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I – Motivation

Most studies on bank competition are based upon margins which are not net of credit losses

Banks make their pricing decisions considering not just operational and financial (funds) costs but also the expected percentage credit loss on the loan

Banks make decisions not just on their loan (interest rate) prices but also on their acceptable levels of risk

Actual quantities depend upon not just prices but also on the bank’s decision concerning acceptable expected credit losses

Expected credit losses absent from either the “traditional” “reduced-form” approach and the “structural form” approach to model and measure banking competition

Some recent studies introduced effective credit losses as a control variable, including Bikker and Haaf (2002), Bikker et al. (2006) and Casu and Girardone (2006). Dermine (1986) treated them as exogenous.

This implies that either margins or Lerner indices may be upward biased and that high risk could wrongly be taken market power

The partial endogeneity of credit risk has mostly been ignored. Main exception is Liang (1989), by simultaneously estimating a profit function and a risk function. Martín-Oliver et al. (2007), provide a valuable contribution by estimating loans’ risk premium and adjusting loans’ marginal costs accordingly.

II – Literature reviewBank’s use their own internal credit risk model to determine each borrower’s probability of default (PD) and loss given default (LGD). Expected Loss = PD × LGD × Exposure

Stiglitz – Weiss (1981) expect banks to control their exposure to individual customers (credit rationing)

We model banks as deciding the maximum acceptable expected loss on a loan (this is usually done by setting a scoring cutoff)

Given a certain distribution of borrowers and their losses, setting the maximum expected loss also determines the average expected loss for that group of borrowers. Thus, we use expected average credit losses as the control variable

III – The modelIII – The model: determine each borrower’s probability of default (PD) and loss given default (LGD). Expected Loss = PD modelling expected loan losses

Demand for Loans: determine each borrower’s probability of default (PD) and loss given default (LGD). Expected Loss = PD

Demand for Deposits:

Objective function:

st:

control: rlit ; rdit ; Bit ; it

III – The model: main equationsExample: Price and expected loss of Loans determine each borrower’s probability of default (PD) and loss given default (LGD). Expected Loss = PD

With the additional assumptions:

Similar equations for price of deposits and for branches

We take “conjectural variations” as mere deviations from perfect competition

III – The model: FOC and conjectural variations

We use a determine each borrower’s probability of default (PD) and loss given default (LGD). Expected Loss = PD Banco de Portugal proprietary database that combines both accounting and statistical data for each single bank operating in Portugal. Complete and accurate characterisation of the individual banks and their activities, both in terms of outstanding amounts and at the margin.

Period 1997 - 2004

We obtained data for new loans and deposits granted/accepted at each period (quarter), thus being able to use effective marginal prices rather than average prices as in most studies.

We use each bank’s average effective losses during the period as a proxy of their expected losses

Data on rivals: for interest rates and expected losses, weighted average of the (n – 1) rivals; for branches, sum of all (n – 1) rivals’ branches.

IV – The data

IV – The data: determine each borrower’s probability of default (PD) and loss given default (LGD). Expected Loss = PD interest rate differentials

The system of non-linear equations using the full information maximum likelihood technique (FIML);

Starting values were obtained from single equation estimates of Lit and Dit before estimating all the equations jointly (fixed effects)

White (1980) robust estimates for the variance-covariance matrix of the parameters.

IV – The data: estimation procedures

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans
- Deposits and loans more sensitive to own rates than to rivals’

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans
- Deposits and loans more sensitive to own rates than to rivals’
- Branches more effective on deposits

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans
- Deposits and loans more sensitive to own rates than to rivals’
- Branches more effective on deposits
- Banks’ loan quantities sensitive to other banks’ credit policies

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans
- Deposits and loans more sensitive to own rates than to rivals’
- Branches more effective on deposits
- Banks’ loan quantities sensitive to other banks’ credit policies
- Size and market concentration: + collusive behaviour in the loans’ market

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans
- Deposits and loans more sensitive to own rates than to rivals’
- Branches more effective on deposits
- Banks’ loan quantities sensitive to other banks’ credit policies
- Size and market concentration: + collusive behaviour in the loans’ market
- In the deposits’ market: size and market concentration have opposite effects

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans
- Deposits and loans more sensitive to own rates than to rivals’
- Branches more effective on deposits
- Banks’ loan quantities sensitive to other banks’ credit policies
- Size and market concentration: + collusive behaviour in the loans’ market
- In the deposits’ market: size and market concentration have opposite effects
- Higher concentration leads to lower impact on rival’s credit policies (relationship banking effect?)

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans
- Deposits and loans more sensitive to own rates than to rivals’
- Branches more effective on deposits
- Banks’ loan quantities sensitive to other banks’ credit policies
- Size and market concentration: + collusive behaviour in the loans’ market
- In the deposits’ market: size and market concentration have opposite effects
- Higher concentration leads to lower impact on rival’s credit policies (relationship banking effect?)
- Larger banks (MS) tend to have higher impact on rival’s credit policies

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans
- Deposits and loans more sensitive to own rates than to rivals’
- Branches more effective on deposits
- Banks’ loan quantities sensitive to other banks’ credit policies
- Size and market concentration: + collusive behaviour in the loans’ market
- In the deposits’ market: size and market concentration have opposite effects
- Higher concentration leads to lower impact of rival’s credit policies (relationship banking effect?)
- Larger banks (MS) tend to have higher impact on rival’s credit policies
- High inertia on the branching variable

V – Main findings information maximum likelihood technique (FIML);

- Low own price elasticities
- Deposits more inelastic than loans
- Deposits and loans more sensitive to own rates than to rivals’
- Branches more effective on deposits
- Banks’ loan quantities sensitive to other banks’ credit policies
- Size and market concentration: + collusive behaviour in the loans’ market
- In the deposits’ market: size and market concentration have opposite effects
- Higher concentration leads to lower impact of rival’s credit policies (relationship banking effect?)
- Larger banks (MS) tend to have higher impact on rival’s credit policies
- High inertia on the branching variable
- Extension of branching network relies heavily on loans’ margins

THANK YOU! information maximum likelihood technique (FIML);

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