a consumer credit risk structural model based on ... · on time. this formulation is best suited to...

Journal of Credit Risk 15(2), 1–19DOI: 10.21314/JCR.2018.244

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Research Paper

A consumer credit risk structural modelbased on affordability: balance at risk

Marcelo S. Perlin, Marcelo B. Righi and Tiago P. Filomena

Escola de Administracao, Universidade Federal do Rio Grande do Sul,Rua Washington Luiz, 855 – Centro Historico de Porto Alegre – RS,90010-460 Brazil; emails: [email protected],[email protected], [email protected]

(Received August 3, 2017; revised June 11, 2018; accepted August 20, 2018)

ABSTRACT

This paper introduces an approach designed for personal credit risk, with possibleapplications in risk assessment and optimization of debt contracts. We define a struc-tural model related to the financial balance of an individual, allowing for cashflowseasonality and deterministic trends in the process. Based on the proposed model,we develop risk measures associated with the probability of default rates conditionalon time. This formulation is best suited to short-term loans, where the dynamics ofindividuals’ cashflow, such as seasonality and uncertainty, can significantly impactfuture default rates. In the empirical section of this paper, we illustrate an applicationby estimating risk measures using simulated data. We also present the specific caseof optimization of a financial contract, where, based on an estimated model, we findthe yield rate/time to maturity pair that maximizes the expected profit or minimizesthe default risk of a short-term debt contract.

Keywords: credit risk; balance at risk (BaR); personal finance; cashflow.

Corresponding author: M. S. Perlin Print ISSN 1744-6619 jOnline ISSN 1755-9723c 2019 Infopro Digital Risk (IP) Limited

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2 M. S. Perlin et al

1 INTRODUCTION

The assessment of consumer credit quality is an important aspect in banking andfinance (Thomas 2000). The existence of a developed financial market allows an indi-vidual to sell future cashflows in exchange for present purchases of goods, such as aTV, car or real estate (Shiller 2013). Over the years, a higher demand for consumercredit products and competitive incentives for cost minimization in credit analysishave motivated a transition from the subjective evaluation of credit risk to the useof quantitative models for so-called credit and behavior scoring (Crook et al 2007;Hand and Henley 1997; Thomas 2000).

Traditional models for consumer credit risk assessment tend to rely on a broadrange of techniques, such as artificial neural networks (Khashman 2010; Oreski et al2012), support vector machines (Huang et al 2007), logistic regressions (Crook andBellotti 2010; Wiginton 1980), decision trees (Matuszyk et al 2010) and mathemati-cal programming (Crook et al 2007), to cite some examples.1 Another, less exploredapproach to consumer credit risk assessment is a structural model, which is moreoriented toward obtaining the probability of default (PD) of a loan.

The first structural model for credit risk was an option-based approach proposedby Merton (1974) that focused on modeling the stochastic dynamics of a firm’s value.If the firm’s value falls below its debt value on its maturity date, then the firm is indefault. This phenomenon is equivalent to stating that equity holders did not exercisetheir call option on a company’s assets: the option expired out-of-the-money. Thus,the PD on the loan equals the probability of the option being out-of-the-money onthe expiration date (Allen et al 2004). Since Merton’s seminal work, other corporatestructural credit risk models have appeared, such as Longstaff and Schwartz (1995),Leland and Toft (1996) and Collin-Dufresne and Goldstein (2001).2

Despite the developments related to structural corporate credit risk models, notmuch attention has been paid to structural models for consumer credit risk. Perliand Nayda (2004) discuss an option-based model that is similar to one for corporatecredit risk: if a consumer’s assets are lower than a threshold, then they will default onthe loan. De Andrade and Thomas (2007) propose a structural model in which the PDis based on the consumer’s reputation. In their model, the consumer has a call optionon the value of their reputation, and the strike price of this option is the debt value.The credit/behavior score is the proxy for the value of the reputation. Thomas (2009)classifies this model as a consumer structural model on the basis of reputation.

1 Reviews of other techniques can be found in Baesens et al (2003) and Lessmann et al (2013).2 Reduced-form models, in which default is modeled exogenously, are also alternatives to structuralmodels (see Jarrow et al 2004).

Journal of Credit Risk www.risk.net/journals

BaR: balance at risk 3

Our approach is a consumer structural model based on affordability (Thomas2009), which differs not only from Perli and Nayda (2004) but also from De Andradeand Thomas (2007). We model directly the balance of the individual client by explic-itly defining a stochastic process for their income and expenses. For the purposesof this paper, we define an applicant’s balance as the available funds in their bankaccount. This is not a strict definition; one can clearly define any asset as part of thebalance, as long as it is liquid enough to be cashed without price restrictions. This orother definitions can be adapted to the use of our proposed model.

This approach allows for greater flexibility in simulating the scenarios and a morerealistic assessment of the PD because it does not require any characteristic infor-mation from the applicant. The method is best suited to short-term loans, for whichthe distribution of cashflows over time can significantly impact the default rate ofthe debt. The model is easily justified because a better assessment of the applicants’default rate is very important, given that short-term loans can improve the long-termrelationship between a bank and its clients (Bodenhorn 2003) as well as the financialstability of the economy (Wagner and Marsh 2006).

Further, as discussed by Thomas (2010) and Crook and Bellotti (2010), intro-ducing economics and market conditions into consumer risk assessment is still achallenge. Thus, we also contribute to the literature by directly considering the con-ditions of the economy, such as unemployment and wage levels. We call our modela balance-at-risk (BaR) model.

Note that there is a literature combining transaction data with credit bureau infor-mation based on a variety of machine learning techniques (Butaru et al 2016; Khan-dani et al 2010; Oreski and Oreski 2014; Oreski et al 2012). These studies are relianton finding patterns in different sets of data to make inferences about consumer creditratings. Our BaR model, however, is organized around the consumer’s account bal-ance, which means it is closely related to corporate cashflow-at-risk (CFaR) models.This type of model is a downside risk metric based on past cashflow distributionsused to evaluate corporate risks (Andren et al 2005; Oral and Akkaya 2015; Steinet al 2001).

The theory that supports this proposal, along with derivations of risk measures, isprovided in this paper. Using an artificial data set, we illustrate the model’s usefulnessby first estimating an empirical model and then using it to calculate forward PDsthat are conditional on time. We also show how the model can be used in a loanapplication by finding the parameters of a debt contract that maximize the expectedreturn of the financial transaction.

This paper is organized as follows. In Section 2, we discuss the theory that sup-ports the empirical BaR model. In Section 3, an application model is examined. InSection 4, we finish the paper with some concluding remarks.

www.risk.net/journals Journal of Credit Risk


2 BALANCE AT RISK

In this section, we present our proposed approach, BaR, to measure and managecredit risk in a mathematical and theoretical framework. Unless otherwise stated, thecontent is based on the following notation. Consider the filtered atom-less probabilityspace XT WD X.˝;F ; .FT /T2T;P/ of monetary values, where ˝ is the samplespace; F is the set of possible events in ˝; FT is a filtration with F0 D f;; ˝g,F D �.

ST>0 FT /, T WD RC[1 and the usual assumptions; and P is a probability

measure that is defined in ˝ of the events contained in F .We consider adapted random processes XT W XT ! R to represent the variables

in our approach. Thus,EŒXT � is the expected value ofXT under P. All equalities andinequalities are considered to be almost surely in P. FXT

WD FX jFTis the probability

function of XT , with inverse F �1XTand density fXT

. At time T , BT , RT , CT , IT , ETandLT represent, respectively, balance, risk-free rate, net cashflow, income, expenseand loans to be paid. We assume thatRT andLT are FT�dt measurable because theirvalues are known by contract in the previous period.

The main idea is to consider the balance of an agent as a stochastic process, com-posed of their net cashflows, to measure the credit risk incurred for a financial insti-tution when it loans money to this agent. The BaR approach is the analysis of riskmeasures through this stochastic process. Thus, in this section, we define and exposesome properties of the stochastic process under analysis as well as the credit riskmeasures that are derived from it.

DEFINITION 2.1 Let BT , RT , CT , IT , ET W XT ! R and CT D IT � ET . Theclosed form for the stochastic process that represents the balance is given by

BT D B0 exp�Z T

0

Rt dt�C

Z T

0

Ct exp�Z T

t

Rs ds�

dt; (2.1)

dBT D��B0 exp

�Z T

0

Rt dt��RT C CT

�dt: (2.2)

REMARK 2.2 The stochastic process in Definition 2.1 depends only on cashflowsand can be considered information that summarizes the distinct dimensions (eco-nomic, social, geographic and others) of the agent’s behavior. Equations (2.1) and(2.2) can be recursively generalized for any time T � 6 T and, respectively, conformto

BT D BT � exp�Z T

T �Rt dt

�C

Z T

T �Ct exp

�Z T

t

Rs ds�

dt;

dBT D��BT � exp

�Z T

T �Rt dt

��RT C CT

�dt:



Moreover, it is possible to obtain simpler formulations under a null initial balance,ie, B0 D 0.

Obviously, the properties of BT as a stochastic process directly depend on thosepossessed by CT and, consequently, by IT and ET . At this point, we do not makeassumptions about the stochastic process that governs such random variables. Ourgoal is to keep the model as general as possible. Nonetheless, for a special case, it ispossible to analytically derive FBT

given FITand FET

. To do so, we use the conceptof probability function convolution. Below, we present the definition of this conceptand two well-known lemmas in probability theory. Given this background, we provea theorem.

DEFINITION 2.3 The convolution between two probability densities fX and fYis given by

fX�fY .x/ DZ 1�1

fX .x � u/fY .u/ du: (2.3)

LEMMA 2.4 Let X and Y be two independent random variables. Then, fXCY DfX�fY .x/.

LEMMA 2.5 Let k 2 R. Then,

f .x/ D1

jkjfX

�x

k

�:

PROPOSITION 2.6 Let BT , RT , CT , IT , ET W XT ! R, CT D IT � ET and�t D

R TtRs ds. If IT , �ET are independent for all T 2 T, and CT , CS are

independent for all T; S 2 T, T ¤ S , then

FBT.x/ D

Z x

�1

hlim

dt!0fB0�0

.s/�fCt�t.s/ˇT0

ids; (2.4)

fCt�t.x/ D

�1

j�t jfIt

�x

�t

��1

j�t jf�Et

�x

�t

��for all t 2 Œ0; T �; (2.5)

fCt�t.x/jT0 D fC0�0

.x/�fC0Cdt�0Cdt.x/� � � ��fCT�T

.x/: (2.6)

PROOF BecauseRT is FT�dt measurable by assumption, �t 2 R for all t 2 Œ0; T �.Because Ct�t D It�t � Et�t for all t 2 Œ0; T �, we have by Lemma 2.4 andLemma 2.5 that

fCt�t.x/ D fIt�t�Et�t

D

�1

j�t jfIt

�x

�t

��1

j�t jf�Et

�x

�t

��for all t 2 Œ0; T �.



Repeating the argument for Ct�t ; t 2 Œ0; T �, which are independent by assump-tion, we obtain

fR T0 Ct�t dt .x/ D lim

dt!0fCt�t

.x/ˇT0:

Because fB0�0is a Dirac measure, which is independent of any probability function,

and FX .x/ DR x�1

fX .s/ ds, we have

FBT.x/ D

Z x

�1

hlim

dt!0fB0�0

.s/�fCt�t.s/ˇT0

ids:

This concludes the proof. �

REMARK 2.7 Naturally, such assumptions of independence in Proposition 2.6 aretoo restrictive and can be questioned in real life. However, even with such constraints,the degree of complexity becomes significant and leads to an analytical expressionthat is difficult to compute. Moreover, obtaining FBT

from cashflow distributions isat least an alternative. One can eliminate the assumption that IT and �ET are inde-pendent if the distribution for CT is directly known. Nonetheless, in practical situa-tions, it is also possible to directly estimate FBT

from balance data using numericalsimulations.

Having explained the stochastic process, we turn our focus to the next step in theBaR approach, which is measuring credit risk. We concentrate on two risk measures:the PD and the loss given default (LGD). As both PD and LGD are very well-knownrisk measures for credit risk, we shall not pursue a debate of their theoretical prop-erties in this paper. Nonetheless, we formally define them and briefly discuss theirfinancial meaning.

DEFINITION 2.8 Let BT , LT W XT ! R. The probability of default, PDT WXT ! Œ0; 1�, and the loss given default, LGDT W XT ! R, are defined, respectively,as follows:

PDT WD P.BT 6 LT / D FBT.LT /; (2.7)

LGDT WD �EŒBT � LT j BT 6 LT D F �1BT.PDT /�: (2.8)

REMARK 2.9 The PD is the probability of an agent not having enough balanceto pay their loan at time T . It is a very simple and intuitive credit risk measure.However, PD alone does not give the entire picture of a situation, because it does notconsider the loss magnitudes in the event of a default. This shortcoming is handled bythe LGD, which indicates how much money is necessary to fulfill the loss in the eventof a default. This measure has similarities to the well-known market risk measure



expected shortfall (ES), but for LGD the expectation is truncated by LT instead of aquantile of interest. Moreover, because (2.7) and (2.8) both depend directly on FBT

,under the hypothesis of Proposition 2.6 one can obtain analytical formulations forthese two risk measures.

3 AN EMPIRICAL BaR MODEL

Now, we consider a specific BaR model nested in the previous formulations. Fromthis point forward, we change the notation so that the time index is represented byt D 1; : : : ; T , which is the usual notation in empirical time series models.

We use a discrete version of the model by setting the income and the expenseequation of an individual to separate stochastic processes. We expect income to be farmore predictable than expenses, because most of society is bound by work contractsthat explicitly define the amount of financial reward that one receives per unit of time.However, when looking at expenses, we expect a higher quantity of noise because theindividual decision to purchase goods and services is a function of diverse economic,social and personal factors. The heterogeneous stochastic properties of the inflow andoutflow of cash separate the process of income and expenses.

Another important aspect of empirically modeling the inflow and outflow of finan-cial resources is recognizing the existence of seasonalities. An individual mightreceive more cash in particular periods than others. For instance, a worker in Brazil isentitled to an extra paycheck and an additional cash-based holiday premium through-out the year. These amounts are usually paid in June and December. Identifying theseparticular months is essential to developing a realistic credit risk model, particularlyin the case of short-term loans. The expectation of receiving more cash in particularmonths affects the balance and adds a time dependency to the dynamic of cashflowsand, therefore, the forward PD.

A realistic empirical model should also consider the effect of unemployment. Ifan individual loses their work contract for any reason, their cash income ceases, thusaffecting the PD. When an individual loses their job, all that is left as support forexpenses is the current value of the balance.

In considering these effects, we propose a discrete version of (2.1) and (2.2) forour empirical example:

Bt D Bt�1.1C rt /C Ii;t �Ei;t ; (3.1)

Ii;t D

(0 if St D 1 (unemployed);

FICMIi;t if St D 2 (employed);(3.2)

Ei;t D FECMEi;t ; (3.3)



where Bt is the balance at time t (t D 1; : : : ; T ); rt is the risk-free yield (monthly),assumed constant (rt D r); Ii;t is the total income for month i and time t ; Ei;t isthe total expense for month i and time t ; FI is the fixed monthly income; MI is themonthly extra income for month i ; FE is the fixed monthly expense; and ME is themonthly extra expense for month i .

Following the ideas described in Huh et al (2010) and Malik and Thomas (2012),the states of employment and unemployment (St ) follow a discrete Markov chain,given by transition matrix P :

P D

"p11 1 � p22

1 � p11 p22

#: (3.4)

Because only two states and two transition probabilities exist, we estimate thetransition probabilities by inverting the expected duration of the states E.Dur.St Dk// D 1=.1 � pkk/:

p11 D 1 � 1=E.time employed/; (3.5)

p22 D 1 � 1=E.time unemployed/: (3.6)

Thus, depending on how much time it takes for a worker in the same professionas the applicant to get a job or lose their job, transforming this information intotransition probabilities using (3.5) and (3.6) is easy. Given this setup, we allow foreconomic conditions in the job market to affect the applicant’s PD. If the unemploy-ment duration increases, so does the likelihood of a lower income. Therefore, we canexpect an increase in the PD on a loan. Note that the balance in (3.1) changes overtime, indicating that the PD should be recalculated as time moves forward, in a man-ner similar to the behavioral models (Thomas 2000). If the effective balance levelincreases with an applicant’s good financial status, the likelihood of a future defaultdecreases, and the individual should receive the reward of paying lower interest rates.

3.1 Simulation of the income and expense data

As an example of an empirical application, we assume the existence of a historyof inflows and outflows of a balance account over five years. That is, in the data-generating stage, we also assume the individual was employed or had passive earn-ings throughout the period. Individual banking statements should be easily availablewithin a commercial bank. In commercial applications of the model, it might beinteresting to consider the creation of a central organization using applicants’ aggre-gate bank statements. On the one hand, banks would benefit from gaining a largeramount of information from applications. On the other hand, clients would benefitfrom competition between banks, which should create a downward pressure on theinterest rates of debt.



To illustrate the use of this model, we simulate the inflows and outflows of moneyas random normal variables, with different means and standard deviations accordingto the month of the year. This simulation should emulate the expected noise fromempirical banking data.

In the simulation, we set an initial balance of $1000, where, on the income side,the applicant makes an average of $3050 every month. In the months of June andDecember, they receive a bonus of 50%. The income in each month is a random nor-mal variable, with its mean defined using the previous rules and a standard deviationof $50. On the expense side, the applicant expends an average of $3000, with 40%increases in June and December. The instability, however, is higher for expenses,with a standard deviation of $250. Given the previous information, we can simulatea banking history for any period of time.

In Figure 1, we present the time series plot of the simulated income and expensedata. Note that the behavior of income is far more stable than that of expenses, withclear seasonality in June and December. The balance of this individual has a clearupward trend because their income is usually higher than their expenditure. Further,we assume that, generally, the applicant is employed for three years, and they canacquire a new job within three months when unemployed. This information can beretrieved from the applicant’s job records or other sources. For example, the Bureauof Labor Statistics reports that the average duration of employment in the UnitedStates for March 2016 was twenty-nine weeks (7.25 months).3

3.2 Estimation of the coefficients

Given the general mathematical formulation discussed in the previous section, theempirical model can be formulated in different ways. For simplicity, we assume thatincome and expenses follow a linear model conditional on time. However, it is impor-tant to point out that more sophisticated methods, such as cubic splines, can be usedto model the seasonality of the banking data. Such an approach has been used withsuccess in finance to model volatility and term structures (Audrino and Buhlmann2009; Engle and Rangel 2008; Jarrow et al 2004).

The model’s estimation is straightforward. We define a statistical model forincome and expenses as

Ii;t D ˛I C

12XiD2

ˇiDi C "i;t ; (3.7)

Ei;t D ˛E C

12XiD2

�iDi C �i;t ; (3.8)

3 See www.bls.gov/news.release/empsit.t12.htm.



FIGURE 1 Simulated data for income and expenses.

2000

0 6 12 18 24 30 36 42 48 54 60

6000

4000

2500

4500

3500

3000

4000

3500

3000

4500

4000

Va

lue

Time (months)

Balance

Income

Expenses

Valu

eV

alu

e

where Di is a dummy variable that takes the value 1 if the current month is i and 0otherwise. We exclude the dummy for the first month, i D 1, to avoid identificationissues in the regression model. Using the simulated data set as input, we estimate withleast squares the empirical model defined in (3.7) and (3.8). The resulting coefficientsfrom the estimation are omitted. Note that they capture the seasonality of the data.For reproducibility, the R code that generates the data and estimates the model isavailable at https://bit.ly/2X9eaS1.

As an illustration of the use of this model, we set the expected time of employ-ment to unemployment as three years (thirty-six months) and the expected time



of unemployment to employment as three months. These are used to define thetransition probabilities of (3.4) using (3.5) and (3.6).

3.3 Methodology for and results of simulating an individual’sfuture balance and the associated PDs

Given information on the regression models, transition matrix and an initial bal-ance of $1000, we simulate an individual’s future balance by sampling the estimatedresiduals from the regression, "i;t and �i;t , conditional on the month of the year.This simulation allowed for seasonality not only with expected income and expensesbut also with their corresponding volatility. The estimated transition probabilitieswill drive the Markov chain forward. In the simulation, we first assume state 1 attime 0 (S0 D 1): employed. Then, we sample a random number from 0 to 1. Ifthis number is lower than the probability of staying in regime 1, we set S1 D 1.If this random number is higher than the probability of staying in regime 1, weswitch the state to S1 D 2: unemployed. Likewise, following (3.2), if St D 1, thenIi;t D 0; else, Ii;t D FI C MIi;t . We use the same algorithm to drive the Markovchain for t D 2; 3; 4; : : : ; T . We perform 10 000 simulations with a time horizon oftwenty-four months.

Once the balance is simulated, calculating the PD of the individual is straight-forward: it can be done by simply looking at the number of simulated scenarios inwhich the resulting balance was negative for each forward point in time (see (2.7)).Because our first example had no loans, this figure represents the probability of theapplicant falling short on their expenses. We call this figure the benchmark defaultrate curve.

In Figure 2, we note that the PD is also seasonal, with a small drop in month 12,which is when a larger cash income occurs. We also see that the PDs generallyincrease over time, indicating that this applicant is more likely to run out of cashas time passes. While not an intuitive result, this is the effect of the chances of unem-ployment, driven by the transition matrix. Losing income puts pressure on the bal-ance of the individual. Even if the overall chance of unemployment is small, it willstill have a great impact on the forward PD.

We now illustrate a common practical case in which the same applicant asks for aloan that can be paid through monthly installments of $400 for two years or monthlyinstallments of $200 for four years. Given the previous regression for the expenses,implementing this information in the model is easy by defining

Ei;t D ˛E C�C

12XiD2

�iDi C �i;t ;



FIGURE 2 Probability of default.

0

0 6 12 18 24

0.02

0.04

0.06

Pro

babili

ty o

f defa

ult

Time (months)

PD (without loan)

where� is the expected change in expenses represented as the monthly loan paymentvalue. Figure 3 presents the impact on the PD.

Based on Figure 3, the payment of the loan has an explicit impact on the for-ward PD. For monthly payments of $400, the applicant is very likely to default ontheir loan in month 4. However, when setting a monthly payment of $200, the PDdecreases significantly, indicating a better financial contract for both sides. This illus-tration shows the flexibility of the empirical model and could accommodate differ-ent practical scenarios when evaluating credit risk. Next, in Table 1, we present thecalculation of the LGD for each scenario.

As is observed, the first scenario leads to an LGD of 3346.6, which is significantlyhigher than the second case with longer maturity and lower monthly installments. Assuspected, the second setup presents a lower expected cost for the bank.



FIGURE 3 Probabilities of default given a new loan.

0

0 6

0.25

0.50

0.75

1.00

12 18 24

Pro

babili

ty o

f defa

ult

PD (without loan) PD (with $400 pm loan) PD (with $200 pm loan)

Time (months)

Here, “pm” denotes “per month”.

TABLE 1 Risk measures for empirical BaR model.

Case LGD

Installments of $400 per month 3346.6Installments of $200 per month 475.2

3.4 Optimizing parameters of a loan using a BaR model

In practice, the bank receives data from the applicant and must decide on the structureof the offered debt contract, including its maturity and rate of return. If the contract



is perceived as risky, the bank is likely to charge a higher yield rate and set a lowermaturity range. In this section, we present an illustration of how the BaR formulationcan be used to optimize the parameters from the point of view of the bank.

We consider a case in which the applicant requests a loan for 10 000 units of cashand has the same banking records as used in the previous example (see Figure 1). Thedebt is paid back by the applicant in fixed monthly installments that are defined for agiven maturity and annual yield rate. This loan is equivalent to a fixed rate bond withmonthly coupons (Fabozzi and Mann 2012). Using an annual yield rate, we definethe installment value as:

rm D .1C ra/1=12� 1; (3.9)

I D 10 000rm

1 � .1C rm/�T; (3.10)

where rm is the monthly yield of the debt contract, ry is the yearly yield of the debtcontract, T is the maturity of the contract (in months) and I is the fixed value ofmonthly installments.

Equation (3.10) indicates that the increase in the yield rate and the decrease inthe maturity of a contract increase the value of the installments and the risk of thecontract. In the BaR model, a higher installment leads to a higher level of uncertaintyand, consequently, a higher PD.

From the bank side, a natural question to investigate is this: given the expectedcashflow dynamic of the applicant, which values of yield and maturity maximizethe profit of the contract? We analyze this question by defining the objective func-tion as the expected return from the loan, which is calculated on the basis of thesimulation procedure defined in the previous section. Note that the risk is also con-sidered because higher installment values increase the likelihood of a default and,thus, decrease the expected profit.

We first create a vector of values for maturity (T D 1; 2; : : : ; 23; 24) and yields(ry D 0:025; 0:05; : : : ; 0:475; 0:5). For each pair of maturity and yields, we simu-lated the BaR model and calculated the expected return from the contract. We per-formed a grid search procedure to find the pair of maturities and yields that max-imizes the value of E.R/, the expected cashflow divided by the total value of theloan. Our results are presented in Figure 4.

Figure 4 indicates that the pair of maturities and yields that maximizes the con-tract’s expected profit is twenty-three months and a 30% rate of return on the debt.The white spots in the figure show that a low maturity and a higher yield lead to anull return of the contract. This result is explained by the fact that a higher install-ment (see (3.10)) quickly drains the applicant’s balance, leading to a default on thecontract. We also point out that the risk of the contract, LGD (2.8), is minimized witha maturity of eighteen months and a 5% yield. This exercise clearly shows how the



FIGURE 4 Result of grid search procedure.

0.1

0.2

5 10 15 20 25

0.3

0.4

0.5

Maturity (months)

Annual yie

ld r

ate

E(R)

0.03

0.06

0.09

0.12

BaR model can be used to better design short-term financial contracts by taking intoaccount the applicant’s banking history.

4 CONCLUSIONS

In this paper, we propose an alternative model to calculate personal financial risk bydefining a stochastic process for an individual’s cash income and expenditures. For-mal theoretical definitions for this approach along with the usual risk measures arepresented. Using an artificial data set, we illustrate the model’s usage by estimating



an empirical version of the general formulation and calculating the forward PDs ondifferent loan scenarios. We also present an example of an optimization scenario inwhich we find the optimal maturity and yield rate of a short-term loan.

The main advantage of using our approach is its flexibility. By directly employinga stochastic process for income and expenses, we allow seasonality to play a role inthe analysis of short-term credit risk. By using this model, one can verify the impactof several economic factors on an individual’s personal credit risk, eg, loan paymentincreases, specific payment schedule adjustments or job market changes.

Empirical applications of the model are straightforward. On the basis of bankingdata and information from the job market, one can estimate and simulate an indi-vidual’s future income and expenses. As argued in this paper, the creation of a cen-tral organization to store and analyze banking data could facilitate the process andbenefits for both parties in the transaction.

Because banks deal with portfolios of individual loans, one suggestion for a futurestudy is to investigate a multivariate version of the model. Formulating a modelthat incorporates systematic dependencies within a pool of applicants’ cashflows,for which economic shocks such as an increase in general unemployment affect theoverall PD on loans, would be interesting in the analysis of the general risk of abank. Future investigations with real banking data are also suggested. Given individ-ual records, one could study the dynamics of cashflows and test for the structure ofan empirical model that efficiently represents the data set and, thus, provides realisticdefault probability values. Understanding and modeling how applicants react whenfaced with a low balance in their accounts is also suggested. A person is likely toplace a hold on their expenses once their balance passes a particular threshold. Aninvestigation using real data could shed light on this effect. These and other ideas areleft for future development.

DECLARATION OF INTEREST

The authors report no conflicts of interest. The authors alone are responsible for thecontent and writing of the paper.

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a consumer credit risk structural model based on ... · on time. this formulation is best suited to...

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