μ ( s ) ,

    where Λ > 0 is a real number such that t t + ω 2 Λ ( s ) d s > Λ for t sufficiently large. Therefore, lim inf t N ( t ) Λ ω 2 e ω 2 sup t 0 μ ( s ) . By using the comparison property together with the solutions of (25) and (26), we deduce that

    t t 0 , N ( t ) N ( t ) N ( t )

    and

    m lim inf t N ( t ) lim inf t N ( t ) lim sup t N ( t ) lim sup t N ( t ) M ,

    where m = Λ ω 2 e ω 2 sup t 0 μ ( s ) and M = e a 2 a 1 sup t t 0 Λ ( t ) .

  4. Let H t 0 , D t 0 , and C t 0 be initial conditions for system (12). From (iii), the solution ( H ( t ) , D ( t ) , C ( t ) ) of (12) is contained in a compact subset of R 3 and, therefore, is globally defined.

  5. Assume that D ( t ) < k for all t > T and some k > 0 . From the third equation of (12), we have for t > T ,

    (28) C ˙ ( t ) < γ ( t ) k ( μ ( t ) + θ ( t ) ) C ( t ) .

    From the assumptions on the functions γ ( t ) , μ ( t ) and θ ( t ) , there exist real numbers γ i , γ s , μ i , μ s , θ i , θ f ( 0 , 1 ) such that

    (29) γ i γ ( t ) γ s , μ i μ ( t ) μ s , θ i θ ( t ) θ s .

    By using (29), (28) becomes C ˙ ( t ) < γ s k ( μ i + θ i ) C ( t ) . That is,

    lim sup t C ( t ) < γ s k μ i + θ i and C ( t ) < γ s k μ i + θ i

    for t sufficiently large. The proof of Lemma 2.1 is completed.□

To study the asymptotic behavior of model (12), consider the following ODE:

(30) w ˙ ( t ) = Λ ( t ) a ( t ) w ( t ) .

For each solution w ( t ) of (30), define the function:

(31) b ( t , w ( t ) ) = β ( t ) w ( t ) ( μ ( t ) + γ ( t ) + δ ( t ) ) .

We have the following result.

Proposition 2.3

Let t 0 0 .

  1. All solutions w ( t ) of (30) with initial condition w ( t 0 ) > 0 are positive for all t > t 0 .

  2. If w ( t ) is a solution of (30) with initial condition w ( t 0 ) > 0 , then there are constants M 1 > 0 and t 1 t 0 such that w ( t ) M 1 for all t t 1 .

  3. Each solution w ( t ) of (30) with initial condition w ( t 0 ) > 0 is bounded and globally uniformly attractive on [ 0 , ) .

  4. If w ( t ) is a solution of (30) and w ˜ ( t ) is a solution to the ODE

    (32) w ˙ ( t ) = Λ ( t ) a ( t ) w ( t ) + h ( t )

    with w 0 = w ˜ 0 for some bounded function h , then there exists M 2 0 such that

    sup t t 0 w ( t ) w ˜ ( t ) M 2 sup t t 0 h ( t ) .

  5. The real numbers

    (33) s 1 ( ω ) = lim inf t t t + ω b ( s , w ( s ) ) d s

    and

    (34) s 2 ( ω ) = lim sup t t t + ω b ( s , w ( s ) ) d s ,

    where ω ( 0 , ) do not depend on the solution w ( t ) of (30).

Proof

  1. Given t 0 0 , the solution of (30) with initial condition w ( t 0 ) is

    w ( t ) = w ( t 0 ) e t 0 t a ( s ) d s + t 0 t e u t a ( s ) d s Λ ( u ) d u ,

    and thus, since Λ ( t ) 0 for all t 0 , if w ( t 0 ) > 0 , we obtain w ( t ) > 0 for all t t 0 .

  2. The solution of (30) with initial condition w ( t 0 ) > 0 is

    w ( t ) = w ( t 0 ) e t 0 t a ( s ) d s + t 0 t e u t a ( s ) d s Λ ( u ) d u .

    By ( H 2 ) , since t a ( t ) is bounded, we obtain for t sufficiently large

    w ( t ) = w ( t ω 2 ) e t ω 2 t a ( s ) d s + t ω 2 t e u t a ( s ) d s Λ ( u ) d u > e ω 2 sup t 0 a ( s ) t ω 2 t Λ ( u ) d u Λ ω 2 e ω 2 sup t 0 a ( s ) ,

    and item (ii) of Proposition 2.3 follows.

  3. If w ( t ) and w 1 ( t ) are two solutions of (30) with initial condition w ( t 0 ) > 0 and w 1 ( t 0 ) > 0 , respectively, we have

    w ( t ) w 1 ( t ) = w ( t 0 ) w 1 ( t 0 ) e t 0 t a ( s ) d s ,

    and thus, w ( t ) w 1 ( t ) 0 as t and item (iii) of Proposition 2.3 follows.

  4. If w ( t ) is a solution of (30) and w ˜ ( t ) is a solution of (32), with w ( t 0 ) = w ˜ ( t 0 ) , then, setting z ( t ) = w ˜ ( t ) w ( t ) , we obtain the Cauchy problem:

    (35) z ˙ ( t ) = a z ( t ) + h ( t ) , z ( t 0 ) = 0 .

    The solution of (35) is z ( t ) = t 0 t e u t a ( s ) d s h ( u ) d u . Hence,

    z ( t ) t 0 t e u t a ( s ) d s h ( u ) d u sup t t 0 h ( t ) t 0 t e u t a ( s ) d s d u sup t t 0 h ( t ) t 0 t e a 1 ( t u ) + a 2 d u = e a 2 a 1 sup t t 0 h ( t ) ( 1 e a 1 ( t t 0 ) ) e a 2 a 1 sup t t 0 h ( t ) ,

    for t 0 sufficiently large and t t 0 .

  5. Let w ( t ) and w ( t ) be solutions of (30). By the attractiveness of solutions of (30), we deduce that for all ε > 0 , there is t ¯ such that

    w ( t ) ε w ( t ) w ( t ) + ε , for all t t ¯ .

    Hence, b ( t , w ( t ) ε ) b ( t , w ( t ) ) b ( t , w ( t ) + ε ) , for all t t ¯ .

    Since

    lim inf t t t + ω b ( s , w ( s ) + ε ) d s lim inf t t t + ω b ( s , w ( s ) ) d s + ε β s ω

    and

    lim inf t t t + ω b ( s , w ( s ) ε ) d s lim inf t t t + ω b ( s , w ( s ) ) d s ε β s ω ,

    where β s = sup t 0 β ( s ) , we obtain

    lim inf t t t + ω b ( s , w ( s ) ) d s ε β s ω lim inf t t t + ω b ( s , w ( s ) ) d s lim inf t t t + ω b ( s , w ( s ) ) d s + ε β s ω .

    From the arbitrariness of ε , we finally obtain

    lim inf t t t + ω b ( s , w ( s ) ) d s = lim inf t t t + ω b ( s , w ( s ) ) d s .

    We can proceed in the same way to prove that s 2 = lim sup t t t + ω b ( s , w ( s ) ) d s is independent of the choice of solution of (30). This ends the proof of Proposition 2.3.□

Let define for ω ( 0 , ) the following real numbers

S 1 ( ω ) = e s 1 ( ω ) and S 2 ( ω ) = e s 2 ( ω ) ,

where s 1 ( ω ) and s 2 ( ω ) are as in (33) and (34).

The next theorem is about the permanence and extinction of distressed banks in the banking system.

Theorem 2.1

  1. If there is a constant ω > 0 such that S 1 ( ω ) > 1 , then the stressed banks remain permanent in the banking system.

  2. If there is a constant ω > 0 such that S 2 ( ω ) < 1 , then the stressed banks go to extinction, and any stress-free solution ( H ( t ) , 0 , 0 ) is globally attractive.

Proof

We assume that there exists ω > 0 such that S 1 ( ω ) > 1 , that is s 1 ( ω ) > 0 . Let ( H ( t ) , D ( t ) , C ( t ) ) be a solution of (12) with H ( T 0 ) > 0 , D ( T 0 ) > 0 , and C ( T 0 ) > 0 for some T 0 0 .

Since s 1 ( ω ) > 0 , using the definition of s 1 ( ω ) , there exists ρ > 0 such that

(36) t t + ω b ( s , w ( s ) ε ) d s > ρ ,

for all t sufficiently large, say t T 1 and ε sufficiently small, say ε ( 0 , ε ¯ ] , where t w ( t ) is any solution of (30) with w ( 0 ) > 0 . Note that by (ii) in Proposition 2.3, we have w ( T 0 ) > 0 .

Define

(37) ε 0 = min ρ 4 β s ω ; ε ¯ and ε 1 = min ε 0 a 1 ( θ i + μ i ) 2 e a 2 ( δ ( θ i + μ i ) + θ γ s ) ; ε 0 a 1 2 2 β s Λ ¯ e 2 a 2 ,

where Λ ¯ = sup t 0 Λ ( t ) .

We will show that

(38) lim sup t D ( t ) ε 1 .

We proceed by contradiction by assuming that (38) doesn’t hold. Then there exists T 2 0 satisfying D ( t ) < ε 1 for all t T 2 .

Consider the auxiliary ODEs

(39) w ˙ ( t ) = Λ ( t ) a ( t ) w ( t ) β s e a 2 Λ ¯ a 1 ε 1

and

(40) w ˙ ( t ) = Λ ( t ) a ( t ) w ( t ) + δ + θ γ s μ i + θ i ε 1 .

Let w ¯ ( t ) be the solution of (30) with w ¯ ( T 0 ) = H ( T 0 ) and w ˜ ( t ) be the solution of (39) with initial condition w ˜ ( T 0 ) = w ¯ ( T 0 ) . By (37) and (iv) in Proposition 2.3, we obtain

(41) w ˜ ( t ) w ¯ ( t ) β s Λ ¯ e 2 a 2 a 1 2 ε 1 ε 0 2 ,

for t T 0 .

According to (iii) in Proposition 2.3, t w ( t ) is globally uniformly attractive on ( 0 , ) . Thus, there exists T 3 > 0 such that for all t T 3 , we have

(42) w ¯ ( t ) w ( t ) ε 0 2 .

We have

0 β ( t ) H ( t ) D ( t ) β s Λ ¯ e a 2 a 1 D ( t ) < β s Λ ¯ e a 2 a 1 ε 1 ,

for all t max ( T 0 ; T 2 ) . Thus, by the first equation of (12), we obtain

(43) H ˙ ( t ) = Λ ( t ) β ( t ) h ( t ) D ( t ) a ( t ) H ( t ) + δ ( t ) D ( t ) + θ ( t ) C ( t ) > Λ ( t ) a ( t ) H ( t ) β s Λ ¯ e a 2 a 1 ε 1 ,

for all t max ( T 0 ; T 2 ) .

Comparing (39) and (43), we have

(44) H ( t ) > w ˜ ( t ) ,

for all t max ( T 0 ; T 2 ) .

By (v) in Lemma 2.1, we also have for t max ( T , T 2 )

(45) H ˙ ( t ) = Λ ( t ) β ( t ) h ( t ) D ( t ) a ( t ) H ( t ) + δ ( t ) D ( t ) + θ ( t ) C ( t ) Λ ( t ) a ( t ) H ( t ) + δ + θ γ s ( μ i + θ i ) ε 1 .

Let t w ^ ( t ) be the solution of (40) with initial condition w ^ ( T 0 ) = H ( T 0 ) . Comparing (40) and (45), we have

(46) w ^ ( t ) H ( t ) ,

for t max { T 0 ; T ; T 2 } .

By (37) and (iv) in Proposition 2.3, we obtain

(47) w ^ ( t ) w ¯ ( t ) e a 2 a 1 δ + θ γ s μ i + θ i ε 1 ε 0 2 ,

for t max { T 0 ; T ; T 2 } .

Take T 4 = max { T 0 ; T 1 ; T 2 ; T 3 ; T } . By (41), (42), (44), (46), and (47), we have

w ¯ ( t ) + ε 0 w ¯ ( t ) + ε 0 2 w ^ ( t ) H ( t ) > w ˜ ( t ) w ¯ ( t ) ε 0 2 w ¯ ( t ) ε 0 ,

and thus,

(48) H ( t ) w ( t ) + ε 0 H ( t ) w ( t ) + ε 0 2 ε 0 ,

for t T 4 . By using (48), we obtain for t T 4

b ( t , H ( t ) ) b ( t , w ( t ) ε 0 ) = β ( t ) ( H ( t ) w ( t ) + ε 0 ) 2 β s ε 0

and

(49) b ( t , H ( t ) ) > b ( t , w ( t ) ε 0 ) 2 β s ε 0 .

According to (37), we have ε 0 ε ¯ . By (36) and Chasles’ relations for integral, we obtain

(50) T 4 t b ( s , w ( s ) ε 0 ) d s = T 4 T 4 + ( t T 4 ) ω ω b ( s , w ( s ) ε 0 ) d s > ρ t T 4 ω > ρ t T 4 ω 1 .

By the second equation of (12), we obtain

D ˙ ( t ) = D ( t ) ( β ( t ) H ( t ) ( μ ( t ) + δ ( t ) + γ ( t ) ) ) ,

and thus, integrating from T 4 to t for all t T 4 , we obtain from (49) and (50)

D ( t ) = D ( T 4 ) e T 4 t ( β ( s ) H ( s ) ( μ ( s ) + δ ( s ) + γ ( s ) ) ) d s D ( T 4 ) e T 4 t b ( s , w ( s ) ε 0 ) d s 2 β s ε 0 ( t T 4 ) > D ( T 4 ) e ρ ( t T 4 ω 1 ) 2 β s ε 0 ( t T 4 ) > D ( T 4 ) e ρ ( t T 4 ) ω ρ ,

and we conclude that D ( t ) as t . This contradicts the assumption that D ( t ) < ε 1 for t T 2 . From this, we conclude that (38) holds.

Next, we will prove that for some constant b > 0 , we have

(51) lim inf t D ( t ) > b

for every solution ( H ( t ) , D ( t ) , C ( t ) ) with H ( T 0 ) > 0 , D ( T 0 ) > 0 and C ( T 0 ) > 0 .

In fact, from (36), we obtain that there is a positive constant ω 3 such that

(52) t t + η b ( s , w ( s ) ε ) d s > ρ ,

for all η > ω 3 , t T 1 and ε ( 0 , ε ¯ ] . If (51) is not true, then there is a sequence of initial values X n = ( H n , D n , C n ) ( n = 1 , 2 , ) with H n > 0 , D n > 0 and C n > 0 such that

lim inf t D ( t , n ) < ε 1 n 2 , n = 1 , 2 , ,

where D ( t , n ) denotes the second component of the solution ( H ( t ) , D ( t ) , C ( t ) ) of (12) with initial conditions H ( T 1 ) = H n , D ( T 1 ) = D n and C ( T 1 ) = C n . From (38), for every n , there are two time sequences ( t k ( n ) ) k N and ( s k ( n ) ) k N , satisfying

T 1 < s 1 ( n ) < t 1 ( n ) < s 2 ( n ) < t 2 ( n ) < < s k ( n ) < t k ( n ) <

and lim k s k ( n ) = , such that

(53) D ( s k ( n ) , n ) = ε 1 n , D ( t k ( n ) , n ) = ε 1 n 2

and

(54) ε 1 n 2 < D ( t , n ) < ε 1 n , for all t ( s k ( n ) , t k ( n ) ) .

From the second equation of (12), we have

D ˙ ( t , n ) = D ( t , n ) ( β ( t ) H ( t , n ) ( μ ( t ) + δ ( t ) + γ ( t ) ) ) ( μ ( t ) + δ ( t ) + γ ( t ) ) D ( t , n ) ( μ s + δ s + γ s ) D ( t , n ) .

Integrating from s k ( n ) to t k ( n ) , we obtain

D ( t k ( n ) , n ) D ( s k ( n ) , n ) e ( μ s + δ s + γ s ) ( t k ( n ) s k ( n ) ) .

By using (53), we obtain 1 n e ( μ s + δ s + γ s ) ( t k ( n ) s k ( n ) ) , and therefore, we have

(55) t k ( n ) s k ( n ) ln ( n ) μ s + δ s + γ s

as n . From (54), for all t ( s k ( n ) , t k ( n ) ) , we have D ( t , n ) < ε 1 n < ε 1 , and therefore, by using the first equation of (12), we have for all t ( s k ( n ) , t k ( n ) ) ,

H ˙ ( t ) = Λ ( t ) β ( t ) H ( t ) D ( t ) a ( t ) H ( t ) + δ ( t ) D ( t ) + θ ( t ) C ( t ) Λ ( t ) a ( t ) H ( t ) β s e a 2 Λ ¯ a 1 ε 1 .

By comparison, we have H ( t , n ) w ˜ ( t ) for all t ( s k ( n ) , t k ( n ) ) , where t w ˜ ( t ) is the solution of (39) with initial condition w ˜ ( s k ( n ) ) = H ( s k ( n ) , n ) . By using (41), we obtain, for all t ( s k ( n ) , t k ( n ) ) ,

(56) w ˜ ( t ) w ¯ ( t ) ε 0 2 ,

where t w ¯ ( t ) is the solution of (30) with w ¯ ( s k ( n ) ) = H ( s k ( n ) , n ) . Since t w ( t ) is globally uniformly attractive, there exists T > 0 , independent of n and k , such that

(57) w ( t ) w ¯ ( t ) ε 0 2 ,

for t > s k ( n ) + T . From (55), we choose N > 0 such that t k ( n ) s k ( n ) > ω + T for all n N . Now, given n N , from (52), (56), and (57) and the second equation of (12), we deduce that

ε 1 n 2 = D ( t k ( n ) , n ) D ( s k ( n ) + T , n ) e s k ( n ) + T t k ( n ) b ( s , H ( s , n ) ) d s ε 1 n 2 e s k ( n ) + T t k ( n ) b ( s , w ( s ) ε 0 ) d s > ε 1 n 2 .

This leads to a contradiction and establishes that lim inf t D ( t ) > b . This ends the proof of part 1 of Theorem 2.1.

We assume that S s ( ω ) < 1 (that is s 2 ( ω ) < 0 ) for some ω > 0 . To prove that the distressed bank dies out, we will consider the following system equivalent to system (12)

(58) N ˙ ( t ) = Λ ( t ) a ( t ) N ( t ) α ( t ) ( D ( t ) + C ( t ) ) , D ˙ ( t ) = ( β ( t ) N ( t ) μ ( t ) δ ( t ) γ ( t ) β ( t ) D ( t ) β ( t ) C ( t ) ) D ( t ) , C ˙ ( t ) = γ ( t ) D ( t ) ( μ ( t ) + θ ( t ) ) C ( t ) ,

where, for all t 0 , μ ( t ) = a ( t ) + α ( t ) and N ( t ) = H ( t ) + D ( t ) + C ( t ) .

Since s 2 ( ω ) < 0 for some ω > 0 , by ( H 1 ) , ( H 2 ) and ( H 3 ) , for any constant ε ( 0 , 1 ) , we choose constants ε 0 ( 0 , ε ) , ζ > 0 and a sufficiently large positive constant T 0 such that

(59) t t + ω ( b ( s , w ( s ) + ε 0 ) β ( s ) ε ) d s < ζ

for all t T 0 , where w is a solution of (30) with initial value w ( 0 ) > 0 .

Let ( H ( t ) , D ( t ) , C ( t ) ) be any solution of (58) with initial conditions N ( T 0 ) > 0 , D ( T 0 ) > 0 , and C ( T 0 ) 0 . Since

N ˙ ( t ) = Λ ( t ) a ( t ) N ( t ) α ( t ) ( D ( t ) + C ( t ) ) Λ ( t ) a ( t ) N ( t )

for all t T 0 , we have N ( t ) w ( t ) for all t T 0 , where w is the solution of (30) with the initial value w ( T 0 ) = N ( T 0 ) . By (iii) in Proposition 2.3, we obtain that for ε 0 considered earlier, there exists T 1 > T 0 , such that

w ( t ) w ( t ) ε 0

for all t T 1 . Thus, we have

(60) N ( t ) w ( t ) w ( t ) + ε 0

for all t T 1 . By using (60) and the second equation of system (58), we have

(61) D ˙ ( t ) = D ( t ) ( b ( t , N ( t ) ) β ( t ) D ( t ) β ( t ) C ( t ) ) D ( t ) ( b ( t , w ( t ) + ε 0 ) β ( t ) D ( t ) )

for all t T 1 . Integrating both sides of (61), we have

(62) D ( t ) D ( T 1 ) e T 1 t ( b ( s , w ( s ) + ε 0 ) β ( s ) D ( s ) ) d s

for all t T 1 . If D ( t ) ε for all t T 1 , then from (62), we obtain

D ( t ) D ( T 1 ) e T 1 t ( b ( s , w ( s ) + ε 0 ) β ( s ) ε ) d s ,

and by (59), it follows that D ( t ) 0 as t . This leads to the contradiction with D ( t ) ε for all t T 1 . Therefore, there exists a t 1 > T 1 such that D ( t 1 ) < ε .

Now, define

N ( ε ) = sup t T 1 ( b ( s , w ( s ) + ε 0 ) + β ( t ) ε ) .

It is straightforward that N ( ε ) is bounded for ε ( 0 , 1 ) . We will prove that for all t t 1 ,

(63) D ( t ) < ε e ω N ( ε ) .

By contradiction, if (63) is not true, then there is a t 2 > t 1 , such that D ( t 2 ) > ε e ω N ( ε ) . Therefore, from the continuity of t D ( t ) , there exists t 3 ( t 1 , t 2 ) such that D ( t 3 ) = ε and D ( t ) > ε for all t ( t 3 , t 2 ) . Let q be a nonnegative integer such that t 2 ( t 3 + q ω , t 3 + ( q + 1 ) ω ) . Then, from (60), we have

ε e ω N ( ε ) < D ( t 2 ) = D ( t 3 ) e t 3 t 2 ( b ( s , N ( s ) ) β ( s ) D ( s ) β ( s ) C ( s ) ) d s ε e t 3 t 2 ( b ( s , w ( s ) + ε 0 ) β ( s ) ε ) d s < ε e ω N ( ε ) .

This leads to a contradiction. Hence, inequality (63) holds. Furthermore, by the arbitrariness of ε , we conclude that D ( t ) 0 as t .

To complete the proof of part 2 in Theorem 2.1, we prove in what follows that any stress-free solution of (12) is globally attractive. We still assume that S s ( ω ) < 1 (that is, s 2 ( ω ) < 0 ) for some ω > 0 . Let ( H ( t ) , D ( t ) , C ( t ) ) be a solution of (12) with nonnegative initial conditions and ( H 0 ( t ) , 0 , 0 ) a stress-free solution with nonnegative initial condition. We proved that lim sup t D ( t ) = 0 , since for all t 0 D ( t ) 0 and lim t D ( t ) = 0 . Therefore, given ε > 0 , there exists T ε 0 , such that D ( t ) < ε for all t T ε .

From (v) in Lemma 2.1, we have C ( t ) < ν ε for all t T ε , where ν = γ s ( μ i + θ i ) . From the arbitrariness of ε , we deduce that lim t C ( t ) = 0 . It remains to prove that lim t H ( t ) = lim t H 0 ( t ) . From (iii) in Lemma 2.1, we have H ( t ) M for all t T ε . By using the first equation in (12), we obtain

(64) Λ ( t ) a ( t ) H ( t ) β Λ ¯ e a 2 a 1 ε H ˙ ( t ) Λ ( t ) a ( t ) H ( t ) + ( δ + θ ν ) ε

for all t T ε .

Consider the following ODEs:

(65) x ˙ 1 ( t ) = Λ ( t ) a ( t ) x 1 ( t ) β s Λ ¯ e a 2 a 1 ε

and

(66) x ˙ 2 ( t ) = Λ ( t ) a ( t ) x 2 ( t ) + ( δ + θ ν ) ε .

Let t x 1 ( t ) and t x 2 ( t ) be solutions of (65) and (66), respectively, satisfying x 1 ( T ε ) = x 2 ( T ε ) = H ( T ε ) .

By comparison, we have x 1 ( t ) H ( t ) x 2 ( t ) for all t T ε .

It can easily be proved that for all t T ε

x 2 ( t ) x 1 ( t ) δ + ν θ + β s Λ ¯ e a 2 a 1 ε .

From the arbitrariness of ε , we deduce that lim t x 1 ( t ) = lim t x 2 ( t ) , and therefore, from the Squeeze Theorem, we obtain

lim t x 1 ( t ) = lim t x 2 ( t ) = lim t H ( t ) .

By using the first equation in (12), we also have the following ODE satisfied by H 0 ( t ) for t 0

(67) H ˙ 0 ( t ) = Λ ( t ) a ( t ) H 0 ( t ) .

Let t H 0 ( t ) be a solution of (67) with nonnegative initial condition. We easily prove that

(68) x 1 ( t ) H 0 ( t ) ( x 1 ( T ε ) H 0 ( T ε ) ) e a 1 ( t T ε ) + a 2 + β s Λ ¯ e a 2 a 1 ε

for all t T ε . For fixed ε , taking limit as t in (68), we obtain

lim t x 1 ( t ) H 0 ( t ) β s Λ ¯ e a 2 a 1 ε ,

which implies from the arbitrariness of ε that lim t x 1 ( t ) H 0 ( t ) = 0 . We deduce that lim t x 1 ( t ) = lim t H ( t ) = lim t H 0 ( t ) . This ends the proof of Theorem 2.1.□

Remark 2.2

The numbers S 1 ( ω ) = e s 1 ( ω ) and S 2 ( ω ) = e s 2 ( ω ) , where s 1 ( ω ) and s 2 ( ω ) are as in (33) and (34) play the role of the basic reproduction number (defined for autonomous systems) in epidemiological terminology. In our setting, the basic transmission number represents the average number of secondary distressed banks caused by a single distressed bank in the entirely healthy banks population during its entire period of distress. It is easy to prove that in the absence of rumor and in autonomous case, the numbers S 1 ( ω ) and S 2 ( ω ) are equal.

3 An optimal control problem of the model

Given the horizontal linkages in the market environment, all banks are equally needed to be under financial supervision. Such supervision is necessary in order to prevent global contamination and avoid serious consequences due to spread of contagion between banks. This partially explains why the central bank exists in many countries. The central bank is a supervisory competent authority that implements vertical connections among interacting commercial banks. Since one of the main goal of the local central bank is to avoid the wide dissemination of a contagion, we consider the following optimal control problem:

(69) min 0 t f ( κ C ( t ) + α D ( t ) + α 1 u 2 ( t ) + α 2 v 2 ( t ) ) d t , subject to H ˙ ( t ) = Λ ( t ) β ( I ( t ) ) H ( t ) D ( t ) a ( t ) H ( t ) + δ ( I ( t ) ) D ( t ) + θ ( I ( t ) ) C ( t ) + u ( t ) D ( t ) + v ( t ) C ( t ) , D ˙ ( t ) = β ( I ( t ) ) H ( t ) D ( t ) ( δ ( I ( t ) ) + μ ( I ( t ) ) + γ ( I ( t ) ) ) D ( t ) u ( t ) D ( t ) , C ˙ ( t ) = γ ( I ( t ) ) D ( t ) ( θ ( I ( t ) ) + μ ( I ( t ) ) ) C ( t ) v ( t ) C ( t ) , S ˙ ( t ) = β 1 S ( t ) I ( t ) f ( U ( t ) ) , I ˙ ( t ) = θ 1 β 1 S ( t ) I ( t ) f ( U ( t ) ) g ( U ( t ) ) I ( t ) , U ˙ ( t ) = Γ e U ( t ) δ 1 I ( t ) U ( t ) K + U ( t ) , R ˙ ( t ) = ( 1 θ 1 ) β 1 S ( t ) I ( t ) f ( U ( t ) ) + g ( U ( t ) ) I ( t ) , ( H ( 0 ) , D ( 0 ) , C ( 0 ) , S ( 0 ) , I ( 0 ) , U ( 0 ) , R ( 0 ) ) = ( H 0 , D 0 , C 0 , S 0 , I 0 , U 0 , R 0 ) ,

where κ , α , α 1 , α 2 , H 0 , D 0 , C 0 , S 0 , I 0 , U 0 , and R 0 are positive real numbers. The integral represents the general cost of financial assistance necessary to prevent the spread of contagion and economic decline in the period [ 0 , t f ] . The constants α 1 and α 2 are values of possible recapitalization with state funds as considered in [26]. κ and α are weights associated with distressed banks, and banks in crisis, respectively. They measure the relative importance of these classes of banks in the cost of the control. The states variables ( H ( ) , D ( ) , C ( ) , S ( ) , I ( ) , U ( ) , R ( ) ) belong to A C ( [ 0 , t f ] ; R 7 ) , the set of absolutely continuous functions from [ 0 , t f ] to R 7 . The controls ( u ( ) , v ( ) ) belong to the set L 1 ( [ 0 , t f ] ; [ 0 , 1 ] × [ 0 , 1 ] ) of Lebesgue integrable functions from [ 0 , t f ] to [ 0 , 1 ] × [ 0 , 1 ] . For given t 0 , u ( t ) and v ( t ) are rates at which assistance will be provided to distressed banks and banks in crisis, respectively. u ( t ) is the ratio between the financial support from the local central bank at time t and the financial needs by distressed banks at that time. v ( t ) is the ratio between the financial support from the local central bank at time t and the financial needs by banks in crisis at that time. Therefore, u ( t ) = v ( t ) = 1 means full support from the local central bank at time t (all money needed by the banks is being covered by the local central bank), while u ( t ) = v ( t ) = 0 means no financial lending or recapitalization from the local central bank at time t .

The last fourth equations of (69) do not depend neither on the first three one nor on the controls. Therefore, we restrict our study to the following problem:

(70) min 0 t f ( κ C ( t ) + α D ( t ) + α 1 u 2 ( t ) + α 2 v 2 ( t ) ) d t , subject to H ˙ ( t ) = Λ ( t ) β ( t ) H ( t ) D ( t ) a ( t ) H ( t ) + ( δ ( t ) + u ( t ) ) D ( t ) + ( θ ( t ) + v ( t ) ) C ( t ) , D ˙ ( t ) = β ( t ) H ( t ) D ( t ) ( μ ( t ) + δ ( t ) + γ ( t ) + u ( t ) ) D ( t ) , C ˙ ( t ) = γ ( t ) D ( t ) ( μ ( t ) + θ ( t ) + v ( t ) ) C ( t ) , ( H ( 0 ) , D ( 0 ) , C ( 0 ) ) = ( H 0 , D 0 , C 0 ) .

3.1 The existence and uniqueness of optimal solution

Problem (70) can be written in Lagrange form:

(71) min 0 t f ( t , X ( t ) , V ( t ) ) d t , subject to X ˙ ( t ) = h ( t , X ( t ) , V ( t ) ) , a.e. t [ 0 , t f ] , X ( 0 ) = X 0 ,

where

X ( t ) = ( H ( t ) , D ( t ) , C ( t ) ) A C ( [ 0 , t f ] ; R 3 ) ,

V ( t ) = ( u ( t ) , v ( t ) ) ( L 1 ( [ 0 , t f ] ; [ 0 , 1 ] × [ 0 , 1 ] ) ) ,

( t , X ( t ) , V ( t ) ) = κ C ( t ) + α D ( t ) + α 1 u 2 ( t ) + α 2 v 2 ( t ) ,

X 0 = ( H 0 , D 0 , C 0 ) and

h ( t , X , V ) = Λ ( t ) β ( t ) H D a ( t ) H + ( δ ( t ) + u ( t ) ) D + ( θ ( t ) + v ( t ) ) C β ( t ) H D ( μ ( t ) δ ( t ) + γ ( t ) + u ( t ) ) D γ ( t ) D ( μ ( t ) + θ ( t ) + v ( t ) ) C .

It is obvious that the functions and h are continuous. We say that a pair in ( X ( t ) , V ( t ) ) A C ( [ 0 , t f ] ; R 3 ) × L 1 ( [ 0 , t f ] ; [ 0 , 1 ] × [ 0 , 1 ] ) is feasible if it satisfies the Cauchy problem:

(72) X ˙ ( t ) = h ( t , X ( t ) , V ( t ) ) , a.e. t [ 0 , t f ] , X ( 0 ) = X 0 .

We denote by the set of all feasible pairs.

The following Lemma derived from Theorem III.4.1 and Corollary III.4.1 in [27] will be important to prove the existence of solution for the optimal control problem (71).

Lemma 3.1

For problem (71), we assume there exist positive constants c 1 and c 2 such that, for t R , X , X 1 , X 2 in R 3 and V R 2 , we have

  1. h ( t , X , V ) c 1 ( 1 + X + V ) ,

  2. h ( t , X 1 , V ) h ( t , X 2 , V ) c 2 X 1 X 2 ( 1 + V ) ,

  3. is nonempty,

  4. there is a compact set K such that X ( t f ) K for any state variable X ,

  5. h and are convex functions of V ,

  6. ( t , X , V ) c 3 V σ c 4 for some c 3 > 0 and σ > 1 .

Then, there exists ( X , V ) minimizing 0 t f ( t , X ( t ) , V ( t ) ) d t on .

The following Theorem is about the existence of optimal solution of problem (71).

Theorem 3.1

There exists an optimal control pair ( u , v ) and a corresponding triplet ( H , D , C ) of the initial value problem (71) that minimizes the cost functional over L 1 ( [ 0 , t f ] ; [ 0 , 1 ] × [ 0 , 1 ] ) . Furthermore, for small value of t f , the control pair is unique.

Proof

By using hypothesis on functions Λ , β , a , δ , γ , θ , and μ , we immediately obtain conditions (a) and (b) of Lemma 3.1. Condition (c) is immediate from the definition of . By (iii) in Lemma 2.1, we obtain condition (d). Since the state equations are linearly dependent on the controls, we obtain (e). We also have

( t , X , V ) = κ C + α D + α 1 u 2 + α 2 v 2 max ( α 1 , α 2 ) V 2

and (f) follows. Therefore, we obtain the existence of ( u , v ) and the corresponding optimal trajectory. Since the function h is Lipschitz and the solution ( H ( t ) , D ( t ) , C ( t ) ) is in a compact set in R 3 (Lemma 2.1), we obtain the uniqueness of optimal solution for small value of t f [28].□

3.2 The characterization of the optimal controls

All control minimizers in problem (71) are in [ 0 , 1 ] and therefore are essentially bounded. By the Pontryagin maximum principle (PMP) [29,30], we address the question of how to identify the solutions predicted by Theorem 3.1. Moreover, our optimal control problem (71) has only fixed initial conditions, with the state variables being free at the final time, that is, H ( t f ) , D ( t f ) , and C ( t f ) are free. This implies that abnormal minimizers [31] are not possible in our context and we can fix the cost multiplier associated with the Lagrangian to be equal to one. The Hamiltonian of problem (71) is given by

( t , ( H , D , C ) , ( p 1 , p 2 , p 3 ) , ( u , v ) ) = κ C + α D + α 1 u 2 + α 2 v 2 + p 1 ( Λ ( t ) β ( t ) H D a ( t ) H + δ ( t ) D + θ ( t ) C + u D + v C ) + p 2 ( β ( t ) H D ( μ ( t ) δ ( t ) + γ ( t ) ) D u D ) + p 3 ( γ ( t ) D ( μ ( t ) + θ ( t ) ) C v C ) .

The following theorem gives necessary optimality conditions for the optimal control problem (71).

Theorem 3.2

If ( ( H , D , C ) , ( u , v ) ) is a minimizer of problem (71), then there exist multipliers p 1 ( ) , p 2 ( ) and p 3 ( ) in A C ( [ 0 , t f ] ; R 3 ) such that

(73) p ˙ 1 ( t ) = β ( t ) D ( t ) ( p 1 ( t ) p 2 ( t ) ) + a ( t ) p 1 ( t ) , p ˙ 2 ( t ) = β ( t ) H ( t ) ( p 1 ( t ) p 2 ( t ) ) p 1 ( t ) ( δ ( t ) + u ( t ) ) + p 2 ( t ) ( μ ( t ) + δ ( t ) + γ ( t ) + u ( t ) ) p 3 ( t ) γ ( t ) α , p ˙ 3 ( t ) = p 1 ( t ) ( θ ( t ) + v ( t ) ) + p 3 ( t ) ( θ ( t ) + μ ( t ) + v ( t ) ) κ ,

for almost all t [ 0 , t f ] , with transversality conditions

(74) p 1 ( t f ) = p 2 ( t f ) = p 3 ( t f ) = 0 .

The optimal control pair for all t [ 0 , t f ] is given by

(75) u ( t ) = min max 0 , ( p 2 ( t ) p 1 ( t ) ) D ( t ) 2 α 1 , 1

and

(76) v ( t ) = min max 0 , ( p 3 ( t ) p 1 ( t ) ) C ( t ) 2 α 2 , 1 .

Proof

Direct computations show that system (73) follows from the adjoint system of the PMP [29]. Similarly, the equalities in (74) are directly given by the transversality conditions of the PMP. It remains to characterize the controls using the minimality condition of the PMP [29].

The minimality condition on the set { t [ 0 , t f ] : 0 < u ( t ) < 1 and 0 < v ( t ) < 1 } is

u = 2 α 2 u + p 1 D p 2 D = 0 and v = 2 α 2 v + p 1 C p 3 C = 0 .

Thus, on this set,

u = D ( p 2 p 1 ) 2 α 1 and v = C ( p 3 p 1 ) 2 α 2 .

If t { t [ 0 , t f ] : u = 1 } , then the minimality condition is

u = 2 α 1 u + p 1 D p 2 D 0 , i.e., D ( p 2 p 1 ) 2 α 1 1 .

Analogously, if t { t [ 0 , t f ] : v = 1 } , then the minimality condition is

v = 2 α 2 v + p 1 C p 3 C 0 , i.e., C ( p 3 p 1 ) 2 α 2 1 .

If t { t [ 0 , t f ] : u = 0 } , then the minimality condition is

u = 2 α 1 u + p 1 D p 2 D 0 , i.e., D ( p 2 p 1 ) 2 α 1 0 .

Analogously, if t { t [ 0 , t f ] : v = 0 } , then the minimality condition is

v = 2 α 2 v + p 1 C p 3 C 0 , i.e., C ( p 3 p 1 ) 2 α 2 0 .

Therefore, we obtain (75) and (76).□

4 Numerical simulations

By using ODE 45 routine and the shooting method implemented on MATLAB software, we now carry out numerical simulations to apply our results. To illustrate theoretical results obtained earlier, we assume in what follows that

(77) δ ( I ) = δ 0 1 + I , θ ( I ) = θ 0 1 + I , γ ( I ) = γ 0 1 + e I , μ ( I ) = μ 0 1 + e I , β ( I ) = β 0 1 + e I ,

where δ 0 , θ 0 , γ 0 , μ 0 , and β 0 are positive real numbers. We also assume that the functions t Λ ( t ) and t a ( t ) are constant.

Under these assumptions and the existence of a rumor about the financial health of given banks, (33) and (34) become respectively

(78) s 1 ( ω ) = lim inf t t t + ω β ( I ( s ) ) Λ a ( μ ( I ( s ) ) + γ ( I ( s ) ) + δ ( I ( s ) ) ) d s

and

(79) s 2 ( ω ) = lim sup t t t + ω β ( I ( s ) ) Λ a ( μ ( I ( s ) ) + γ ( I ( s ) ) + δ ( I ( s ) ) ) d s ,

for all ω ( 0 , ) . Therefore, we have

s 1 ( ω ) ω β i Λ a ( μ i + γ i + δ i ) and s 2 ( ω ) ω β s Λ a ( μ s + γ s + δ s ) ,

where β i = inf I 0 β ( I ) , γ i = inf I 0 γ ( I ) , θ i = inf I 0 θ ( I ) , δ i = inf I 0 δ ( I ) , μ i = inf I 0 μ ( I ) , β s = sup I 0 β ( I ) , γ s = sup I 0 γ ( I ) , θ s = sup I 0 θ ( I ) , δ s = sup I 0 δ ( I ) and μ s = sup I 0 μ ( I ) .

Now, define S 1 ¯ = β i Λ a ( μ i + γ i + δ i ) and S 2 ¯ = β s Λ a ( μ s + γ s + δ s ) .

We have the following important result, which is a particular case of Theorem 2.1.

Theorem 4.1

  1. If S 1 ¯ > 1 , then the stressed banks are permanent in the banking system;

  2. If S 2 ¯ < 1 , then there is an extinction of stressed banks in the banking system and any stress-free solution of (12) is globally attractive.

Remark 4.1

Using the functions defined in (77), if P 0 = S 0 + I 0 + R 0 is large enough, we obtain S 1 ¯ S 2 ¯ . In fact, we have

β s = β 0 1 + e P 0 , γ s = γ 0 1 + e P 0 , μ s = μ 0 1 + e P 0 , δ s = δ 0 , θ s = θ 0

and β i = β 0 2 , γ i = γ 0 2 , μ i = μ 0 2 , δ i = δ 0 2 , θ i = θ 0 2 .

Table 1 gives the range of parameters values used to study the influence of parameters on rumors spread. Table 2 gives the values of the parameters used to perform simulations.

Table 1

Range of parameters used to study the influence of parameters on rumors spread

Parameter Range Source
e ( 0 , 1 ) [10]
K ( 0.5 , 1.5 ) [10]
β ( 1 0 4 , 0.6 ) Assumed
Γ ( 0.5 , 1.5 ) [10]
θ 1 (0, 1) Assumed
δ 1 ( 0 , 1 ) Assumed
Table 2

Values of the parameters used for simulations

Parameter Value Source
κ 1 Assumed
α 1 Assumed
α 1 1.5 [26]
α 2 1.5 [26]
γ 0 0.25 Assumed
Λ 0.01 Assumed
a 0.003 Assumed
δ 0 0.02 Assumed
θ 0 0.05 Assumed
μ 0 0.01 Assumed
e 0.3 [10]
K 1 [10]
β 0.5 [10]
Γ 1 [10]
θ 1 0.2 [10]
δ 1 0.5 [10]
f ( U ) 0.5 ( 1 + e U ) [10]
g ( U ) 0.2 U 1 + U [10]

4.1 The impact of rumors on the banking crisis spread

Figure 1 displays the influence of parameters on rumors spread. It suggests that the spread of rumors is most sensitive to β 1 (the influence rate of spreaders on ignorant individuals), Γ (the allotted budgeting rate by the government for adjusting and inhibiting mechanisms), and e (the decay rate of adjusting and inhibiting mechanisms). This emphasizes the importance of government’s actions to attenuate the influence of spreaders on ignorant individuals.

Figure 1 
                  Influence of the parameters on rumors spread.
Figure 1

Influence of the parameters on rumors spread.

Once a rumor on the financial health of given banks starts, we observe in Figure 2 that, after a few period of time, almost all ignorant individuals are already informed and the rumor tends to die out. In Figure 3, by using the same initial conditions, we illustrate the difference between the behavior of local banking system with and without the presence of bank run phenomenon due to rumor spread. Under the influence of bad news about the financial health of some given banks, it can happen that stressed banks temporally disappear because the great amount of them enter into liquidation and other are admitted to restructuring stage as illustrated in Figure 3. Hence, in the absence of bank run phenomenon, stressed banks can resist for a long time.

Figure 2 
                  Time evolution of the rumor.
Figure 2

Time evolution of the rumor.

Figure 3 
                  Difference between the evolution of the local banking system with and without the presence of rumor about the financial health of given banks.
Figure 3

Difference between the evolution of the local banking system with and without the presence of rumor about the financial health of given banks.

The long-time behavior of the local banking system with the persistence and extinction of stressed banks is illustrated in Figures 4 and 5, respectively.

Figure 4 
                  Time evolution of the local banking system under the influence of rumor with 
                        
                           
                           
                              
                                 
                                    β
                                 
                                 
                                    0
                                 
                              
                              =
                              0.38
                           
                           {\beta }_{0}=0.38
                        
                      and 
                        
                           
                           
                              
                                 
                                    
                                       
                                          S
                                       
                                       
                                          1
                                       
                                    
                                 
                                 
                                    ¯
                                 
                              
                              =
                              4.8346
                              >
                              1
                           
                           \overline{{{\mathcal{S}}}_{1}}=4.8346\gt 1
                        
                     . There is persistence of stressed banks.
Figure 4

Time evolution of the local banking system under the influence of rumor with β 0 = 0.38 and S 1 ¯ = 4.8346 > 1 . There is persistence of stressed banks.

Figure 5 
                  Time evolution of the local banking system under the influence of rumor with 
                        
                           
                           
                              
                                 
                                    β
                                 
                                 
                                    0
                                 
                              
                              =
                              0.07
                           
                           {\beta }_{0}=0.07
                        
                      and 
                        
                           
                           
                              
                                 
                                    
                                       
                                          S
                                       
                                       
                                          2
                                       
                                    
                                 
                                 
                                    ¯
                                 
                              
                              =
                              0.8906
                              <
                              1
                           
                           \overline{{{\mathcal{S}}}_{2}}=0.8906\lt 1
                        
                     . There is extinction of stressed banks.
Figure 5

Time evolution of the local banking system under the influence of rumor with β 0 = 0.07 and S 2 ¯ = 0.8906 < 1 . There is extinction of stressed banks.

4.2 Optimal paths

We proved in Section 3, Theorem 3.1, the existence and uniqueness of optimal solution of problem (71) for small time. We also gave the characterizations of optimal controls in Theorem 3.2. In what follows, we use the shooting method implemented on MATLAB software to illustrate these results.

Figure 6 gives the profiles of the optimal control. Figure 7 displays the optimal states trajectories. We can observe that without control the healthy banks can be reduced significantly, which is prejudicious for the stability of the financial system and even for the development of the economy. The control allows to maintain a minimum level of healthy banks in the banking system. We also observe that the rate of assistance to distressed banks is greater than that of banks in crisis. An explanation is that, due to the fact that distressed banks contribute to spread the stress (it is not the case for banks in crisis), it is more efficient to assist them in order to contain the crisis.

Figure 6 
                  Optimal controls paths 
                        
                           
                           
                              
                                 
                                    β
                                 
                                 
                                    0
                                 
                              
                              =
                              0.38
                           
                           {\beta }_{0}=0.38
                        
                      and 
                        
                           
                           
                              
                                 
                                    t
                                 
                                 
                                    f
                                 
                              
                              =
                              15
                           
                           {t}_{f}=15
                        
                      days.
Figure 6

Optimal controls paths β 0 = 0.38 and t f = 15 days.

Figure 7 
                  Optimal behavior of the banking system with 
                        
                           
                           
                              
                                 
                                    β
                                 
                                 
                                    0
                                 
                              
                              =
                              0.38
                           
                           {\beta }_{0}=0.38
                        
                      and 
                        
                           
                           
                              
                                 
                                    t
                                 
                                 
                                    f
                                 
                              
                              =
                              15
                           
                           {t}_{f}=15
                        
                      days.
Figure 7

Optimal behavior of the banking system with β 0 = 0.38 and t f = 15 days.

5 Conclusion

The bank run phenomenon, most often originated from the rumor about the financial health of given banks, can create important damage on a banking system. In the literature, many theoretical and empirical studies have been made on the spread of the financial crisis. However, to the best of our knowledge, no theoretical model of the impact of rumors on the propagation of the banking crisis has been made. In this paper, we proposed a mathematical model based on an epidemiological approach of the above problem. Our main findings are summarized below.

  • By using a Lyapunov function and a comparison Theorem, we studied the asymptotic behavior of the submodel of rumor spread.

  • A sensitivity study allowed us to highlight the importance of the parameters β 1 (the influence rate of spreaders on ignorant individuals), Γ (the allotted budgeting rate by the government for adjusting and inhibiting mechanisms), and e (the decay rate of adjusting and inhibiting mechanisms). This emphasizes the importance of government’s actions to attenuate the influence of spreaders on ignorant individuals.

  • We obtained conditions under which stressed banks disappear or persist in the banking system.

  • To contain the disastrous effects of rumor spread on the stability of the financial system, we proposed and solved an optimal control problem with the objective of minimizing, at the lowest cost, the number of distressed banks and banks in crisis. The results obtained can be useful to regulators in developing strategies to maintain financial stability.

  • All the aforementioned findings are illustrated by numerical simulations.

However, the epidemiological approach does not allow to clearly identify the possible sources of contagion even if it allows to explicitly describe the contagion dynamics with the ultimate purpose to fully understand its underlying mechanisms. This constitutes a limitation of our model. Our next challenge is to make the same investigation by relying on a network-based approach and incorporating the rating of each bank.

Acknowledgements

The authors would like to thank the three anonymous referees for their comments and suggestions, which helped greatly improve the manuscript.

  1. Funding information: The authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Conflict of interest: The authors state no conflict of interest.

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Received: 2021-05-19
Accepted: 2022-02-14
Published Online: 2022-05-04

© 2022 Calvin Tadmon and Eric Rostand Njike-Tchaptchet, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. On some summation formulas
  3. A study of a meromorphic perturbation of the sine family
  4. Asymptotic behavior of even-order noncanonical neutral differential equations
  5. Unconditionally positive NSFD and classical finite difference schemes for biofilm formation on medical implant using Allen-Cahn equation
  6. Starlike and convexity properties of q-Bessel-Struve functions
  7. Mathematical modeling and optimal control of the impact of rumors on the banking crisis
  8. On linear chaos in function spaces
  9. Convergence of generalized sampling series in weighted spaces
  10. Persistence landscapes of affine fractals
  11. Inertial iterative method with self-adaptive step size for finite family of split monotone variational inclusion and fixed point problems in Banach spaces
  12. Various notions of module amenability on weighted semigroup algebras
  13. Regularity and normality in hereditary bi m-spaces
  14. On a first-order differential system with initial and nonlocal boundary conditions
  15. On solving pseudomonotone equilibrium problems via two new extragradient-type methods under convex constraints
  16. Local linear approach: Conditional density estimate for functional and censored data
  17. Some properties of graded generalized 2-absorbing submodules
  18. Eigenvalue inclusion sets for linear response eigenvalue problems
  19. Some integral inequalities for generalized left and right log convex interval-valued functions based upon the pseudo-order relation
  20. More properties of generalized open sets in generalized topological spaces
  21. An extragradient inertial algorithm for solving split fixed-point problems of demicontractive mappings, with equilibrium and variational inequality problems
  22. An accurate and efficient local one-dimensional method for the 3D acoustic wave equation
  23. On a weighted elliptic equation of N-Kirchhoff type with double exponential growth
  24. On split feasibility problem for finite families of equilibrium and fixed point problems in Banach spaces
  25. Entire and meromorphic solutions for systems of the differential difference equations
  26. Multiplication operators on the Banach algebra of bounded Φ-variation functions on compact subsets of ℂ
  27. Mannheim curves and their partner curves in Minkowski 3-space E13
  28. Characterizations of the group invertibility of a matrix revisited
  29. Iterates of q-Bernstein operators on triangular domain with all curved sides
  30. Data analysis-based time series forecast for managing household electricity consumption
  31. A robust study of the transmission dynamics of zoonotic infection through non-integer derivative
  32. A Dai-Liao-type projection method for monotone nonlinear equations and signal processing
  33. Review Article
  34. Remarks on some variants of minimal point theorem and Ekeland variational principle with applications
  35. Special Issue on Recent Methods in Approximation Theory - Part I
  36. Coupled fixed point theorems under new coupled implicit relation in Hilbert spaces
  37. Approximation of integrable functions by general linear matrix operators of their Fourier series
  38. Sharp sufficient condition for the convergence of greedy expansions with errors in coefficient computation
  39. Approximation of conic sections by weighted Lupaş post-quantum Bézier curves
  40. On the generalized growth and approximation of entire solutions of certain elliptic partial differential equation
  41. Existence results for ABC-fractional BVP via new fixed point results of F-Lipschitzian mappings
  42. Linear barycentric rational collocation method for solving biharmonic equation
  43. A note on the convergence of Phillips operators by the sequence of functions via q-calculus
  44. Taylor’s series expansions for real powers of two functions containing squares of inverse cosine function, closed-form formula for specific partial Bell polynomials, and series representations for real powers of Pi
  45. Special Issue on Recent Advances in Fractional Calculus and Nonlinear Fractional Evaluation Equations - Part I
  46. Positive solutions for fractional differential equation at resonance under integral boundary conditions
  47. Source term model for elasticity system with nonlinear dissipative term in a thin domain
  48. A numerical study of anomalous electro-diffusion cells in cable sense with a non-singular kernel
  49. On Opial-type inequality for a generalized fractional integral operator
  50. Special Issue on Advances in Integral Transforms and Analysis of Differential Equations with Applications
  51. Mathematical analysis of a MERS-Cov coronavirus model
  52. Rapid exponential stabilization of nonlinear continuous systems via event-triggered impulsive control
  53. Novel soliton solutions for the fractional three-wave resonant interaction equations
  54. The multistep Laplace optimized decomposition method for solving fractional-order coronavirus disease model (COVID-19) via the Caputo fractional approach
  55. Special Issue on Problems, Methods and Applications of Nonlinear Analysis
  56. Some recent results on singular p-Laplacian equations
  57. Infinitely many solutions for quasilinear Schrödinger equations with sign-changing nonlinearity without the aid of 4-superlinear at infinity
  58. Special Issue on Recent Advances for Computational and Mathematical Methods in Scientific Problems
  59. Existence of solutions for a nonlinear problem at resonance
  60. Asymptotic stability of solutions for a diffusive epidemic model
  61. Special Issue on Computational and Numerical Methods for Special Functions - Part I
  62. Fully degenerate Bernoulli numbers and polynomials
  63. Wigner-Ville distribution and ambiguity function associated with the quaternion offset linear canonical transform
  64. Some identities related to degenerate Stirling numbers of the second kind
  65. Two identities and closed-form formulas for the Bernoulli numbers in terms of central factorial numbers of the second kind
  66. λ-q-Sheffer sequence and its applications
  67. Special Issue on Fixed Point Theory and Applications to Various Differential/Integral Equations - Part I
  68. General decay for a nonlinear pseudo-parabolic equation with viscoelastic term
  69. Generalized common fixed point theorem for generalized hybrid mappings in Hilbert spaces
  70. Computation of solution of integral equations via fixed point results
  71. Characterizations of quasi-metric and G-metric completeness involving w-distances and fixed points
  72. Notes on continuity result for conformable diffusion equation on the sphere: The linear case
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