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4 .

VOL. 24 | NO.4 | 2010.12

2009 ( ) .

()

(), ()

() ()

() ()

() ()

() ()

() ()

() ()

() ()

() ()

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William N. Goetzmann(Yale School of Management)

David Hirshleifer(University of California at Irvine)

Sheridan Titman(The University of Texas at Austin)

Jun-Koo Kang(Nanyang Technological University)

Hyung-Song Shin(Princeton University)

Bong-Soo Lee(Florida State University)

Kee-Hong Bae(York University)

Yeon-Koo Che(Columbia University)

Wi Saeng Kim (Hofstra University)

E-mail: [email protected]

100-021 1 4-1

8

02) 3705-6325

( 386-01-021236,

: )

.

,

.

4 .

VOL.24 | NO.4 | 2010.12

Article

/ 1Financial Regulation and Liquidity Risk

(Jong Ku Kang)

/ 49The Impact of International Financial Shocks on the Volatility of Domestic Financial Markets

(Keun Yeong Lee)

/ 87An Analysis of Default and Prepayment in Korean Mortgage Markets

(Doowon Bang), (Sae Woon Park), (Yun Woo Park)

DSGE / 119A Simple, Implementable, and Optimal Monetary Policy in a DSGE Model with Incomplete Financial Markets

(Yongseung Jung)

: , , , ,

1

||||||| Journal of Money & Finance | Vol.24 | No. 4 | 2010. 12 1)

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. Pyle-Hart-Jaffee

BIS

, ,

.

,

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: , ,

JEL : E61, G21, G28

2010 09 13; 2010 10 20; 2010 12 06

* .

** (Tel : 02-759-5418, E-mail : [email protected])

2 24 4 2010

.

(BIS, 2006).

.

.

.

(Northern Rock) BIS

(Shin, 2009; , 2010). (Bear Sterns)

BIS

(Morris and Shin, 2008).

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, Shin and Shin(2010) /

. 1 , 1

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(Liquidity Coverage Ratio : LCR) /30

1)

.

4 24 4 2010

.

30

30

. 100%

1 .

1998

1 .

.

(Net Stable Funding Ratio : NSFR) .

1

. /

100%

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1 ,

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. 1998 11

.

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2) .

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.

. Shin and Shin(2010)

.

Shin and Shin(2010)

. .

4)

.

.

.

.

(-)/

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.

3) 2008 2

27.3% 2008 4 30.7% DB

2008 2 51.6% 2008 4 53.3% .4) 2005. 122007. 12

40 21

. , 2007

12.2% .

6 24 4 2010

2.

(

-)/ 2000

2008 3 33.4% .5) 2000

. 2000 12006 4 2007

12008 3 .

/ , , (+)/

(-)/

.

Commercial Bank's Liquidity Risk Indexes

0%

10%

20%

30%

40%

50%

'00.1 '01.1 '02.1 '03.1 '04.1 '05.1 '06.1 '07.1 '08.1 '09.1 '10.10.5

0.7

0.9

1.1

1.3

1.5

1.7

Non Depos it/ TF(left scale)

Loan-Depos it Ratio(right s cale)

(Non Depos it-Safe Asset)/ TF ( left s cale)

(Depos it+Capital)/ Ris ky Asset ( right s cale)

Note) TF stands for Total Funding.

Source : Korea Financial Supervisory Service DB.

5) , , .

. .

7

(-)/

, , .

.

(

-)/ .

.

2004 0

2006 2008 2

. 20002004

. (

-)/

. 2005

2006 2009 2 .

2008 2009

.

(Non Deposit-Safe Asset)/Total Funding Ratio by Bank Group

-20%

0%

20%

40%

60%

80%

'00 .1 '01 .1 '02 .1 '03 .1 '04 .1 '05 .1 '06 .1 '07 .1 '08 .1 '09 .1 '10 .1

Fo reign Bank B ranches

S pecialized Banks

Nationwide Banks

Local Banks

Source : Korea Financial Supervisory Services DB.

8 24 4 2010

Non Deposit and Safe Asset Ratios by Bank Group

Source: Financial Supervisory Services DB.

(-)/

/ /

/

/ .

/ (-

)/ . 2007

/ /

(-)/

.

3.

(-)/

.

9

(-)/

.

2008 4

.

Liquidity Support to Commercial Banks

year.quarter 2008. 2 2008. 3 2008. 4 2009. 1 2009. 2 2009. 3

Borrowing from the Public Sector1) 83,291 83,745 116,417 109,169 114,358 115,786

Financial Support2) - - 32,672 25,424 30,613 32,041

Degree of Financial Support3) - - 0.34 0.26 0.32 0.34

Note) 1) The sum of commercial banks borrowing from the BOK and the government and the BOK

R/P purchase at end of period.

2) Difference between Borrowing from the Public Sector at 2008. 3q and that at each quarter.

3) ('Financial Support'/Total Liability) Ratio.

Unit : 100 million, %.

Source : Financial Supervisory Service DB.

(-)/

2008 42009 3 6)

2007 4 , 2008 1 , 2 , 3 (

-)/ 0.50 .

(-)/

. 2008 2 2008 3

. 2008 2

(-)/

2008 3 (-

)/ . 2008 2

.

6) 2008. 42009. 3 .

10 24 4 2010

Correlation between(Non Deposit-Safe Asset)/Total LiabililtyRatio and the

Degree of Financial Support

Time of Liquidity Risk Index Period of Financial Support

2007. 4 2008. 1 2008. 2 2008. 3

2008. 42009. 3 0.589 0.701 0.764 0.708

Correlation between Loan-Deposit Ratio and the Degree of Financial Support

Time of L-D Ratio Period of Financial Support

2007. 4 2008. 1 2008. 2 2008. 3

2008. 42009. 3 0.528 0.534 0.616 0.325

. 2008 4

2009 3 2007

4 , 2008 1 , 2 .

.

(-)/

.

.

1.

(bank run) . Diamond and

Dybvig(1983) 3

.

11

(bank run)

.

.

.

Diamond and Dybvig(1983)

.

Bougheas(1999)

. Cooper and

Rossi(2002)

.7) Ennis and Keister(2006)

.

Schotter and Yorulmazer(2009)

.

. Peck and Shell(2010)

.

. Uhlig(2010)

.

.

. Wetmore(2004)

.

7) Merton(1977) .

12 24 4 2010

.

. ,

.

.

Shin and Shin(2010) /

.

.

.

.

.

Kim and Santomero(1988)

2 .

.

.

. ()

(). Rochet(1992)

Kim and Santomero(1998)

(Limited Liability)

. Franck and Krausz(2007)

LLR(Lender of Last Resort)

. 3 (0, 1, 2)

13

. 1

. 1

LLR .

LLR

.

.

.

.

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2.

.

Pyle-Hart-Jaffee (Pyle, 1971; Hart and Jaffee,

1974). Monti-Klein ,

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(2010)

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, (), ()

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2 1 1

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14 24 4 2010

. 0 , , ,

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2 . 1 , ,

. 2 , , ,

.

Financial Market Situation and Features

period Financial Market Situation

0 stable

1 stable unstable

(low interest rate of (high interest rate of risky liability) risky liability)

2 (high return on risky asset) (low return on risky asset)

. 0 .8) 1

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. 2010 3

(commercial bank) HHI 1468

. 10) Microeconomics of Banking(2008), pp. 78-79 .11) , ,

() . 12) 0 1

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18 24 4 2010

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20042007

. (CPI)

2.8%

2.8% .

16)

(Sharpe, 1964; Lintner, 1965; Markowitz, 1952 ).17) (14) .

20 24 4 2010

2.8%

.

(3) ( = +

) 0.028 .

CD, 20042007

18) 3.9%, /

4.5 . 0.039 ,

() 1 4.5 .

((0.039) = (0.028) +

(4.5)) .

0.002 0.002 .

.

, . 20042007

=

0.68% . (

/) 2.9% (2.9%)

(0.68%) 3.58% .

.

1.4% 0.68%

2.1% . 0.021 .

9.6 (0.0358) =

(0.021) + (9.6)

0.0016 . 0.0016

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7.5% . 12% 19)

12% .

(5) =

0.12 .

2%

0.10 .20) 20042007 (+)

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(0.075) = (0.12) (12.8)

0.003 0.003

.

20042007 , , ,

2.3

(3 ) (2) 4.6% .

(5 )

4.9% 0.049

. (0.046) = (0.049)

(2.3) 0.001

0.001 .

10

0.9 Arrow(1971)

1.0 . 0 2004

2007 9.6 9.6 .

()

1% 0.01 . 1

19) 20042007 12% 0.7%,

0.1%, 0.3%. 20) 20042007 ROA(/) 1.06%

2% ROA - 1% < ROA < 0 . 21)

.

22 24 4 2010

20042007 0.001

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0 (),

.22) 20042007 , ,

, ( ) 9.6, 4.5, 12.8, 2.3

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.23)

3.

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22) negative definite

2 (Chiang, 1984; pp. 332-333),

.23)

.

23

.

.

BIS

, , ,

, ,

.

(1)

. BIS

/

(11)

. (11)

. .

(11)

(7) (11)

. .

. (11) ()

BIS ()

. BIS 8.18% BIS

BIS 8.18%

. (11) 0.0818

. D (), M (),

L (), S (), A .

24 24 4 2010

.

. (M/A)

. (-)/ ((M-S)/A)

. (L/D)

. BIS

.

The Effect of Strengthening Capital Ratio() Regulation

0

5

10

15

0 0.01 0.02 0.03 0.04

D M L S

-10%

0%

10%

20%

30%

40%

0 0.01 0.02 0.03 0.04

M/A S/A (M-S)/A L/D

Note) Hereafter, D, M, L, S and A stand for Safe Liability, Risky Liability, Risky Asset, Safe Asset and

Total Asset(Total Funding), respectively. The scale of Loan-Deposit Ratio(L/D) is reduced by 10%.

The scale of the horizontal axis represents the difference between regulated capital ratio and the

capital ratio in section 2, Chapter 3.

(2)

. BIS

BIS

.

(/) BIS

.

BCBS(Basel Committee on Banking Supervision)

24)(G-20

(2010. 5)).

25

Koehn and Santomero

(1980), Kim and Santomero(1988), Rochet(1992), Calem and Rob(1996), Blum(1999)

/ ( )

.

Kamada and Nasu(2010)

.

Furlong(1988)

19811986

Shrieves and Dahl(1992)

2

.

BIS

( )

(Stolz, 2002).

.

.

.

BIS

(Net Stable Funding Ratio)

.

24) , , ,

.

26 24 4 2010

. ,

(G-20 (2010)).

.25)

.

.

/

(12) .

(12) .

(12)

.

. (/)

14.64 14.64

. (12) 14.64 .

.

.

.

/ .

(-)/

25) 2009 16.22, 14.15, 13.08,

9.26.

27

.

.

(-)/

. .

The Effect of Strengthening Leverage Ratio() Regulation

0

5

10

15

0 1 2 3 4

D M L S

-10%

0%

10%

20%

30%

40%

0 1 2 3 4

M/A S/A (M-S)/A L/D

Note) D, M, L, S and A stand for Safe Liability, Risky Liability, Risky Asset, Safe Asset and Total

Asset(Total Funding), respectively.

The scale of the horizontal axis represents the difference between regulated leverage ratios and

the leverage ratio in section 2, Chapter 3.

.

(market discipline)

.

.

(3)

(Core Capital) (Supplemen-

28 24 4 2010

tary Capital) .

.

.

.

.

.

(Core Capital)

(Limited Liability).

.

.

.

.

. ,

,

2

(13) .

(14) .

(14)

.

(13)

(14)

29

.

> (13)

< (14) .

(14)

.

.

/ (

-)/ .

(-)/

.26)

Liquidity Risk Indexes depending on Core Capital

key indexes Large Core Capital Small Core Capital

Risky Liability/Total Funding 27.64% 27.80%

Safe Asset/Total Funding 16.45% 15.53%

(Risky Liability-Safe Asset)/Total Funding 11.19% 12.27%

Loan-Deposit Ratio 127.5% 129.1%

26) .

. (16)

. (16)

. (16)

. , ,

, (15) (16)

.

30 24 4 2010

(4)

.

.

.

.

.

. .

(15)

0% 40%

.

The Effect of Raising Taxes on the Bank's Profit( )

0

5

1 0

1 5

0 0 .1 0 .2 0 .3 0 .4

D M L S

10%

20%

30%

0 0 .1 0 .2 0 .3 0 .4

M/A S/A (M-S)/A L/D



The scale of the horizontal axis represents the rate of tax on bank profit.

31

(

)

.

.27)

. ()

0 0.5% .

/ (-

)/ .

The Effect of Raising Taxes on the Non Deposit Liability( )

0

2

4

6

8

10

12

14

0 0 .00125 0 .0025 0 .00375 0 .005

D M L S

0%

10%

20%

30%

0 0.00125 0 .0025 0 .00375 0 .005

M/A S/A (M-S)/A L/D



The scale of the horizontal axis represents the rate of tax on the non deposit liability

(5)

.

.

.

27) 2010 1 0.15%

.

32 24 4 2010

.

. (16) .

(16)

() 0 20%

(-

)/ .

.

The Effect of Raising Levy on the Bank Profit( )

0

5

10

15

0 0.05 0.1 0.15 0.2

D M L S

10%

20%

30%

0 0.05 0.1 0.15 0.2

M/A S/A (M-S)/A L/D



The scale of the horizontal axis represents the rate of levy on the bank profit.

33

.

.

0 1 .

.

.

()

.

.

() .

.

.

(17)

.

(17)

0% 0.3%

(-)/

.28)

34 24 4 2010

The Effect of Raising Levy on the Non Deposit Liability()

0

2

4

6

8

10

12

14

0 0.00075 0.0015 0.00225 0.003

D M L S

0%

10%

20%

30%

0 0.00075 0.0015 0.00225 0.003

M/A S/A (M-S)/A L/D



The scale of the horizontal axis represents the rate of levy on the non deposit liability.

.

.

.

.29)

28) 10

0.25% . 29) , ,

.

.

. 100% ()

() . ++ = +

- = . (-

)/ .

-(/) . 100%

(-

35

Changes in the Liquidity Risk After Imposing Tax or Levy

type

objectBank Tax Bank Levy

Profit a little change Increase

Non Deposit Decrease a little change

.

.

.

. (

) .

()/

.

.

.

( - )/

2000

20072008 3/4 .

/ .

)/ . 100%

(-)/

.

36 24 4 2010

.

Pyle-Hart-Jaffee

Monti-Klein

. 2, 1

. 1

. 0

.

.

() , () ,

,

.

.

.

BIS

, (

)/ .

.

.

.

.

.

37

.

.

BIS , ,

.

,

.

38 24 4 2010

1. , , , 16 2, 2010, .

2. , ,

, 2010.

3. G-20 , G-20 , G-20

/ , , 2010.

4. Arrow, K. J., Essays in the Theory of Risk-Bearing, Markham, Chicago, IL 1971.

5. Aggeler, Heidi and Ron Feldman, Is the loan-to-deposit ratio still relevant?, Fedgazette,

The Federal Reserve Bank of Minneapolis, July 1998.

6. BIS, The Management of Liquidity Risk in Financial Groups, Basel Committee on

Banking Supervision, The Joint Forum Paper, May 2006.

7. Blum, Jurg, Do capital adequacy requirements reduce risks in banking?, Journal of

Banking and Finance 23, 1999, 755-771.

8. Bougheas, S., Contagious bank runs, International Review of Economics and Finance

8, 1999, 131-146.

9. Calem, Paul and Rafael Rob, The Impact of Capital-based Regulation on Bank Risk-

taking : A Dynamic Model, Finance and Economics Discussion Paper, 1996-12, Board

of Governors of the Federal Reserve System, 1996.

10. Cooper, Russel and Thomas Rossi, Bank runs : Deposit insurance and capital require-

ments, International Economic Review 43(1), 2002, 55-72.

11. Diamond, D. W. and P. H. Dybvig, Bank runs, deposit insurance, and liquidity,

Journal of Political Economy 91(3), 1983, 401-19.

12. Ennis, Huberto and Todd Keister, Bank runs and investment decisions revisited,

Journal of Monetary Economics 53, 2006, 217-232.

13. Furlong, Frederick, Changes in Bank Risk-Taking, Federal Reserve Bank of San

Francisco Economic Review, Spring 1988, 45-56.

14. Franck, Raphael and Miriam Krausz, Liquidity risk and bank portfolio allocation,

International Review of Economics and Finance 16, 2007, 60-77.

15. Hart, O. and D. Jaffee, On the application of portfolio theory of depository financial

intermediaries, Review of Economic Studies 41(1), 1974, 129-146.

39

16. Kamada, K. and K. Nasu, How Can Leverage Regulations Work for the Stabilization

of Financial Systems?, Bank of Japan Working Paper Series, No.10-E-2, March 2010.

17. Kim, Daesik and Anthony Santomero, Risk in Banking and Capital Regulation, The

Journal of Finance 43(5), 1988, 1219-1233.

18. Koehn, Michael and Anthony Santomero, Regulation of Bank Capital and Potfolio

Risk, Journal of Finance 35, 1980, 1235-1250.

19. Lintner, J., The vluation of risk assets and the selection of risky investments in stock

portfolios and capital budgets, Review of Economics and Statistics 47, 1965, 13-37.

20. Markowitz, H., Portfolio selection, Journal of Finance 7(1), 1952, 77-91.

21. Merton, R., An analytic derivation of the cost of deposit insurance and loan

guarantees, Journal of Banking and Finance 1, 1977, 3-11.

22. Morris, S. and H. S. Shin, Financial Regulation in a System Context, Conference Paper

for Brookings Panel meeting on Sep. 2008.

23. Peck, James and Karl Shell, Could making banks hold only liquid assets induce bank

runs? Journal of Monetary Economics 57, 2010, 420-427.

24. Pyle, D., On the theory of financial intermediation, Journal of Finance 26(3), 1971,

737-747.

25. Rochet, Jean-Charles, Capital requirements and the behavior of commercial banks,

European Economic Review 36, 1992, 1137-1178.

26. Schotter, Andrew and Tanju Yorulmazer, On the dynamics and severity of bank runs:

An experimental study, Journal of Financial Intermediation 18(2), 2009, 217-241.

27. Sharpe, W., Capital Asset Prices : A theory of market equilibrium under conditions

of risk, Journal of Finance 19, 1964, 425-442.

28. Shin, H. S. and K. H. Shin, Macroprudential Policy and Monetary Aggregates, Bank

of Korea Conference Paper 2010.

29. Shin, H. S., The Fundamental Principles of Financial Regulation, Geneva Report on

World Economy 11, 2009.

30. Shrieves, R. and D. Dahl, The Relationship between Risk and Capital in Commercial

Banks, Journal of Banking and Finance 16, 1992, 439-457.

31. Stolz, Stephanie, The Relationship between Bank Capital, Risk-Taking, and Capital

Regulation: A Review of the Literature, Kiel Working Paper 1105, Kiel Institute for

World Economics, February 2002.

40 24 4 2010

32. Uhig, Herald, A model of a systemic bank run, Jorunal of Monetary Economics 57,

2010, 78-96.

33. Wetmore, Jill, L., Panel Data, Liquidity Risk, and Increasing Loans-to Core Deposits

Ratio of Large Commercial Bank Holding Companies [1992~2000], American Business

Review June 2004.

41

()

(M/A) .

. / (M/A) (S/M)

(-)/ ((M-S)/A) .

/

(L/D) .

The Effect of Changes in Safe Debt Funding Cost()

0

2

4

6

8

10

12

14

0.6 0 .8 1 1.2 1 .4

D M L S

-10%

0%

10%

20%

30%

40%

0.6 0.8 1 1.2 1.4

M/A S/A (M-S)/A L/D

Note) Hereafter, the scale of Loan-Deposit Ratio(L/D) is reduced by 10%. The horizontal axis represents

the percentage change of each exogenous variable from the basis value in the section 2 Chapter

III.

()

(M/A) .

(S/A) . (-)/

((M-S)/A) (M/A) .

(L/D) .

42 24 4 2010

The Effect of Changes in Risky Debt Funding Cost()

0

2

4

6

8

10

12

14

0.6 0.8 1 1.2 1.4

D M L S

-10%

0%

10%

20%

30%

40%

0.6 0.8 1 1.2 1.4

M/A S/A (M-S)/A L/D

(

)

(S/A) . (-)/

((M-S)/A) (S/A) .

(L/D) () .

The Effect of Changes in Risky Asset Return

0

5

10

15

20

0.6 0.8 1 1.2 1.4

D M L S

-10%

0%

10%

20%

30%

40%

0.6 0.8 1 1.2 1.4

M/A S/A (M-S)/A L/D

()

(S/A)

(-)/ ((M-S)/A) . (L/D)

.

43

The Effect of Changes in Safe Asset Return

0

5

10

15

0.6 0.8 1 1.2 1.4

D M L S

-10%

0%

10%

20%

30%

40%

0.6 0.8 1 1.2 1.4

M/A S/A (M-S)/A L/D

()

.

(S/A) (-

)/ ((M-S)/A) .

.

The Effect of Changes in the Probability of Financial Market being Stable

0

5

10

15

0.6 0.7 0.8 0.9 1

D M L S

0%

10%

20%

30%

40%

0.6 0.7 0.8 0.9 1

M/A S/A (M-S)/A L/D

Note) The horizontal axis represents changes in

()

(-)/

((M-S)/A) (L/D) .

44 24 4 2010

The Effect of Changes in the Bank Risk Aversion

0

5

10

15

0 1 2 3 4

D M L S

10%

20%

30%

0 1 2 3 4

M/A S/A (M-S)/A L/D

45

The Effect of Introducing both Leverage

Regulation and BIS Capital Regulation

BIS

. . (3) (4) BIS

,

. BIS

(AB)

(CD) .

Bank's Choice under both BIS Capital Regulation and Leverage Regulation

C1

C2

A B1 E1 B2 B3 B

O D2 D1

BIS E1

. E1 E1

E1 .

C2D2 AB2D2O

. AB2 B2D2

46 24 4 2010

. C2D2

B3 B3 ( AB)

( C2D2) .

B1 ( AB)

( C2D2) B2

. AB2 B2D2

AB2 E1 B2 B2D2

B1 B2 . BIS

.

B1 . B1 BIS

BIS

B2 . AB B2

. B2

.

47

< Abstract >30)

Financial Regulation and Liquidity Risk

Jong Ku Kang*

After the global financial crisis, it is widely recognized that liquidity

risk management is indispensible for macro economic stability. Northern

Rock (UK) and Bear Stearns (US) could not avoid bankruptcy due to liquidity

risk problem even though their capital adequacy and asset soundness did

not pose serious threat to the banks stability. Contagion of systemic risk

among banks is contributed mainly by lack of adequate liquidity risk

management.

Banks liquidity risk needs to be measured considering both the asset

and the liability structure. This paper, given that liquidity risk rises when

non deposit liability increases and safe asset decreases, employs the ratio

of (non deposit liability-safe asset) to total funding as an index measuring

liquidity risk.

Since the global financial crisis, the introduction of new financial

regulation has been under discussion. In the light of this, the necessity

of analysing the relationship between financial regulation and liquidity risk

has grown. This paper mentions factors affecting liquidity risk and analyses

the relationship between financial regulation and liquidity risk by setting

up a model and conducting simulation.

The trend of the liquidity risk index can be identified using the ratio

of (non deposit liability-safe asset) to total funding. The liquidity risk of

the commercial banks in Korea had risen from 2000 to the time before the

financial crisis, and it had risen especially rapidly during the period from

2007 to the third quarter of 2008. Meanwhile, the movement of the ratio

of loan to deposit and the ratio of non deposit liability to total fund is

similar to that of the liquidity risk index.

The result of analyzing correlation shows that banks with higher liq-

uidity risk index before the financial crisis tend to have received greater

financial support from the government and the central bank after the fi-

nancial crisis, which implies that the liquidity risk index can be useful in

measuring bank's exposure to liquidity risk.

* Microeconomic Studies Team, Institute for Monetary and Economic Research Bank of Korea

(Tel : 82-2-759-5418, E-mail : [email protected])

48 24 4 2010

The results obtained from setting up a model and conducting simu-

lation are as follows : rise of the safe debt funding cost, decrease in the

risky debt funding cost, decrease in the safe asset return and rise of the

risky asset return contribute to increase in liquidity risk through expansion

of the risky debt and reduction of the safe asset. As the expectation for

the financial market being stable grows, banks hold the safe asset less and

the risky asset more, which increases liquidity risk. When banks become

more risk-averse, banks hold the safe asset more, which leads to decrease

in liquidity risk.

Simulation results show that strengthening BIS capital ratio regu-

lation can bring about decrease in the ratio of (risky debt-safe asset) to

total funding through reduction in the risky asset and expansion of the

safe asset. Raising required core capital ratio restrains banks risk taking

by increasing stockholders responsibility for bank losses, and consequently,

decrease liquidity risk. Strengthening leverage ratio regulation may be a

factor in liquidity risk increase as it leads banks to reduce mostly the safe

asset that has lower return than the risky asset. Imposing bank tax on

non deposit liability can reduce the amount of non deposit and decrease

liquidity risk, while imposing tax on bank profit does not have a significant

effect. And, imposing levy on bank profit can increase liquidity risk as

banks expect more support when in trouble and bank moral hazard problem

becomes more severe. Meanwhile, imposing levy on non deposit liability

does have little effect on liquidity risk. Introducing the loan to deposit

ratio regulation can be a factor which decreases liquidity risk. It is expected

that most regulations currently under discussion can restrain banks' risk

taking activity and decrease liquidity risk, contributing to macro economic

stability. However, there is a possibility that some of them can increase

liquidity risk.

Key words : Liquidity Risk, Financial Regulation, Financial Stability

JEL Classification : E61, G21, G28

49


*

**

, , /

, KOSPI, /

. , KOSPI, /

, , /

,

.

: , , VAR , GARCH

JEL : F3, G1

.

1990 , ,

. 1990 3

0.4% 1997 11

2010 04 14; 2010 07 19; 2010 09 30

* 2008 . 2010

.


50 24 4 2010

10% . 1997

1997 12

.

4%

.

.

1990

.

1992 10% 1998

.

.

/

/

.

/ / .

.

.

.

.

, .

51

.

.

/,

.

.

. block exogeneous

Lastrapes(2005, 2006) VAR

. Rigobon and Sack(2003) GARCH

.

, KOSPI, /

,

, /

,

.

.

. block exogenous

VAR (Lastrapes, 2005, 2006) Rigobon and Sack

(2003) GARCH block exogenous

. 1999 , /,

, , KOSPI, / .

.

.

/,

.

.

52 24 4 2010

.

.

Rigobon and Sack(2003)

GARCH

.

. Flemming, Kirby, and Ostdiek(1998) , ,

GMM . (2002)

, ,

. (2003)

.

Bollerslev(1990)

GARCH EU

(, 2000 ). Longin and Solnik(1995), Tse and Tsui(2002), Engle(2002)

. (2001) Engle(2002) /

/ .

Engle, Ito, and Lin(1990) GARCH

Harvey, Ruiz, and

Shephard(1994) (stochastic)

.1)

, ,

.

1) (2001), (2003)

(2004), (2006)

. (2002) (2010)

.

53

.

1.

( ).

(1)

(1) , , /

3 1 , KOSPI, / 3 1

. ,

, 3 3

3 1 . (1) .

(2)

(3)

(3) (Hamilton, 1994 ).

(4)

(5)

(6)

(7)

(2) (3) OLS .

VAR (4) (7)

.

54 24 4 2010

(A5) (A4) .

(8)

, (9)

(8)

. (A5)

.

(10)

, (11)

2.

(B ).

(12)

vech() = +

+

(13)

(13) vech() (12) 6 6

1 21 1 . , , , , , , , /, , KOSPI, / .

55

(13)

1 . (8)

.

(14)

,

,

,

,

(14) 21 6

, , 4, 5, 6 0 .

(13)

GARCH .

56 24 4 2010

vech() = +

+

(15)

(15) 21 1, 21 6

15 3 0 .

.

, (DJ), /(/$),

, KOSPI, / .

(3) (), CD(91), CP(91

), (3) .

,

(USTB), (DJ), /(/$), (CBR), KOSPI,

/(/$) 6 .2) 1999 1

4 2009 4 23 2539. /

FRB , /

KOSPI Thompson Reuters Datastream Database .

2 .3)

2) / / 2000

(2009) /

/ /

/ . 3) 1 GARCH

.

57

.

1.

4 ADF (Dickey and Fuller, 1979)

PP (Phillips and Perron, 1988) . PP

Newey and West(1987) .

6

. 6

1% .4)

Unit Root Tests(Lag = 4)

In the Table, USTB, DJ, /$, CBR, KOSPI, and /$ imply the U.S. Treasury bill(3-month), Dow Jones

index, yen/dollar exchange rates, corporate bond(3-year), Korea composite stock price index, and

won/dollar exchange rates, respectively. ** denotes significant at the 1% level.

Statistics

Variables

ADF PP

Trend Excluded Trend Included Trend Excluded Trend Included

Level

USTB -0.178 -0.529 -0.173 -0.514

DJ -1.511 -1.229 -1.426 -1.163

/$ -1.501 -1.932 -1.360 -1.745

CBR -1.223 -1.496 -1.104 -1.349

KOSPI -1.190 -1.857 -1.114 -1.729

/$ -0.936 -0.411 -0.848 -0.372

Difference

USTB -21.255** -21.271** -21.593** -21.613**

DJ -20.494** -20.523** -20.865** -20.900**

/$ -19.591** -19.605** -19.965** -19.981**

CBR -17.176** -17.172** -17.068** -17.062**

KOSPI -19.547** -19.542** -20.737** -20.733**

/$ -18.735** -18.760** -19.938** -20.027**

4) .

58 24 4 2010

Johansen(1988)

. , , /

3 VAR Schwarz 9

9 . 2

1 .

(H0 : r = 0) 10%

. 5%

. VAR .

Johansen Cointegration Tests

In the Table, H0 : r = 0 implies the null hypothesis that a cointegrating vector dose not exist.

H0 LagTrend

Included max

Critical Value(95%)

TraceCritical Value

(95%)

r = 0 9 10.493 21.144 15.497 31.618

28.234 24.482 38.397 39.098

2.

(%) .

(3, USTB) -0.17210-2%

10% .

.

(DJ) -0.006% .

2007 11

. /(/$) -0.005%

. / 2000

. (CBR) -0.089 10-2%

. . KOSPI

59

/(/$) 0 .

KOSPI

/ 2000 /

. / KOSPI

.

Summary Statistics of Percentage Changes

In the Table, USTB, DJ, /$, CBR, KOSPI, and /$ imply the U.S. Treasury bill(3-month), Dow Jones


won/dollar exchange rates in order. + denotes significant at the 10% level. Q(10) and Q2(10) imply

Ljung-Box statistics for 10th order correlation in changes and squared changes, respectively. Numbers

in parentheses and brackets present standard errors and the p-values of the asymptotic chi-squared

statistics, respectively.

USTB DJ /$ CBR KOSPI /$

Mean-0.17210-2

(0.10110-2)+-0.006(0.018)

-0.005(0.010)

-0.08910-2

(0.10410-2)0.033

(0.028)0.006

(0.010)

StandardDeviation

0.051 0.902 0.480 0.052 1.426 0.514

Skewness -1.837 -0.329 -0.194 0.548 -0.466 -0.688

Kurtosis 38.205 8.241 5.891 10.957 5.957 38.581

Maximum 0.426 5.555 3.031 0.395 6.781 5.561

Minimum -0.714 -5.459 -3.127 -0.360 -9.393 -7.070

Q(10)685.305[0.000]

498.948[0.000]

599.635[0.000]

996.730[0.000]

647.072[0.000]

812.720[0.000]

Q2(10)1702.788

[0.000]3037.483

[0.000]545.282[0.000]

987.295[0.000]

1113.624[0.000]

2289.770[0.000]

, ,

. , , , , , .

, , /

60 24 4 2010

Level of USTB Difference of USTB

'99.01.04 '01.04.05 '05.01.03 '07.11.01 09.04.23 '99.01.04 '01.04.05 '05.01.03 07.11.01 09.04.23

Level of DJ Index Difference of DJ Index

'99.01.04 '01.04.05 '05.01.03 '07.11.01 09.04.23 '99.01.04 '01.04.05 '05.01.03 07.11.01 09.04.23

Level of Yen/Dollar Difference of Yen/Dollar

'99.01.04 '01.04.05 '05.01.03 '07.11.01 09.04.23 '99.01.04 '01.04.05 '05.01.03 07.11.01 09.04.23

61

Level of Corporate Bond Difference of Corporate Bond

'99.01.04 '01.04.05 '05.01.03 '07.11.01 09.04.23 '99.01.04 '01.04.05 '05.01.03 07.11.01 09.04.23

Level of KOSPI Difference of KOSPI

'99.01.04 '01.04.05 '05.01.03 '07.11.01 09.04.23 '99.01.04 '01.04.05 '05.01.03 07.11.01 09.04.23

Level of Won/Dollar Difference of Won/Dollar

'99.01.04 '01.04.05 '05.01.03 '07.11.01 09.04.23 '99.01.04 '01.04.05 '05.01.03 07.11.01 09.04.23

62 24 4 2010

. 2

1 .

. 3

/ 38.205 38.581

. KOSPI, /,

.

.

Q(10) Q2(10) 10

Ljung-Box (Ljung and Box, 1978) . 2

1%

.

.

GARCH .

.

1.

(2) (3) . SIC

(p) 9 .

/

(2) (3)

. 1999 1 2008 8 0,

1 .5) 100

bp(basis point) .

5) 2007 11 2009 4

.

63

-0.065(0.076)

0.005(0.014)

-0.001(0.007)

-0.021(0.071)

0.014(0.019)

-0.002(0.007)

-0.402(0.306)

-0.153(0.056)**

-0.046(0.029)

-0.385(0.291)

0.090(0.079)

0.051(0.030)+

0.000 0.000 0.0000.000

(0.019)0.016

(0.005)**0.000

(0.002)

0.000 0.000 0.0000.021

(0.106)0.247

(0.029)**-0.056

(0.011)**

0.000 0.000 0.0000.436

(0.200)*0.172

(0.054)**0.074

(0.020)**

0.908

(0.020)**-0.003(0.004)

0.004(0.002)*

0.025(0.026)

-0.021(0.007)**

0.001(0.003)

0.037

(0.111)0.834

(0.020)**0.017

(0.011)0.112

(0.138)0.292

(0.038)**-0.085

(0.014)**

0.286

(0.211)0.077

(0.039)*0.860

(0.020)**0.140

(0.263)-0.111(0.072)

0.021(0.027)

-0.836

(0.027)**-0.007(0.005)

-0.005(0.003)*

-0.085(0.030)**

0.020(0.008)*

0.004(0.003)

-0.082(0.142)

-0.812(0.026)**

-0.034(0.014)*

-0.115(0.162)

-0.021(0.004)**

0.048(0.016)*

0.122

(0.275)-0.105

(0.050)*-0.773

(0.026)**-0.293(0.301)

0.152(0.082)+

-0.059(0.031)+

0.609

(0.031)**0.004

(0.006)0.000

(0.003)0.083

(0.031)**-0.019

(0.009)*-0.006(0.003)+

0.292

(0.164)+0.748

(0.030)**0.020

(0.016)0.361

(0.177)*0.288

(0.048)**-0.029(0.018)

0.028

(0.314)0.067

(0.057)0.672

(0.030)**0.062

(0.324)-0.168(0.088)+

0.058(0.033)+

-0.520

(0.032)**-0.001(0.006)

0.003(0.003)

-0.066(0.032)*

0.016(0.009)+

0.002(0.003)

0.008

(0.176)-0.643

(0.032)**-0.025(0.017)

-0.090(0.185)

-0.194(0.050)**

0.024(0.019)

0.346

(0.334)-0.100(0.061)

-0.584(0.032)**

-0.389(0.336)

0.112(0.092)

0.049(0.034)

0.461

(0.032)**0.004

(0.006)-0.008

(0.003)*0.063

(0.032)*-0.029

(0.009)**0.001

(0.003)

Estimation Results for the VAR(9) Model with Exogenous Variables

In the Table, TB, DJ, /$, CBR, KOSPI, and /$ imply the U.S. Treasury bill(3-month), Dow Jones


won/dollar exchange rates in order. +, *, and ** denote significant at the 10%, 5%, 1% level, respectively.

64 24 4 2010

0.119

(0.181)0.519

(0.033)**-0.007(0.017)

0.123(0.185)

0.273(0.051)**

-0.042(0.019)**

-0.408(0.341)

0.015(0.062)

0.479(0.033)**

-0.022(0.335)

-0.103(0.091)

-0.003(0.034)

-0.454

(0.032)**-0.015(0.006)

0.004(0.003)

-0.092(0.031)**

0.021(0.009)*

-0.001(0.003)

-0.204(0.176)

-0.443(0.032)**

-0.017(0.017)

-0.109(0.178)

-0.150(0.049)**

0.025(0.018)

0.329

(0.333)-0.031(0.061)

-0.349(0.032)**

-0.393(0.322)

0.124(0.088)

0.020(0.033)

0.389

(0.031)**0.018

(0.006)**-0.003(0.003)

0.051(0.030)+

-0.028(0.008)**

-0.003(0.003)

0.052

(0.163)0.335

(0.030)**-0.002(0.016)

0.162(0.165)

0.143(0.045)**

-0.025(0.017)

-0.366(0.311)

0.045(0.057)

0.278(0.030)**

0.641(0.300)*

-0.092(0.082)

0.010(0.030)

-0.248

(0.027)**-0.008(0.005)+

0.002(0.003)

-0.016(0.026)

0.020(0.007)**

-0.004(0.003)

-0.007(0.142)

-0.227(0.026)**

0.006(0.014)

-0.124(0.142)

-0.029(0.039)

0.006(0.014)

0.434

(0.273)-0.035(0.050)

-0.178(0.026)**

-0.213(0.261)

0.022(0.071)

0.003(0.027)

0.137

(0.020)**0.001

(0.004)-0.001(0.002)

0.012(0.019)

-0.011(0.005)*

-0.002(0.002)

0.013

(0.111)0.096

(0.020)**-0.004(0.011)

0.043(0.114)

0.050(0.031)

-0.030(0.012)**

-0.314(0.209)

0.024(0.038)

0.096(0.020)**

0.160(0.201)

0.030(0.055)

0.023(0.020)

0.000 0.000 0.0001.031

(0.020)**-0.007(0.006)

-0.002(0.002)

0.000 0.000 0.000-0.008(0.076)

0.847(0.021)**

0.006(0.008)

0.000 0.000 0.0000.031

(0.207)-0.064(0.056)

0.884(0.021)**

0.000 0.000 0.000-0.881

(0.029)**0.002

(0.008)0.003

(0.003)

0.000 0.000 0.0000.108

(0.099)-0.848

(0.027)**-0.030

(0.010)**

0.000 0.000 0.0000.010

(0.273)0.005

(0.074)-0.747

(0.028)**

0.000 0.000 0.0000.800

(0.033)**-0.007(0.009)

0.002(0.003)

65

0.000 0.000 0.000-0.018(0.117)

0.740(0.032)**

0.001(0.012)

0.000 0.000 0.0000.442

(0.309)0.118

(0.084)0.535

(0.031)**

0.000 0.000 0.000-0.653

(0.036)**-0.005(0.010)

0.000(0.004)

0.000 0.000 0.000-0.006(0.127)

-0.663(0.035)**

0.002(0.013)

0.000 0.000 0.000-0.414(0.323)

-0.047(0.088)

-0.518(0.033)**

0.000 0.000 0.0000.556

(0.037)**0.008

(0.010)-0.02

(0.004)

0.000 0.000 0.0000.166

(0.130)0.508

(0.035)**0.014

(0.013)

0.000 0.000 0.0000.973

(0.329)**0.128

(0.090)0.442

(0.033)**

0.000 0.000 0.000-0.421

(0.036)**-0.009(0.010)

0.008(0.004)*

0.000 0.000 0.000-0.075

(0.126)**-0.375

(0.034)**-0.009(0.013)

0.000 0.000 0.000-0.331(0.326)

-0.177(0.089)*

-0.298(0.033)**

0.000 0.000 0.0000.286

(0.033)**0.008

(0.009)-0.008

(0.003)*

0.000 0.000 0.000-0.015(0.115)

0.259(0.031)**

0.020(0.012)+

0.000 0.000 0.000-0.221(0.312)

0.202(0.085)*

0.211(0.032)**

0.000 0.000 0.000-0.175

(0.028)**-0.013(0.008)+

0.008(0.003)**

0.000 0.000 0.000-0.032(0.098)

-0.148(0.027)**

-0.015(0.010)

0.000 0.000 0.0000.111

(0.273)-0.097(0.074)

-0.162(0.028)**

0.000 0.000 0.0000.089

(0.020)**0.007

(0.005)-0.004

(0.002)*

0.000 0.000 0.0000.144

(0.073)*0.078

(0.020)**0.018

(0.007)*

0.000 0.000 0.0000.002

(0.203)0.138

(0.055)*0.047

(0.021)*

R2 0.479 0.438 0.448 0.554 0.571 0.542

66 24 4 2010

(2) (3) .

/

10% .

KOSPI KOSPI

.

(3, USTB), (DJ), /(/$) .

KOSPI

, , / 1% KOSPI

0.016%, 0.247%, 0.172% 1% . /

/

1% -0.056% 0.074% 1%

. KOSPI

/ . / KOSPI /

.

/ .

. , KOSPI, /

.

, CD, CP

.

.

2.

. (13)

.

~ .

GARCH

. (1)

67

.

Estimation Results of and




1.000-0.257

(0.062)**-0.084(0.123)

0.000 0.000 0.000

-0.010

(0.004)*1.000

0.204(0.055)**

0.000 0.000 0.000

-0.010

(0.002)**-0.130

(0.020)**1.000 0.000 0.000 0.000

-0.022(0.015)

-0.145(0.089)

-0.441(0.151)**

1.0000.071

(0.101)-0.657

(0.239)**

-0.018

(0.004)**-0.236

(0.025)**-0.134

(0.042)**-0.027

(0.008)**1.000

0.445(0.064)**

0.005

(0.002)**0.008

(0.008)-0.164

(0.012)**-0.003(0.002)+

0.026(0.007)**

1.000

(=

)

1.003 0.262 0.031 0.000 0.000 0.000

0.008 0.976 -0.198 0.000 0.000 0.000

0.011 0.130 0.975 0.000 0.000 0.000

0.024 0.190 0.506 1.000 -0.089 0.696

0.024 0.255 0.026 0.026 1.009 -0.432

-0.004 0.006 0.002 0.002 -0.027 1.013

=

(16)

(16) . 4

68 24 4 2010

1bp

0.022bp .6) / 1%

0.145bp 0.441bp .

10% / 1%

. .

5 1bp KOSPI

0.018% . / 1% KOSPI

0.236% 0.134% . 1% .

6 1bp /

0.005% . 1% / 0.008% .

/ 1% / 0.164% .

.

= =

(17)

(16) 1 1%

0.257bp . 0.257bp

/

1%

(17) 0.262bp .

. .

6) 4 (A1) .

1% 0.022bp .

69

.

1% .

. 1

. 1 .





i USTB DJ /$ CBR KOSPI /$

0.239

(0.038)**0.002

(0.001)**0.004

(0.001)**0.158

(0.045)**0.005

(0.002)**0.024

(0.002)**

0.002

(0.001)**0.021

(0.004)**0.004

(0.002)**0.018

(0.005)**0.000

(0.000)**0.672

(0.097)**

Estimation Results of




Ij


USTB0.715

(0.018)**0.000

(0.000)**0.002

(0.000)**0.000 0.000 0.000

DJ0.000

(0.000)0.925

(0.008)**0.001

(0.000)0.000 0.000 0.000

/$0.000

(0.000)0.000

(0.000)0.918

(0.016)**0.000 0.000 0.000

CBR0.000

(0.000)0.000

(0.000)**0.000

(0.000)**0.882

(0.014)**0.080

(0.061)0.000

(0.000)**

KOSPI0.000

(0.000)0.000

(0.000)**0.000

(0.000)**0.000

(0.000)**0.908

(0.015)**0.000

(0.000)

/$0.000

(0.000)**0.000

(0.000)**0.000

(0.000)**0.000

(0.000)**0.000

(0.000)**0.000

(0.000)**

. 1

70 24 4 2010

. , , /

. /

. KOSPI . /

.

. / /

.

.

.

Estimation Results of




i j


USTB0.348

(0.026)**0.001

(0.000)**0.002

(0.001)*0.000 0.000 0.000

DJ0.000

(0.000)+0.066

(0.008)**0.004

(0.001)**0.000 0.000 0.000

/$0.000

(0.000)0.003

(0.001)*0.035

(0.007)**0.000 0.000 0.000

CBR0.001

(0.000)**0.069

(0.071)0.000

(0.000)**0.101

(0.013)**0.002

(0.000)**0.000

(0.000)**

KOSPI0.000

(0.000)0.017

(0.005)**0.001

(0.000)**0.001

(0.000)**0.068

(0.011)**0.000

(0.000)

/$0.000

(0.000)0.024

(0.004)**0.053

(0.010)**0.000

(0.000)**0.000

(0.000)**0.315

(0.031)**

1

.

1 .

71


0.715 0.015 0.002 0.000 0.000 0.000

0.006 0.248 -0.016 0.000 0.000 0.000

0.008 0.032 0.028 0.000 0.000 0.000

0.017 0.047 0.013 0.000 0.000 0.000

0.017 0.064 -0.002 0.000 0.000 0.000

-0.003 0.002 0.005 0.000 0.000 0.000

0.000 0.925 0.000 0.000 0.000 0.000

0.000 0.126 -0.192 0.000 0.000 0.000

0.000 0.182 -0.105 0.000 0.000 0.000

0.000 0.242 -0.015 0.000 0.000 0.000

0.000 0.006 -0.031 0.000 0.000 0.000

0.000 0.000 0.918 0.000 0.000 0.000

0.000 0.016 0.477 0.000 0.000 0.000

0.000 0.032 0.023 0.000 0.000 0.000

0.000 -0.002 0.153 0.000 0.000 0.000

0.000 -0.004 0.022 0.883 0.079 -0.431

0.000 0.052 0.004 0.023 -0.078 0.004

0.000 -0.001 0.078 0.003 0.002 -0.002

0.000 0.001 0.005 0.000 0.908 -0.165

0.000 0.003 0.004 0.000 -0.024 0.004

0.000 0.000 0.025 0.000 0.001 0.000

0.348 -0.019 0.003 0.000 0.000 0.000

0.003 0.018 0.000 0.000 0.000 0.000

0.004 0.002 0.001 0.000 0.000 0.000

0.008 0.003 0.001 0.000 0.000 0.000

0.008 0.004 0.000 0.000 0.000 0.000

-0.001 0.000 0.000 0.000 0.000 0.000

0.000 0.066 0.003 0.000 0.000 0.000

0.000 0.008 -0.007 0.000 0.000 0.000

0.000 0.013 -0.003 0.000 0.000 0.000

0.000 0.017 0.000 0.000 0.000 0.000

0.000 0.000 -0.001 0.000 0.000 0.000

0.000 0.004 0.035 0.000 0.000 0.000

0.000 0.003 0.018 0.000 0.000 0.000

0.000 0.002 0.001 0.000 0.000 0.000

0.000 0.000 0.006 0.000 0.000 0.000

0.001 0.084 0.003 0.101 0.002 0.101

0.000 -0.003 -0.014 0.003 -0.006 -0.093

0.000 0.018 0.035 0.000 0.000 0.217

0.000 0.023 0.009 0.001 0.068 0.045

0.000 -0.011 -0.020 0.000 -0.002 -0.134

0.000 0.025 0.048 0.000 0.000 0.315

72 24 4 2010

. .

(13) GARCH , , ,

GARCH

. GARCH .

,

.

1998

2000 30%

50%

.

/ /

.

.

.

.

~ (USTB), (DJ), /

(/$), (CBR), KOSPI, /(/$)

. 1

.

. KOSPI

/ KOSPI

73

Impulse Response of Volatility of Bond market

: USTB : DJ Index : Yen/Dollar

Impulse Response of Volatility of Stock Market


Impulse Response of Volatility of Foreign Exchange Market


74 24 4 2010

Impulse Response of Volatility of Bond market

: Federal Funds Rate : DJ Index : Yen/Dollar





75

Impulse Response of Volatility of Money market






76 24 4 2010

. . 1 /

. /, /

. / 1

( ) 0 .

~ (USTB)

.

1

.

.

~ (USTB)

.

1 .

.

/ .

.7)

.

Lastrapes(2005, 2006) Rigobon and Sack(2003)

, , / block exogenous

, KOSPI, /

.

, KOSPI, /

7) CD CP

.

77

,

, /

,

.

. /

/

.

.

.

.

.

.

/ .

.

.

,

.

78 24 4 2010

1. , ,

, 7 3, 2001, 23-45. 2. , , , 9

1, 2003, 1-86.

3. , , , 11 2, 2006, 167-201.

4. , :

, , 16 1, 2010, 161-191. 5. , - - , , 6

3, 2000, 45-70.

6. , : / / , , 49 4, 2001, 311-338.

7. , , , , , 8 1, 2002, 191-212.

8. , , , 51 3, 2003, 53-96.

9. , / , , 15 2, 2009, 23-53.10. , : ,

, , , 8 1, 2002, 136-191.11. ,

, , 21 2, 2006, 125-151.12. Bollerslev, T., Modelling the Coherence in Short Run Nominal Exchange Rates : A

Multivariate Generalized ARCH Model, Review of Economics and Statistics 72, 1990,

498-505.

13. Dickey, D. A. and W. A. Fuller, Distribution of the Estimation for Autoregressive Time

Series with a Unit Root, Journal of the American Statistical Association 74, 1979,

427-431.

14. Engle, R. F., Dynamic Conditional Correlation : A Simple Class of Multivariate

Generalized Autoregressive Conditional Heteroskedasticity Models, Journal of Business

and Economic Statistics 20, 2002, 339-350.

79

15. Engle, R. F., T. Ito, and W. Lin, Meteor Showers or Heat Waves? Heteroskedastic

Intra-Daily Volatility in the Foreign Exchange Market, Econometrica 58, 1990, 525-542.

16. Fleming, J., C. Kirby, and B. Ostdiek, Information and Volatility Linkages in the Stock,

Bond, and Money Markets, Journal of Financial Economics 49, 1998, 111-137.

17. Hamilton, J. D., Time Series Analysis, Princeton, Princeton University Press, 1994.

18. Harvey, A., E. Ruiz, and N. Shephard, Multivariate Stochastic Variance Models, Review

of Economic Studies 61, 1994, 247-264.

19. Johansen, S., Statistical Analysis of Cointegration Vectors, Journal of Economic

Dynamics and Control 12, 1988, 231-254.

20. Lastrapes, W. D., Estimating and Identifying Vector Autoregressions under Diagonality

and Block Exogeneity Restrictions, Economics Letters 87, 2005, 75-81.

21. Lastrapes, W. D., Inflation and the Distribution of Relative Prices : The Role of

Productivity and Money Supply Shocks, Journal of Money, Credit, and Banking 38,

2006, 2159-2198.

22. Ljung, L. M. and G. E. P. Box, On a Measure of Lack of Fit in Time Series Models,

Biometrica 65, 1978, 297-303.

23. Longin, F. M. and B. Solnik, Is the Correlation in International Equity Returns

Constant : 1960~1990?, Journal of International Money and Finance 14, 1995, 3-26.

24. Newey, W. K. and K. D. West, A Simple Positive Semi-Definite, Heteroskedasticity and

Autocorrelation Consistent Covariance Matrix, Econometrica 55, 1987, 703-708.

25. Phillips, P. C. B. and P. Perron, Testing for a Unit Root in Time Series Regression,

Biometrika 75, 1988, 335-346.

26. Rigobon, R. and B. Sack, Spillovers Across U.S. Financial Markets, Working Paper,

Sloan School of Management, MIT and NBER, 2003.

27. Tse, Y. K. and A. K. C. Tsui, A Multivariate Generalized Autoregressive Conditional

Heteroskedasticity Model with Time-Varying Correlations, Journal of Business and

Economic Statistics 20, 2002, 351-362.

80 24 4 2010

Hamilton(1994) Lastrapes(2005, 2006) 3

VAR .

(A1)

(A2)

(A1) (structural model) (A2) (A1)

(reduced form model) . . , , / 3 1

, KOSPI, / 3 1 .

6 6 . ,

, (A1)

.

(A3)

(A1) (A2) MA(moving average)

.

(A4)

(A5)

81

(A1), (A2), (A4), (A5)

.

(A6)

( ) 33 .

.

(A7)

. block exogenous

0 . (A2) (1)

.

82 24 4 2010

VAR VAR

. Lastrapes(2005, 2006)

(A3) (8) lower

triangular .

. lower triangular

.

.

VAR

.

Rigobon and Sack(2003)

GARCH .

(B1)

(B1) . , , / 3 1

, KOSPI, / 3 1 .

6 1 . 6 6 (A6)

.

(B2)

(B3)

83

. block exogenous Rigobon and Sack(2003)

33 0 .

GARCH

- (13)

GARCH .

84 24 4 2010

< Abstract >8)

The Impact of International Financial Shocks on the Volatility of Domestic

Financial Markets

Keun Yeong Lee*

This study analyzes the impact of international financial shocks on

the volatility of domestic financial markets. It simultaneously investigates

casual relations between domestic and foreign financial markets such as

equity, foreign exchange, and money or bond markets. It combines and ex-

tends the models of Lastrapes (2005, 2006) and Rigobon and Sack (2003).

It is assumed that foreign variables such as U.S. interest rates, Dow Jones

Index, and yen/dollar exchange rates are block exogenous, following Last-

rapes (2005, 2006).

Lastrapes (2005, 2006) used Cholesly factorization to recognize pa-

rameters in structural VAR models. But this method cannot reflect con-

temporaneous relations in financial markets well, because it unilaterally

restricts causal relations between financial variables. Therefore, the paper

estimates contemporaneous parameters in structural VAR models under the

assumption that conditional variance-covariance matrix is time varying

like in Rigobon and Sack (2003). In addition, it is also assumed that foreign

variables in conditional variance-covariance matrix are block exogenous

in order to reduce the number of excessive parameters which should be

estimated.

The whole sample period is from January 4, 1999 to April 21, 2009

and the sample size is 2539. Two days average return data are considered

to avoid time lag between Korea and U.S.

The empirical results show that news shocks in domestic stock, for-

eign exchange, and money or bond markets cannot significantly influence

volatility of the other domestic financial variables, when foreign financial

variables are considered together. On the other hand, news shocks to for-

eign variables such as U.S. interest rates, Dow Jones Index, and yen/dollar

exchange rates have relatively large impact on volatility of domestic finan-

cial variables. Particularly, shocks to Dow Jones Index and yen/dollar ex-

* School of Economics, Sungkyunkwan University(Tel : 82-2-760-0614, E-mail : [email protected])

85

change rates have stronger impact on volatility of domestic financial mar-

kets than shocks to U.S. Treasury bill and federal funds rates.

Volatility of domestic money and bond markets is powerfully influ-

enced by shocks to U.S. federal funds rates rather than Treasury bill rates.

Shocks to federal funds rates also have much stronger effect on volatility

of call rates than volatility of corporate bond yield rates. Volatility of cor-

porate bond yield rates is more affected by shocks to Dow Jones Index than

shocks to yen/dollar exchange rates. On the other hand, volatility of call

rates is more strongly influenced by shocks to yen/dollar exchange rates

than shocks to Dow Jones Index. The empirical results suggest that the

domestic monetary policy is closely associated with the foreign exchange

policy because a balance of current accounts is very important in a small

open economy.

Key words : Foreign News Shocks, Volatility, Structural VAR and GARCH

Models

JEL Classification : F3, G1

87


******

.

, LTV(MLTV) , DTI

. . 2004 2007

.

0.78%, 1.40%, 1.10% .

, MLTV

.

20.06%, 20.55%, 17.73% .

Kaplan-Meier product limit (CDR)

(CPR)

CDR 50~100% SDA, 50~150% SDA, CPR 150~

250% PSA, 100~200% PSA .

: , , , ,

JEL : G10, G20, G21

2010 05 06; 2010 06 17; 2010 10 01

* (Tel : 02-2014-8157, E-mail : [email protected])


*** , (Tel : 02-820-5793, E-mail : [email protected])

88 24 4 2010

.

2008

.

, 2004

2003 153 2009 264

64.5% .

ALM

.1)

MBS , 2000

MBS , 2009 12 24 9,298 .

MBS

.

(conditional de-

fault rate, CDR) (conditional prepayment rate, CPR)

.

.

.

.

.

.

.

1) .

89

.

.

.

(Asay, Guillaume, and Mattu, 1987; Richard

and Roll, 1989; Schwartz and Torous, 1989; Chinloy, 1991; Schorin, 1992)

. Deng,

Quigley, and Van Order(2000) Deng, Zheng and Ling(2005) ,

Deng and Liu(2009) , (2008) .

Foot, Gerardi, Goette,

and Willen(2008) , Sanders(2008), Daglish(2009) .

Asay, Guillaume, and Mattu(1987)

.2)

.

.

Richard and Roll(1989) , ,

, (burn-out effect)

.3)

Schwartz and Torous(1989)

2) Brazil(1988), Carron and Hogan(1988), Davidson, Herskovits,

and Drunen(1988), Schwartz and Torous(1989), Chinloy(1991), Schorin(1992) .3)

.

90 24 4 2010

.

Chinloy(1991) , , (age)

GNMA MBS

.

.

.

Schorin(1992) GNMA 30 MBS 1 (pooling)

, , , ,

, 3 , , ,

.

,

.

(2008)

MBS

.

, Schorin(1992)

.

Daglish(2009) (real option)

.

.

Deng, Quigley, and Van Order(2000)

. LTV ,

.

Deng, Zheng and Ling(2005)

. ,

, . ,

91

, .

.

. .

Deng and Liu(2009)

,

.

. LTV ,

. ,

. ,

.

.

(2008)

. (+)

. LTV

(-)

.

. 1

.

6 (+) ( 10%)

.

.

1.

Kaplan-Meier product limit(Kaplan and Meier, 1958) CDR

CPR .

92 24 4 2010

Cox PHM(proportional hazard model)

, , Cox PHM

.

.

(1) Kaplan-Meier product limit

(data generating process) .

.4)

(distribution family)

.

Kaplan-Meier product limit

.

Kaplan-Meier product limit T

.

(1)

CDR , ,

1, 0, t .

(2) Cox PHM

4)

(Kalbfleisch and Prentice, 1973; Kalbfleisch, 1974; Cox 1972, 1975 ).

, (Lancaster,

1979) , , ,

(Engle and Russell, 1998; Zhang et al., 2001).

93

. Cox(1972, 1975)

(proportional hazard model : PHM) , PHM

. Vandell et al.(1993), Deng et al.(2000),

Deng et al.(1996) Cox PHM

.

Cox(1972) PHM

(partial likelihood)

. PHM

(full likelihood)

. .

PHM (2) .

(2)

,

PHM (2)

. (baseline hazard) Cox(1972)

.

PHM .

Cox PHM

Relative hazard Cox PHM

t hazard proportional hazard .5)

Cox PHM

Cox PHM. ,

. ,

5) Relative hazard cohort .

94 24 4 2010

,

, .

.6)

(3) .

,

.

(4) .

(5)

. Cox PHM .

(3)

(4)

(5)

,

,

,

.

6)

.

95

.

.

.

. LTV

.

. , DTI , ,

, .

LTV, , DTI, .

.

.

.

(LTV)

LTV MLTV

. DTI

.

. LTV

. LTV LTV

LTV .

12 36

.

, , .

96 24 4 2010

variables description

Cumulative rate of

house price change

2.

2004 1 2007 12

145,782 . 21,069, 12,503

. 20, LTV 70%

. 1 2%, 3

1.5%, 5 1%, 5 .

BIS Basel 90

.7)

, , LTV(mark-to-market LTV :

MLTV), t , , , DTI(debt-to-in-

come), , ,

. .

, t

, t 1,816,543 .

.

(+) . LTV 60.3%,

57.8%, 60.9% 74 , 111

, 66 .

Variable definition and description

7) .

, 60 , 90

. BIS Basel (395) 90

. Medema. Koning and Lensink(2009), Qi and

Yang(2009) .

97

variables description

: house price change at time s

Interest rate spread between the contract rate and the market

rate at t period

contract rate-market rate

MLTV (mark-to-

market LTV)

: initial loan amount, : initial house value M : maturity, : contract rate, : house price change at time s.

Loan balance

at t period

,

: initial loan amount, M : maturity, : contract rate

Credit credit rate (1-5)

Tenant dummy

DTI initial DTI (1-7)

Borrowers age

dummy

Male dummy

Occupation dummy

Regional dummy

(Seoul, Pusan)

Prepayment penalty

dummy

13~15 months dummy

37~39 months dummy

98 24 4 2010

variable N Mean SD Min Max

A : Overall market

Cumulative rate ofhouse price change(%)

1,816,543 0.276 0.744 -1.86 3.828

Interest rate spreadbetween contract rate and market rate at t period(%)

1,816,543 0.444 0.552 -1.80 1.68

Mark-to-marketloan-to-value ratio(%)

1,816,543 60.3 11.3 1.10 70.0

Loan balanceat t period(thousand won)

1,816,543 69,024 42,919 913 300,000

Loan amount(thousand won) 145,782 74,712 46,673 1,000 300,000

B : Seoul


537,658 0.288 0.24 -0.264 0.84


537,658 0.456 0.564 -1.80 1.68


537,658 57.8 13.2 1.40 70.0


537,658 104,103 53,014 3.654 300,000

Loan amount(thousand won) 21,069 111,499 56,789 4,000 300,000

C. Pusan


304,428 1.38 1.368 -0.552 3.828


304,428 0.456 0.54 -1.68 1.56


304,428 60.9 11.0 4.20 70.0


304,428 62,289 32,853 2,779 300,000

Loan amount(thousand won) 12,503 66.075 34,886 3,000 300,000

Descriptive statistics

99

.

1.

1,142 0.78%, 296

1.4% , 138 1.1%

. 0.3%

1% .

6.75 .

29,246 20.06%, 4,329

20.55% . 2,217

17.73% . 2.82% ,

.

1.99 .

Default and prepayment in Seoul and Pusan

Overallmarket

Seoul (a) Pusan (b)difference

(a-b)t statistics

p value

A : Default

No. of loan 145,782 21,069 12,503

Remaining loans 144,640 20,773 12,365

No. of default 1,142 296 138

Default rate(%) 0.78 1.40 1.10 0.30 2.36 0.0096

Average loan life atdefault(month)

12.96 19.72 -6.75 7.43 0.0001

B : Prepayment

No. of loan 145,782 21,069 12,503

Remaining loans 116,536 16,740 10,286

No. of prepayment 29,246 4,329 2,217

Prepayment rate(%) 20.06 20.55 17.73 2.82 6.29 0.0001

Average loan life atprepayment(month)

20.74 18.74 1.99 -7.45 0.0001

100 24 4 2010

2. Kaplan-Meier product limit

(1)


. (hazard rate) (monthly conditional

default rate) (conditional default rate;

CDR) . (Bond Market Association)

CDR SDA(Standard Default Assump-

tion) . 100% SDA . 1 0.02%

0.02% 30 0.6% 60

. 61 120 0.0095% 120

0.03% . SDA (6) .

(6)

The 100% SDA(Standard Default Assumption) benchmark

Source : Lakhbir Hayre, Salomon Smith Barney Guide to Mortgage-Backed and Asset-Backed Securities,

John Wiley and Sons, Inc., 2001, p. 168.

101

CDR SDA .

SDA SDA

.

(Aging) .8)

SDA . SDA

Pool CDR

Kaplan-Meier product limit CDR

SDA .

50% SDA 100% SDA .

.

. .

50% SDA 100% SDA , 50% SDA 150% SDA

.

.

2004

.

2004

,

.9) 2004

, , .10)

8)

. 9)

, .

.10) 2003 10 29 , LTV 40%

.

102 24 4 2010

The Relationship Between the Default Rate and the Loan Age by Region

A : Overall market

B : Seoul

C : Pusan

SDA : Standard Default Assumption(Fabozzi, 1996).

Mortgage Age(Months)



103

(2)

PSA(Public Securities Association)

Standard Prepayment Benchmark

. PSA (7) .

(7)

t . PSA

.


(CPR)

.

. 12 36

.

, ,

. 30 ,

. PSA 36

30

.

150% PSA 250% PSA

, 100% PSA 200% PSA

.

. , ,

PSA .

104 24 4 2010

The Relationship Between the Prepayment Rate and the Loan Age by Region

A : Overall market

B : Seoul

C : Pusan

PSA : Public Securities Association(Fabozzi, 1996).




105

3. Cox PHM

(1)

Cox PHM .

, , MLTV, , ,

, DTI, , ,

. ,

(-) (+)

. MLTV, DTI (+)

, . Deng and Liu(2009)

LTV, (+)

.

(2008) LTV (+)

MLTV (-)

. LTV MLTV (+)

.

(-)

(+)

.

.

, .

(-)

(-) .

.

.

106 24 4 2010

. (+). ,

.

.

.

Apartment prices in Seoul, Pusan and Overall Market

80

100

120

140

160

180

200

220

1999

:01

2000

:01

2001

:01

2002

:01

2003

:01

2004

:01

2005

:01

2006

:01

2007

:01

2008

:01

2009

:01

Apartment prices in KoreaApartment prices in PusanApartment prices in Seoul

Note) All time series data are set to 100 in 1999 : 1(Kookmin Bank).

(2)

.

(+)

. , , (-)

. , ,

.

108 24 4 2010

110 24 4 2010

111

d13, d37 3

. (2008)

(-)

.

(+) . MLTV

. (-)

. (+)

MLTV (+)

.

(robustness test) Cox PHM ,

, Cox PHM

.

. LTV

LTV .11)

.

SDA/PSA

. .

. 0.78%,

1.40%, 1.10% . 12

11) LTV LTV LTV . : LTV = LTV

if LTV < 60%, LTV = 60% if LTV > = 60%.

112 24 4 2010

. , 2004

.

.

,

, MLTV, ,

DTI, , , .

DTI ,

.

, MLTV (+) .

20.06%, 20.55%, 17.73% .

.

Kaplan-Meier product limit CDR 50~100% SDA,

50~150% SDA , CPR 150~250% PSA, 100~

200% PSA .

.

(PSA SDA) CDR CPR

. CDR

CPR SDA/PSA

pool

.

.

113

ALM .

.

114 24 4 2010

1. , MBS

: OAS , 08-04, 2008. 2. , , ,

16 3, 2008, 5-26.

3. , , , 13, 2003, 67-83. 4. Asay, M., F. H., Guillaume and R. K., Mattu, Duration and convexity of mortgage

backed securities : some hedging implications from a prepayment linked present value

model, in Mortgage Backed Securities, edited by Frank J. Fabozzi, Chicago : Probus

Publishing, 1987.

5. Brazil, A. J., Citicorps mortgage valuation model : option-adjusted spreads and option

based duration, Journal of Real Estate and Economics 1, 1988, 151-162.

6. Carron, A. S. and M. Hogan, The option valuation approach to mortgage pricing,

Journal of Real Estate Finance and Economics 1, 1988, 131-149.

7. Chinloy, P., The option structure of a mortgage contract, Journal of Housing Research

2, 1991, 21-38.

8. Cox, D. R., Regression models and life-tables, Journal of the Royal Statistical Society

Series 34, 1972, 187-220.

9. Cox, D. R., Partial Likelihood, Biometrica 62, 1975, 269-276.

10. Daglish, Toby, What motivates a subprime borrower to default?, Journal of Banking

and Finance 33, 2009, 681-693.

11. Davidson, A., M. Herskovits, and L. D. Van Drunen, The refinancing threshold pricing

model : an economic approach to valuing MBS, Journal of Real Estate Finance and

Economics 1, 1988, 117-130.

12. Deng, Y., J. Quigley, and R. Van Order, Mortgage terminations, heterogeneity, and the

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< Abstract >12)

An Analysis of Default and Prepayment in Korean Mortgage Markets

Doowon Bang*, Sae Woon Park**, Yun Woo Park***

The purpose of this study is to investigate the default and the prepay-

ment behaviors in the Korean mortgage ma

Documents

금융연구는 한국금융학회의 공식 ... · An Analysis of Default and Prepayment in Korean Mortgage Markets ... Model with Incomplete Financial Markets ... 한편 미국