Credit Crunch and Saving Glut in Taiwan: Empirical Evidences Cheng-Few Lee Distinguished Professor...

Credit Crunch and Saving Glut in Taiwan: Empirical

Evidences

Cheng-Few Lee Distinguished Professor of Finance, Rutgers University, USA

Chiung-Min TsaiCentral Bank of the Republic of China (Taiwan)

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Abstract

To indentify the developments for bank credit allocation in Taiwan Using a disequilibrium model to evaluate whether

An excess demand, i.e. a credit crunch, or Excess supply, i.e. a saving glut, during these periods

Major findings A considerable excess demand, or credit crunch, during Asian

financial crisis On the contrary, there were some periods experiencing an excess

supply from 2001 to 2004, or the “saving glut” as mentioned by Bernanke (2005)

Furthermore, the saving glut was still prevailing during the periods of global financial crisis

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Outline

I. INTRODUCTION

II. A DISEQUILIBRIUM MODEL OF THE BANK LOAN MARKET

A. Development of Disequilibrium Model for Bank Loan Market B. Estimation Methods

III. TESTING THE EXISTENCE OF PRICE ADJUSTMENT PROCESS IN TAIWAN’S COMMERCIAL LOAN MARKET

A. Model Specification B. Empirical Results

IV. SUMMARY

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The concept of credit rationing is first developed by Jaffee (1971) for a commercial loan market

One of the reasons for credit rationing is the existence of bankruptcy costs, as proposed by Miller (1977) As a result, some firms will not receive the loan regardless of the rate

they are willing to pay

Sealey (1979) extends the study and uses a generalized disequilibrium model (DM) to examine whether there is an excess demand in the commercial loan market The empirical results show that models of commercial loan market

being unable to explicitly include the effects of market disequilibrium are likely to yield inconsistent parameter estimate

I. INTRODUCTION

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Nehls and Schmidt (2003) examine whether the Germany economy is affected by a credit crunch in the early years of 2000 The empirical results suggest a supply-side restriction of loans is not in

line with market interest rates and profitability of investment projects A considerable excess demand, i.e., a credit crunch, particularly in the

second half of 2002

Financial liberalization and deregulation reduce the magnitude of disequilibrium; However, the risk of credit crunch can’t be ruled out

The global imbalance and financial crisis tell us that Sometimes the price adjusts too slow to clear the market, and Sometimes the government has to step in

The insightful value of DM should be explored widely in practice

I. INTRODUCTION

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A. Disequilibrium Model

II. DEVELOPMENT OF DISEQUILIBRIUM MODEL FOR BANK LOAN MARKET

In general, markets are in equilibrium

Supply:

QS = α1 +α2P + α3X + εS (α2 > 0)

Demand:

QD =β1+ β2P + β3Y + εD (β2 < 0)

Equilibrium

QS = QD = Q*

But, sometimes are not

Supply

Demand

P*

Q*

P

Qd, Qs

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All disequilibrium models share the feature that prices do not fully adjust to the market clearing level

The DM consists of the following equations:

(1) QDt = α1 Pt + β1’XD,t + μt,

(2) QSt = α2 Pt + β2’XS,t + υt ,

(3) Qt = min (QDt, Q

St ),

(4) ΔPt = Pt - Pt-1 = γ(QDt - Q

St ),

γ: price adjustment factor, 0<γ∞


P

Qd, Qs

Supply

Demand

P*

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where the QDt and QS

t are the quantities of securities demanded and

supplied, respectively

Qt is the actual or observed quantity of securities in the market; Pt is the

observed return rate of securities

XD,t and XS,t are vectors of exogenous or predetermined variables including

the lagged Pt-1

α1 and α2 are unknown parameters for Pt; β1 and β2 are vectors of unknown

parameters for exogenous variables

γ is an unknown positive scalar parameter; and

μt and νt are disturbance terms and assumed to be jointly normal and

independent over time with distributions N(0, σ2μ) and N(0, σ2

υ)


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In the equilibrium model, the existence of supply effect is the core issue

In the DM, the parameter of the most interested is γ in (4)

If γ = 0, then, there is no price adjustment in response to an excess demand, and

If γ is infinity, price adjusts instantaneously

In other words, if one assumes there is price rigidity in response to an excess demand, γ should equal to zero

i.e., the most important test of the disequilibrium model is to examine whether the price adjustment parameter is zero


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One can reformulate the above model by considering periods of rising prices ΔPt > 0 and periods of falling prices ΔPt < 0

The equations (1) to (4) can be reformulated and summarized as:

(5a)

(5b)

where

otherwise

PifPP ttt

0

0

otherwise

PifPP ttt

0

0

B. Estimated Methods


tttt PPQ

11 D,t

'1 Xβ

tttt PPQ

11 S,t

'2 Xβ

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1. 2SLS Estimator The equations system shown in (5) contains the jointly dependent

variables Qt, Pt, ΔPt+ and ΔPt

-

The parameters in the above modified model can be consistently estimated by the conventional 2SLS method

In the first stage, regress and on all of the exogenous variables, XD, t and XS, t to obtain the estimations of and then,

In the second stage, regress Qt on XD, t and in equation (5a),

and regress Qt on XS, t and in equation (5b)

The estimators of , , and could be consistent, but

are not asymptotically efficient in this model

1 21 2

tPˆ

tPˆ

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2. Maximum Likelihood Estimator

Quandt (1988) employs an appropriately formulated full-information maximum likelihood technique

He suggests one can use the log likelihood function represented as equation (6) to find the ML estimator

(6)

where

t

tt uuT

TTL 1'12 2

1||log

22log|

1|log


)1

,1

( S'

2D'

1'

ttttt,tttt PPQPPQu

XX 21

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Amemiya (1974) suggests the MLE can be obtained by solving the following equations simultaneously


(7) (a) LSMLE ,1,1 LSMLE ,1,1 ββ ,

(b) LSMLE ,2,2 LSMLE ,2,2 ββ ,

(c) 22 21 , 1 1 ,

1 1( ) ( )t t D t t t D

A B

Q P Q P PT

' '

1 t tβ X β X ,

(d) 22 22 2 , 2 ,

1 1( ) ( )t t S t t t S

B A

Q P Q P PT

' '

t 2 tβ X β X ,

(e) 2 2 , 1 1 ,2 2

1 1 1 1( ) ( ) 0t t t S t t t t D t

A B

T Q P P P Q P P P

' 't tβ X β X .

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That is, the ML estimators of α and β are the same as LS estimators given γ applied to equation (5).

Amemiya suggests the above parameters can be solved by using the following iterative procedures

Step 1: Use 2LSL estimates of α, β, σ2μ and σ2

υ as the

initial estimates

Step 2: Substitute , , and into (7e) and solve

for the positive root of γ,

Step 3: Use in (5) to obtain least squares estimates of

α, β, σ2μ and σ2

υ

Step 4: Repeats step 2 and 3 until the solutions converge

2ˆ 2ˆ


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Demand Function

where QDt is the demand for commercial loan (in DM, this can’t be observed)

rt is the observed commercial loan rate,

IPt is the index of industrial production, and

Xt is the growth rate of export

The 2nd variable, the index of industrial production, can measure the firm’s expectations about future economics activity, and thus, is used as a typical specification in many studies

In Taiwan, export sector has played a very important role in economic performance. And thus, firms’ demand for commercial loan is affected by the growth rate of export

A. Model Specification

QDt = α1rt + β11 IPt + β12 Xt + μ1 t

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Supply Function

where QSt is supply of commercial loan,

TDt is the amount of total deposits, and

ORt is the interbank overnight interest rates

The TDt is used to capture the capacity of the banking sector to

lend, which is the major variable to the supply function

The 3rd variable, ORt, is used for two reasons

This rate can be seemed as an indicator of monetary policy direction, and

It is also related to the banks’ profit margin indirectly as well

QSt = α2 rt + β21 TDt + β22 ORt + μ2 t

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The expected signs of the coefficients should be as follows The sign of α1and α2 should be negative and positive respectively since a

higher price (i.e. loan rate) means a decline in demand but encourages bank to lend more

β11and β12 are expected to be positive since a prosper economic activities

boost demand for loan. And finally

β21 and β22 should be positive and negative respectively

For the former, an increase in total deposits gives the banking industry more ability to lend

For the latter, a lower interbank overnight rate will increase one bank’s profit margin and it also means an easing in monetary policy. Both of the reasons will encourage banks to lend more


B. Empirical Results

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Table 1. MLE of Commercial Loan Market

Parameter Estimates Std. Error z-statistic p-value

α1 -0.6720 0.2236 -3.00 (0.0027)

β11 0.3443 0.0703 4.90 (0.0000)

β12 1.3562 0.3796 3.57 (0.0004)

α2 2.9898 1.4670 2.04 (0.0415)

β21 2.2138 0.0838 26.42 (0.0000)

β22 -3.9260 1.8813 -2.09 (0.0369)

γ 0.0135 0.0009 15.55 (0.0000)

Table 1 summarizes the MLE of the presented DM. From the table, the signs of the

estimated parameters are all in the correct direction and at a significance level of 5%

Data used are from 1994Q3 to the end of 2009. This is because though the banking industry was deregulated in 1989, the number of institutions and branches was at peak around 1994. The business of banking industry then becomes more stable and reliable

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The parameter of most interest in the model, the market adjustment parameter γ, is slightly larger than zero and significant Both extreme values, zero and infinity, are excluded in a confidence interval

(even a fifteen times of the standard error) This reflects the partial adjustment mechanism, the most special character of

a disequilibrium model

As we obtain the estimates of the parameters, we can trace back to find out the real or the desired supply and demand for commercial loan

And then, using the difference between the desired demand and supply, divided by supply, we can calculate the excess demand (Figure 1)



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Figure 1. Excess Demand for Bank Loans

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

-

0.20

0.40

0.60

0.80

1.00Excess demand ratio (in % of credit supply) 2 standard deviation

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According the calculation, there are three periods of time in a situation of credit crunch. They are 1996Q1, 1997Q1 to 1998Q1, and 2007Q2 The first one can be related to the Taiwan Strait tension in 1996 The second credit crunch is coincident with the Asian financial crisis The third one is related to a near-bank-run event and a tension in

money market in early 2007

The figure also shows that there exists excess supply in some periods, especially during the periods from 2001 to 2004 This may be reflecting the so-called saving glut as Bernanke (2005)

mentioned.



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To indentify the developments for bank credit allocation in Taiwan Using a disequilibrium model to evaluate whether

an excess demand, i.e. a credit crunch, or excess supply, i.e. a saving glut, since 1994

Major findings A considerable excess demand, or credit crunch, during Asian financial crisis

The main cause of this credit crunch can be contributed to the tightening in the money market because of capital outflow

On the contrary, there were some periods experiencing an excess supply from 2001 to 2004, or “saving glut” as mentioned by Bernanke (2005)

Furthermore, the saving glut was still prevailing during the periods of global financial crisis, this is probably due to

The aggressive and pre-empt policy actions taken by the financial and monetary authorities to avoid a possible meltdown in credit market

IV. SUMMARY

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Discussions According to our specification of supply function, the overnight interest and

bank deposits are two major exogenous variables The authorities lowered the interest rate fast enough and adopted the policy

of blanket guarantee on deposits in time had supported the banking sector to resist the crisis

Debates Though existing debates in the model specification

For example, lack of some variables such as the ROE or the profit margin etc.

However, all the coefficients estimated in both functions are at the expected directions and are significant

Beside, the periods of excess demand/supply for bank loan are coincident with those periods of some specific economic events

These evidences have showed the disequilibrium character in Taiwan’s bank loan market successfully and are helpful in policy discussion

IV. SUMMARY

Credit Crunch and Saving Glut in Taiwan: Empirical Evidences Cheng-Few Lee Distinguished Professor...

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