Is the IMF's Tightening Monetary Policy Right for the Currency...

Thesis Essay 1

Is the IMF's Tightening Monetary Policy Right for

the Currency Crisis in South Korea?

Heeseok Park ∗

(Second Draft: Aug 2003)

(First Draft: June 2003)

Department of Economics

Kansas State University

Abstract

This paper is to empirically evaluate the validity of IMF’s hyper-interest rate policy that is designed to

handle the beginning stage of the Korean currency crisis in order to alleviate the instability of the foreign

exchange market. According to the counterfactual experiment, the hyper-interest rate by IMF’s austerity

monetary policy is not a proper remedy for Korea during the initial stage of this crisis. Expansionary

monetary policy is enough to boost the economy and help prevent bankruptcy in small and medium size

firms with defending the sharp devaluation of the Korean currency.

Keywords: Asian Crisis, Structural VAR, Counterfactual Experiment

JEL: C32, E32, E52

∗ I would like to thank Steve Cassou, William Blankenau, Petric Gormely, Tom Marsh, and James Ragan for their kind comments for this paper. I also specially thank Tim Giles at Kansas State University who is willing to check grammatical errors in this paper. All errors are mine. Corresponding: Department of Economics, Waters Hall 327, Kansas State University, Manhattan, KS 66502-4001, USA. The author may be contacted at [email protected] or [email protected].

i

A Table of Contents

Table 1: Major Economic Indicators before and after the Three Asian Crisis Countries

Table 2: Major Economic Indicators before and after the Korean Currency Crisis

Table 3: Trends of Amount of Exports and Exports Price Index

Table 4: Trends of Long and Short Term Foreign Debt

Table 5: Trend of Debt Ratio in the Manufacturing Sector

Table 6: Management of Monetary Policy under the IMF Program

Table 7: Exchange Rate Changes before and after the Asian Crisis

Table 8: Data for Empirical Studies

Table 9: Major Economic Events

Table 10: Monetary Transmission Mechanism

Table 11: VAR Lag Order Selection Criteria

Table 12: Estimation Result for the Ordering of Variables in Impulse Responses

Table 13: Estimation Result of a Standard VAR Model

Table 14: Empirical Result in Contemporaneous Causal Relationships for SVARact

Table 15: Estimated Result in A and B Matrices for SVARact

A Figure of Contents

Figure 1: Mechanism of IMF’s Tightening Monetary policy

Figure 2: Two Remedies to Solve for the Asian Crisis

Figure 3: Trend of Money Supply Instruments

Figure 4: Trends of Major Macroeconomic Variables before and after Five Years around

the Crisis

Figure 5: Impulse Response by the Standard VAR Model

Figure 6: Causal Diagram for the SVARact Model

Figure 7: Impulse Response by the SVARact Model

Figure 8: Comparison Impulse Responses by the VAR and SVARact Models

Figure 9: Counterfactual Experiment against IMF’s Monetary Policy

1

I. Introduction

The Asian currency crisis began with Thailand in July, 1997, when a large

amount of baht was sold by international speculative investors in the world foreign

exchange markets in February and again in May, 1997. The crisis then spread to

Indonesia in November and South Korea (henceforth, Korea) in December, 1997.

These countries requested a bailout package from the International Monetary

Fund (henceforth, IMF), which the IMF gave an approval of stand-by credits to Thailand

($4 billion) in August, to Indonesia ($10 billion) in November, and to Korea ($21 billion)

in December, 1997.

Although the Asian currency crisis is basically different from the currency crisis

of the United Kingdom in 1976 and the Latin American countries in the 1980s, the IMF

claims a conventional economic remedy such as the austerity monetary and fiscal policies,

the reform of financial sectors, and other structural measures to defend an exchange rate

without a deep understanding of the economic fundamentals in the Asian countries.

For instance, the UK’s currency crisis was caused by massive government

expenditures under budget deficits in order to encourage economic growth and

employment after the first oil shock. The Latin American countries currency crisis was

due to loose government spending on valueless infrastructure and construction projects.

Unlike the U.K. and Latin American countries, the recent Asian crisis resulted

from not only the government sector, but also the private sector and international

speculative investors. Before the crisis occurred, the three Asian countries were in

fundamentally a healthy economic condition. These countries had a relatively high GDP

growth rate, low inflation and unemployment rates, and a budget surplus except for the

deficit of current account (See Table 1 in the appendix).

Table 1: Major Economic Indicators before and after the Three Asian Crisis Countries

2

Here

Some economists, like Furman and Stiglitz (1998), Radelet and Sachs (1998),

Sachs (1997a, and 1997b), Stiglitz (1998) and others, had strongly criticized the

macroeconomic policies of the IMF’s structural adjustment programs for the handling of

the Asian crisis at its beginning stage. The macroeconomic policies focused mainly on

austere monetary and fiscal polices to recover stability of the foreign exchange market.

They argued that the IMF’s hyper-interest rates caused by a tightening monetary

policy would, when a sizable deficit in the balance of payments occurs simultaneously,

further reduce investor’s confidence. The reduction of foreign investor’s confidence

would lead to an even further weakening of the domestic currency, since the austere

monetary policy would reduce the ability of borrowers to repay loans and thereby weaken

the banking system. In the case of Korea, the austere monetary policy led to a severe

credit crunch and generated deep recessions in the Korean economy at the beginning

stage of the crisis.

In the line of this controversy, it would be a valuable research to examine the

following question: Whether does the IMF’s austere monetary policy valid for recovering

the Korean currency crisis? It is one of the most controversial issues associated with the

Asian crisis and the role of the IMF. However, it has proved to be difficult to

statistically test whether the contractionary monetary policy was or was not appropriate

for the exchange rate stabilization during the Korean currency crisis.

This doubt has led many economists to conduct a series of theoretical or empirical

works to find the possible causes of the Asian currency crisis.1 Specifically, a number of

empirical studies have been recently conducted.2

1 Some economists suggest theoretical models that try to explain the cause of the Asian crisis as follows: 1) Moral Hazard Model by Krugman (1998), 2) Bank-Run Model by Radelet and Sachs (1998), and 3) International Illiquidity Model by Chang and Velasco (1998). Krugman (1998) emphasizes the causes of currency crisis in the Asian countries on the excessive loans of financial institutions by moral hazard and the rapid rise of overvalue of assets by the excessive loans. Radelet and Sachs (1998) regard the origin of currency crisis as massive capital outflows by self-fulfilling financial panic associated with the immanent

3

The purpose of this paper is to investigate the validity of IMF’s austere monetary

policy as it pertains to the Korean currency crisis using an impulse response analysis

based on a structural VAR (SVAR) model and a counterfactual experiment.

As a preliminary analysis, I examine the basic features of a monetary transmission

mechanism in Korea using VAR and SVAR models. The aim of this preliminary study

is to compare the impulse response of SVAR model with that of the counterfactual

experiment.

To evaluate IMF’s monetary policy, I conduct a counterfactual experiment to

obtain an alternative impulse response. The alternative impulse response shuts off the

effect of shocks in a money policy variable (short-run interest rates). Using the

counterfactual impulse response, I explain the effects of an alternative monetary policy

on the economy during the beginning stage of crisis.

For this study, I chose to use a SVAR model to evaluate IMF’s monetary policy

on the following reasons: On the empirical side, most recent empirical studies in relation

of monetary policy instruments and real economic activities have adopted a VAR

framework.3 Cooley and LeRoy (1985), however, criticized the VAR methodology

because of its atheoretical identification schemes.4 For this reason, I adopted a SVAR

model that recovers structural innovations from the residuals of economic theories,

assumptions, or relationships among relevant variables.

instability of international financial market on the basis of Diamond and Dybvig (1983)’s bank-run model. Unlike Krugman (1998) and Radelet and Sachs (1998), Chang and Velasco (1998) point out the source of the currency crisis by insolvent financial institutions linked to the international illiquidity crisis. 2 See Jeon (2002), Lee (2000), Lee and Rhee (2000), and Park and Rhee (1998) for more details. 3 The use of VAR to estimate the impact of money on the economy was pioneered by Sims (1980). The empirical findings are summarized by Leeper, Sims, and Zha (1996) [See the chapter 1 of Walsh (1998)]. 4 Standard (or non-structural) VAR models are used in the Choleski decomposition. It actually makes a strong assumption about the underlying structural errors, since it requires all elements above the principal diagonal in the covariance matrix to be zero.

4

The paper is organized as follows. Section II briefly reviews the history of

Korean currency crisis and the IMF’s recommended monetary policy rules during the

crisis. Section III discusses the debates of validity of the IMF’s tightening monetary

policy to stabilize the Asian currency crisis. Section IV presents the preliminary analysis

for the Korean currency crisis based on the trends of major macroeconomic historical

data and a standard VAR model. The VAR analyzes the basic features of the monetary

transmission mechanism in Korea. Section V compares the effect of monetary policy on

the real economy by VAR with that of SVAR models. In this section, I empirically

examine the validity of the IMF’s tightening monetary policy during the crisis using a

counterfactual experiment. Finally, the main results and implications of this paper are

summarized in the concluding section.

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II. Brief Review of the Korean Currency Crisis and

Monetary Policy Rules

1. Brief Review of the Currency Crisis

The Korean currency crisis in 1997 was occurred several complex factors such as

the recession of Korean economy, the weakened terms of trade, the chain of bankruptcies

including five large conglomerates, the spread of Southeast Asian currency crisis, and the

failure to prompt instrument policy measures by the Korean government. To more

deeply understand the true nature of the crisis, it is necessary to quickly review the

Korean economic situation before the crisis occurred.

The Korean economy has shown a high economic growth rate over 8.5% during

1994-95, but it showed a slowdown to 6.8% in 1996. On the other hand, the current

account turned in a deficit of $3.87 billion in 1994 and it increased substantially to

$23.01 billion or to about a 5% share of GDP in 1996 (See Table 2 in the appendix).


Here

The current account deficit was caused by rapidly increased imports from $83.80

billion to $150.34 billion during 1993-96 and the decrease of the per unit export price of

major products such as semiconductors, automobiles, steel, and electronics in the world

market. For instant, the per unit export price of memory chips and steel fell by more

than 70% and 30% respectively for the period of 1995-96. In other words, although

exports were remarkably increased from $82.24 billion in 1993 to $125.06 billion in 1996,

the payability of exports was aggravated by a sharply decreased per unit cost in 1996

(See Table 3 in the appendix).

Table 3: Trends of Account of Exports and Export Price Index

Here

6

Due to the increase in the deficit of current account, total foreign debt is rapidly

growing from $97.44 billion in 1994 to $163.49 billion in 1996 (See Table 4 in the

appendix).


Here

Financial institutions and private enterprises have also influenced the rapidly

increasing total foreign debt. They have increased the inflow of foreign capital

following the capital market liberalization in the beginning of 1990s.5

The debt ratio of the private enterprises increased from 294.9% in 1993 to 317.1%

in 1996. This gave rise to the chain of bankruptcies of Hanbo Steel & General

Construction (January), Sammi group (March), Jinro group (April), Daenong group

(May), and Kia Motors (July) due to both factors of recession and overinvestment (See

Table 5 in the appendix).

Table 5: Trend of Debt Ratio in the Manufacturing Sector

Here

The diversification and globalization of conglomerates in the mid 90s accelerated

an industrial migration to promising industries such as automobiles, construction,

distribution, electronics and petrochemical industries. The industrial migration was not a

creation of new industries based on new technology, but went into existing industries that

promised high earnings. The conglomerate’s over-investment by massive lending from

domestic financial institutions brings a low profit in the promising industries, and it

makes an insolvent enterprise by weaken financial structures. This caused several

conglomerates to file bankruptcy in 1997. This was a primary reason for the sudden rise

in the ratio of insolvent bonds in the domestic financial institutions. Both the series of

5 Korea became a member of OECD in 1996. To join, the government had to make rapid progress toward capital market liberalization in the early 1990s.

7

bankruptcies and the weakened domestic financial institutions were critical ingredients

for the depreciation of the sovereign credit rating of the Korean economy.

It was under these circumstances that Thailand’s currency crisis started in July 2,

1997. The IMF provided a bailout package ($23 billion) with the conditions of

maintaining high interest rates, and a tightening fiscal policy by setting government

spending at a one percent of GDP. The speculative attack on the exchange markets

spread to neighbor countries of Malaysia, Indonesia and Philippines, even to Taiwan and

Hong Kong.

International credit rating agencies such as Moody and S&P began to cut the

credit rating of Korean enterprises after Thailand’s currency crisis, and foreign financial

institutions stopped the rollover of short-term foreign debt to the Korean financial

institutions. As a result, domestic financial institutions and firms were forced to buy

dollars in the domestic foreign exchange market since they could not easily finance

foreign loans from foreign financial institutions.

The Bank of Korea (BOK) intervened in the domestic foreign exchange market to

supply dollars by using its foreign exchange reserves. However, the sovereign credit

ratings of Korea did not quickly recover in the short term because of the unstable

environment of investment. Foreign investors began to withdraw their funds from Korea.

Despite a relatively low external debt and a moderate current account deficit, the

Korean government was temporarily faced with an international liquidity crunch in the

foreign exchange market. The temporary liquidity crisis was worsened by the Korean

government’s policy mistakes including the bailout of Kia Motors, poor control of

foreign exchange reserves, and political uncertainty of the upcoming presidential election.

The Korean government finally requested a bailout package from the IMF: This request

occurred during the middle of the presidential election campaign on December 7, 1997.6

6 Park and Rhee (1998) listed a series of the Korean government’s policy mistakes to handle the crisis.

8

2. IMf’s Monetary Policy Rules to Recover from the Crisis and Its effect on the Economy

Under an agreement on the stand-by arrangement with the IMF on December 5,

1997, the BOK sharply increased its interest rates through open market operations to 35%

in order to overcome the currency crisis. Major market interest rates increased to 30-

40% for some time. This hyper-interest rate policy was to help stabilize the exchange

rate for the following reasons: first, it was expected to prevent rapid capital outflow and

to accelerate capital inflow, and second, it was also expected to accelerate firm’s

restructuring to reduce the import demand for new equipment investment (See Figure 1 in

the appendix).

Figure 1: Mechanism of IMF’s Tightening Monetary Policy

Here

Both actions were expected to improve the current account in the short run and,

therefore, stabilize the exchange rate.

On the other hand, the BOK changed its monetary policy stance in December,

1997 by the agreement with the IMF that mainly focused on the dual intermediate targets

to control the supply of reserve money (M0) served as an indicative limit. The reserve

money was managed by setting a floor for net internal reserves (NIR), which are the

BOK’s net external assets, and a ceiling for net domestic assets (NDA) until the third

quarter of 1998. The control of lower boundary in NIR kept a fixed level of the payment

in external assets, and the management of upper boundary in NDA limited the amount of

money supply to flow into the domestic economy.

Table 6 presents the management of BOK’s monetary policy advised by the IMF

from the end of 1997 to the third quarter of 2000.7

7 The IMF sets limits to the growth rate of money supply (M3) by the EC (European Community) formula as follows:

/ / / /M M P P Y Y V V∆ = ∆ +∆ −∆

9


Here

At the end of 1997 with concurrence of the IMF, the BOK set the indicative limit

of M3 and reserve money at 15.4% and -9.5 %, respectively. In addition, the BOK

established a floor for the NIR at -3 billion dollars and a ceiling for the NDA at 26,570

billion won as performance criteria as well. In response to the conduct of monetary

policy, the growth rate of reserve money and M3 stood at -12.5% and 13.9% respectively

during the same year. Meanwhile, the NIR and NDA met their performance criteria by

registering -3 billion dollars and 25,819 billion won respectively at the end of 1997.

In 1998, the growth rate of the reserve money sharply decreased from 7.7% in the

first quarter to -8.1% in the fourth quarter, but M3 maintained a relatively high growth

rate of 12.5-14.3% as compared to the previous year. The BOK was allowed to increase

its autonomy in the implementation of monetary policy when it was at this meeting that

the ceiling previously set on an indicative limit as the supply of reserve money was

abolished during the fourth quarter with the IMF, October, 1998. However, the BOK

kept the NIR and NDA performance criteria until 2000, and changed over the criteria as a

monitoring indicator in 2001.

Because of the sustained high interest rates by the BOK and IMF at the initial

stage of the crisis, however, a severe credit crunch in financial sectors which reduced

bank loans was created.8 This high interest rate policy was accompanied by a series of

side effects including the bankruptcy of a conglomerate (Daewoo), and a high

unemployment rate as the cost of the defense of the exchange rate for the periods of

1999:07-1999:11 and 2000:06-2001:12 (See the plot of DBR in Figure 4). In addition,

the recession did not recover in the short run due to a severe credit crunch associated with

After considering the targeting growth rate of output, inflation and the velocity of money, the IMF calculates a proper M3 growth rate. Then, the size of reserve money is determined by the basis of M3 growth rate. 8 There are three main causes of a severe credit crunch in financial sectors: first, a sharp increase of firm’s credit risk, second, conduct a banking sector’s restructuring, and third, leaving funds from bank sectors.

10

a large scaled financial sector restructuring. For instant, the growth rate of the industrial

product index had sharply decreased again during 1999:07-2001:07 (See the plot of IPI in

Figure 4).

Although the growth rate of reserve money sharply fell from 7.7% in the first

quarter of 1998 to -8.1% in the fourth quarter of 1998, the money supply of M3

maintained a relatively high growth rate of 12.5-14.3% over the same period. There are

two reasons for the credit crunch in spite of keeping a relatively high growth rate of the

M3 for the crisis periods: first, it was not an illiquid problem in financial sectors, but

rather a distortion of funding flows by contractionary economic activities, and second, it

was attributed to the slow velocity of the money supply so that the level of money supply

(12.5-14.3%) could not help encourage a contractionary economy.9 Some economists

argued that the BOK should have substantially increased its money supply to boost the

economy during the crisis in spite of an inflationary pressure.

In summary, the Korean economy was faced with an inevitable severe recession

after the outbreak of the crisis. The severe recession was exacerbated by the two factors

of excessively high interest rates and credit crunches associated with the aggressive

financial sector restructuring.10 However, the IMF argued that Korea was the only

country to have successfully reformed its financial sectors in such a short period.

9 If money supply ( M ) can be divided into two parts as money supply ( rM ) for real sectors and money supply for financial sectors ( fM ), then the velocity of money (V ) can be expressed as

( ) ( )/ /r r f f r r f fV M M V M M V S V S V= ⋅ + ⋅ = ⋅ + ⋅

In general, the velocity of financial sectors ( fV ) is assumed to be a very small value (or almost zero) since

fM does not have any effect on real sectors. Although the monetary authority has increased the money supply during a severe credit crunch, it only raises the share of money supply in financial sectors ( fS ) because most of the money supply is used in financial circulation. As a result, an increasing money supply will reduce the velocity of money, so that it will not have any influence on the real economy. 10 Most banks had to struggle to fit their BIS capital adequacy ratio over 8% by the Financial Supervisory Commission (FSC). Twelve banks failed to meet the 8% BIS ratio during the crisis and they were faced to submit rehabilitation plans.

11

III. Controversy of IMF’s Tightening Monetary Policy

1. Brief Summary of IMF's Economic Program for the Korean Currency Crisis

The IMF’s economic program for Korea prescribed by the stand-by arrangement

on December 5, 1997 can be summarized by three main categories: macroeconomic

policies; the financial sector restructuring; and, other structural measures.11

First, macroeconomic policies can be divided into a tightening monetary policy,

and an austere fiscal policy to stabilize the exchange rate, to recover confidence, and to

restabilize the economy. The main contents of tightening monetary policy and austerity

fiscal policy can be summarized as

“[Monetary policy and exchange rate policy] … To demonstrate to markets the authorities’ resolve to confront the present crisis, monetary policy will be tightened immediately to restore and sustain calm in the markets and contain the impact of the recent won depreciation on inflation. In line with this policy, the large liquidity injection in recent days has been reversed, and the call rate has been raised from 12 1/2 percent on December 1, 1997 to 21 percent today, and will be raised further in the next few days. Money growth during 1998 will be limited to a rate consistent with containing inflation at 5 percent or less. A flexible exchange rate policy will be maintained, with intervention limited to smoothing operations. … [Fiscal policy] … A tight fiscal policy will be maintained in 1998 to alleviate the burden on monetary policy and to provide for the still uncertain costs of restructuring the financial sector. The cyclical slowdown is projected to worsen the 1998 budget balance of the consolidated central government by about 0.8 percent of GDP. The present estimates of the interest costs of financial sector restructuring is 0.8 percent of GDP. Offsetting measures amounting to about 1.5 percent of GDP will be taken to achieve at a minimum budget balance and, preferably, a small surplus. This will be achieved by both revenue and expenditure measures to be determined shortly. These may include, among others: 1) increasing VAT coverage and removing exemptions; 2) widening the corporate tax base by reducing exemptions and certain tax incentives; 3) widening the income tax base by reducing exemptions and deductions; 4) increasing excises, luxury taxes, and transportation tax; 5) reducing current expenditures particularly support to the corporate sector; and 6) reducing low priority capital expenditures. … ” (IMF, 1997b)

11 See the whole contents of stand-by agreement at http://www.imf.org/external/np/oth/korea.htm.

12

Second, the financial sector restructuring includes the passing of financial sector

reform bills submitted to the National Assembly before at the end of the year, the closing

or merging of troubled financial institutions, and other conditions. Finally, the other

structural measures are about trade liberalization, capital account liberalization, corporate

governance and corporate structure, labor market reform, and information provisions.

13

2. Pros and Cons of the Tightening Monetary Policy and their Empirical Evidence

One of the most controversial issues is whether the IMF’s monetary policy during

the Asian crises was appropriate for the prevention of the rapidly depreciating currencies

in such a short period. Some economists have argued that the hyper-interest rates

prescribed by the IMF’s economic program reduced the ability of borrowers to repay

loans, and thereby weakened the banking system when deficits in the balance of

payments occur simultaneously with financial sector crises as the case in Asian countries

(See an alternative remedy for the Asian Crisis in Figure 2).


Here

Moreover, these policies may have reduced the confidence of investors, leading

to weakening in the domestic currency, and causing movement into a deep recession.

For example, the tightening monetary policy during the Asian crises has been

criticized mainly by Furman and Stiglitz (1998), and Radelet and Sachs (1998), and as

well as others.

They mentioned three reasons why the IMF’s high interest rate policy was not the

correct remedy at the beginning (and middle) stages of the Asian crises: first, hyper-

interest rates might be unsustainable in the economy, second, domestic debtors may not

have the choice of rolling over their external obligations, and finally, higher interest rates

may increase debt-service burdens for firms, lower loan performance, add to pressures on

the banking system and thereby further raise prospects of financial collapse and external

debt default.12 These would have the effect of further undermining investor confidence

and thereby having a depressive effect on currency values.

12 They raise a question in the endogeneity of domestic interest rates with respect to depreciation expectation.

14

Sachs (1997) pointed out the possibility that the IMF’s recommended

macroeconomic policies for Korea may not have been appropriate as

“There is no “fundamental” reason for Asia’s financial calamity except financial panic itself. Asia's need for significant financial sector reform is real, but not a sufficient cause for the panic, and not a justification for harsh macroeconomic policy adjustments. Asia’s fundamentals are adequate to forestall an economic contraction: budgets are in balance or surplus, inflation is low, private saving rates are high, economies are poised for export growth.” (FT, Dec. 11, 1997)

Moreover, he argues the IMF’s tightening monetary policy as

“Without wider professional debate, … Consider the Korea programme … The won has depreciated by around 80 per cent in the past 12 months, … this currency depreciation will force up the prices of traded goods. … the IMF insists that Korea aim for an essentially unchanged inflation rate … To achieve unchanged low inflation in the face of a huge currency depreciation, Korea will need a brutal monetary squeeze. …Short-term interest rates jumped from 12 1/2 per cent to 21 percent upon the signing of the programme, and have since risen further. … It is hard to see how recessionary monetary policy will restore calm.” (FT, Dec. 11, 1997)

Recently, Lee (2000) referred to the invalidity of IMF’s tightening monetary

policy as follows:

“ (1) Korean economy’s saving rates, which stood at 34-36 percent in 1995-97, were already among the world’s highest, thus leaving very little room for further improvement; (2) Higher interest rates would not contribute to large capital inflows in Korea through enlarged interest rate differential, because the local bond market, which guarantees a fixed income for foreign investors, was closed then and, in addition, the FDI flows into Korea had been severely hampered by an extremely unfavorable environment. … (3) The level of the private aggregate demand in the economy had already been squeezed to a sufficiently lower level, generating little inflationary pressures” (Lee (2000), p. 6)

He showed that the IMF’s program prescribed by its stand-by agreement for

Korea did not have an effect on the stability of exchange rates for the beginning stage of

the crisis by using Ito’s data (1999). In table 6, the value of the Korean currency (won)

is falling steadily against dollar by 30.8% in the week after December 4 when the IMF

approved stand-by credit to Korean government, and by 48.2% in the month after the

agreement. Hence, it shows that the speed of depreciation in the won after the IMF

15

approval was faster than before the program. The rapid depreciation of the won was

stopped by the second IMF program on December 24 13 (See Table 7).

Again, he insists on the invalidity of IMF’s tightening monetary policy for the

Korean currency crisis as follows:

“The IMF with the condition of a tight monetary policy does not work when lenders are rushing to get out of the country in a panic, and the root cause of the crisis is financial sector fragility. According to Diamond and Dybvig (1983) and Sachs (1995), thus corresponds to the financial-panic induced crisis and the case of a disorderly workout. If some level of exchange rate should be defended under this circumstance, enough reserves have to be there,… ” (Lee (2000), p. 18)


Here

To the contrary, others have argued that a tightening monetary policy was

necessary to stabilize the exchange rate, restore confidence, and recover economic

activity.

The IMF supported its tightening monetary policy and used the example of

Mexico, Argentina, and Brazil as follows:

“ … Both Mexico and Argentina faced significant banking crises in early 1995. In both eases, the sharp monetary tightening was accompanied by early actions to address the weaknesses in the banking sector, including liquidity support, recapitalization, and particularly in Argentina the exit of insolvent institutions from the system. And these actions made it possible to survive a period of tight monetary policy without serious damage to the financial system. … The recent experience of Brazil provides another good example. … (IMF 1997a)”

Even though the Asian crisis had basic differences with the Latin American

financial crises of the 1980s and the Mexican crisis in the mid-1990s, the IMF

emphasized the importance of tightening monetary policy to stabilize exchange rates as

follows:

13 At the same day, the G-3 finance ministers also announced an emergency agreement on the rescheduling of Korea’s short-term debts.

16

“… Monetary policy tightening is generally necessary to defend a currency that is under severe pressure. Higher interest rates raise the nominal return to investors from assets denominated in the currency and make speculation more expensive by increasing the cost of shorting the currency. Tighter monetary policy will tend to support the exchange rate also by reducing expectations of future inflation and therefore of future currency depreciation, and by lowering domestic demand and improving the current account. A forceful response to currency pressure also reduces default risk for domestic residents who have borrowed in foreign currency. … (IMF 1998c)”

Recently, many economists have tried empirical studies in order to test the IMF’s

monetary policy during the Asian currency crises. Goldfajn and Baig (1999) found little

effect of nominal interest rates on exchange rates in either direction using a VAR with

first order differenced daily data in the five Asian countries. Gould and Kamin (1999)

adopted a simple unrestricted error correction model (ECM) to empirically examine the

relationship between the exchange rate and the interest rate in Indonesia, Korea, Malaysia,

Philippines, and Thailand using weekly data from the week of July 4, 1997 to the week of

August 15, 1997. They also analyzed data for Mexico to compare the result with the five

Asian countries. However, they could not find any consistent evidence from their

empirical work. Dekle, Hsiao, and Wang (1999) found a positive relationship between

nominal exchange rates and interest rates using a VAR of weekly differenced data for

Korea, Malaysia and Thailand. Jeon (2002) empirically evaluated whether the monetary

authority should have responded with tight or expansionary monetary policy during the

crisis in Korea by performing counterfactual experiments based on SVAR models. He

concluded that expansionary monetary policy during the currency crisis may have led to a

less severe recession in Korea.

This debate still remains unresolved since it is not easy to theoretically and

empirically test for the impact of monetary policy on the exchange rate. In other words,

empirical results could depend on various factors such as economic and political

situations in each Asian country. In summary, the empirical evidence of IMF’s

monetary policy effectiveness during the Asian currency crises is not clear.

17

IV. Preliminary Analysis for the Korean Currency Crisis

1. Trends of Data before and after the Crisis

The data sets for the empirical research consist of monthly data over the period of

1980:01-2002:12. The variables are the industrial product index (IPI), consumer price

index (CPI), yield on three-year corporate bonds (CBY), overnight call rates (CALL),

money supply (M3), the ratio of dishonored bills (DBR), exchange rate (WON), foreign

reserve holdings (FRH), and a structural dummy. 14 These are for the proxy variable of

output (IPI), inflation (CPI), long-term interest rates (CBY), short term interest rates

(CALL), the broadest concept of money supply (M3), the capture of a credit crunch

(DHR), considerations in the influences of foreign sectors (WON and FRH), and a

reflection in the structural changes (D97) after the currency crisis in 1997.

Although the variable of M3 has a demerit to capture an observed time lag, the

growth rate of M3 that is compared with M0 and M2 has continuously decreased at the

end of IMF program (See Figure 3).

Figure 3: Trend of Three Kinds of Money Supply

Here

Moreover, the IMF and BOK controlled M3 instead of M2 as the major monetary

policy instrument during the crisis. Due to these, M3 is a reasonable proxy variable as

money supply in the purpose of this study. On the other hand, the data can be classified

three major categories: first, six closed economic variables (IPI, CPI, CBY, CALL, M3

and DBR); second, two open economic variables (WON and FRH); and third, an

exogenous structural changes variable (D97). The definition and source of each variable

are detailed in Table 8.

Table 8: Data for Empirical Studies

14 See the definition of M3 in the note of Figure 3.

18

Here

Figure 4 presents the trends of major macroeconomic variables on the 5 years

before and after the crisis. The variables of CPI, IPI, and M3 are the growth rate of the

same month of the previous year and the others (CALL, CBY, DBR, FEH and WON) are

controlled level data in the figure.

Figure 4: Plots of Major Macroeconomic Variables before and after Five Years around the Crisis

Here

The trends of CALL, CBY, CPI, DBR, and WON had sharply increased and then

decreased during the period of 1997:08-1998:08 (See light yellow shading intervals in

Figure 4). Especially, the CALL had increased by more than 50% in December, 1997

when the BOK conducted the open market operations to increase short-term interest rates

as recommended by IMF’s tightening monetary program. The CBY showed a similar

level at the same period as well. The IPI started to rapidly decrease from October, 1997

and reached its trough in July, 1998. After that, the IPI kept increasing and jumped to a

peak in July, 1999. However, the IPI remarkably decreased again during1999:07-

2001:07 (See the light green shading intervals of IPI in Figure 4).

In relation to this pattern, Lee and Rhee (2000) analyzed the macroeconomic

adjustment process of the Korean financial crisis using a cross-country data set over the

period from 1973 to 1994. They found that the pattern of Korea’s macroeconomic

recovery process might be described as a Y shape instead of a V pattern. This Y shape

means that the degree of initial contraction and following recovery processes has been far

greater in Korea than its cross-country’s stylized empirical regularities.

Especially, the movements of CPI, DBR, IPI, M3, and WON showed a very

unstable pattern again in the periods of 1999:02-2001:05 for CPI, 1999:07-1999:11 and

2000:06-2001:12 for DBR, 1999:07-2001:07 for IPI, 1998:09-2000:02 for M3, and

2000:08-2001:04 for WON after the initial crisis shock (See light green shading intervals

in Figure 4). There seems to be indirect evidence that the Korean economy was faced

19

with a deep recession around fifteen to twenty-two months following the currency crisis

shock. Roughly, the Korean economy has maintained a stable condition since July of

2001.15

15 One might be interested in several major events related with the crisis. For your reference, An event table is presented in appendix (See Table 9).

20

2. Monetary Transmission Mechanism based on a VAR Model

Before examining the effects of monetary policy shocks for the Korean currency

crisis, it is useful at the early stage of preliminary analysis to investigate the basic

features of the monetary transmission mechanism in Korea using a standard (recursive)

VAR model. 16

The recursive VAR model is made up eight variables (IPI, CPI, CALL, CBY, M3,

DHR, WON, and FRH) and two exogenous variables (a constant term and a structural

dummy; D97) with four lags from 1981:01 to 2002:12.17 The variable of IPI, CPI, M3

and FRH takes a natural log into the original data and others are all level data.

Table 11: VAR Lag Order Selection Criteria

Here

The ordering of variables in the VAR model is determined by estimating the

result of all contemporary variables, so that the sequence of variables is IPI CPI CBY

CALL M3 DBR WON, and FEH (See Table 12 in the appendix).18


Here

To capture structural changes after the crisis, a structural dummy (D97) is

included to the VAR model. According to the LR test, D97 is highly significant at the

5% significant level in the VAR system (See Table 13 in the appendix). 19

16 Although Mishkin (1995) classified four channels of monetary transmission mechanism (the interest rate channel, the exchange channel, other asset price effects, and credit channel), it does not seem to be a perfect match with the case of Korean currency crisis (For more details, see Table 10 in the appendix). 17 An optimal lag length is selected by the FPE and AIC criteria (See Table 11 in the appendix). 18 I slightly modified the ordering of variable in the VAR model on the basis of the estimated results in Table 12. 19 The hypothesis and test statistics for D97 are as follows:

0 : 97 0H D = ; 2(1)2(log log ) 2(1297.050 1313.367) 32.634 ( 0.05) 3.8415R ULR L L x α= − − = − − = > = =

21

Table 13: Estimated Result of a Standard VAR Model (1981:01-2002:12)

Here

Figure 5 shows the impulse response of all variables (IPI, CPI, M3, CALL, CBY,

DBR, WON, and FEH) to a monetary policy instrument (CALL) with two standard error

bands by the recursive VAR model (See Figure 5 in the appendix).20

Figure 4: Impulse Response by the Standard VAR Model

Here

The order of impulse responses is the same order of endogenous variables in the

VAR model21.

In Figure 5, the response of IPI to CALL shows a usually hump-shaped pattern

that has a negative output effect of a contractionary shock to monetary policy. The

negative effect reaches its trough after nine months and then gradually converges to zero.

This is a reasonable response of output against the shocks of a high interest rate policy.

A contractionary shock to monetary policy has a positive influence on the

responses of CPI and M3 over the whole time horizon. The effect of shocks does not

quickly subside and is persistent. Particularly, it notes that the response of the CPI has

significantly increased after an initial contractionary shock to a monetary policy over one

to four months. However, the response of the CPI decreases over five to eight months.

It continues to decrease again from eighteen months after the initial shock.

The null hypothesis could not be rejected at the 5% significant level. Hence, it is reasonable to include the dummy variable in the model. 20 I analyze the responses of all variables to M3 as well, but it does not have any differences including VAR and SVAR models except for the magnitude of response function (See Figure 16 in the appendix). 21 The ordering conditions in the impulse response of VAR models are not theoretically clear yet.

22

Hence, the standard VAR model could not avoid a puzzling problem of price in

the open economy studies of VAR literature if CALL shocks were interpreted as

measuring the impact of monetary policy.22

The response of CBY and WON to a contractionary monetary policy shock has a

positive effect, but quickly declines to zero thirteen to fifteen months later. Note that the

impulse response of WON to CALL displays a quick decline except for two months and

shortly converges to zero sixteen months later. Hence, there exist interest rate and

exchange rate puzzles in the VAR model.23

It has an ambiguous relationship in the response of DBR and FEH to CALL, and

has a minor effect on the whole time horizon.

In summary, the response of IPI, CPI and M3 to a contractionary shock in CALL

has an effect over a relatively long period of time and does not quickly disappear.

Therefore, these responses imply that a suddenly change to a contractionary monetary

policy shock during a short period might have a significant negative effect on the

economy. On the other hand, the impulse response analysis by the standard VAR model

contains the problem of price, interest rate and exchange rate puzzles.

22 This puzzle does slightly appear in the SVAR model as well, but it is remarkablely improved (See Figure 8 in the appendix). It may be caused for using M3 as a proxy variable of money supply instead of M1 or M2 in this study. The other possible interpretation about the price puzzle may be attributed to misspecification of the eight variables VAR system. For instant, Christiano et al (1996) solve for such a price puzzle including commodity price index into their VAR models. 23 Both puzzles do not appear from the baseline SVAR model (See Figure 7 in the appendix).

23

V. Impulse Response Analysis Based on a SVAR Model

and Counterfacutal Experiment

1. Impulse Response Analysis based on a SVAR Model

1) Non-Recursive Identification for a SVAR Model

VAR models have been widely used for the empirical research of monetary

economics since Sims (1980). However, Cooley and LeRoy (1985) criticized a standard

VAR model since it solely depends on a strong assumption about the underlying

structural errors in the Cholesky decomposition. In addition, Eichenbaum and Evans

(1995), Grilli and Roubini (1995), Cushman and Zha (1997), and Kim and Roubini

(2000) point out that the impulse response based on a recursive identification scheme in

open economy studies may result in a puzzling problem of the exchange rate. They

recommend a non-recursive structural identification scheme to analyze impulse responses

instead of a standard Cholesky decomposition.24 Due to these reasons, this paper has

adopted a SVAR model to analyze the impulse response of 8 endogenous variables in a

contractionary shock to monetary policy instruments during the Korean currency crisis.

In general, a non-recursive identification scheme for the SVAR model can be

written in the following matrix form.25

(1) t te εΑ = Β

where,

24 The problem of Choleski decomposition in the VAR frameworks is mentioned to the introduction section

(See the footnote 4). 25 For more details, the equation (1) can be obtained by a technical note in the appendix.

24

Α is a (8 8)× matrix for contemporaneous short-run structural restrictions;

Β is a (8 8)× identity matrix for long-run restrictions 26;

te is a (1 8)× vector for observed (or reduced form) residuals; [ ]t tE e e′ = Ω ;

tε is a (1 8)× vector for unobserved structural innovations; [ ] It tE ε ε ′ = .

It is very important for identifying the short-run pattern matrixΑ to analyze an

impulse response using a SVAR framework, since the response depends on the

restrictions of pattern matrixΑ .

To specify the specific restrictions of matrixΑ in a SVAR model, this study

used the same method as Sprites et al. (1993), and Swanson and Granger (1997). Sprites

et al. (1993) developed the Tetrad III package. Tetrad III is a kind of statistical software

used to analyze contemporaneous causal relationships among variables including latent

variables.27

Figure 6 presents the causal diagram of a baseline SVAR model (SVARact) by

Tetrad III. The diagram indicates the direction of causal relationships among the eight

endogenous variables.28


Here

As shown by Figure 6, a contemporaneous causal relationship among eight

endogenous variables is as follows: first, WON, FEH, CBY, and M3 is a direct cause of

DBR, WON, FEH, and CPI, respectively; second, CBY, M3 is also a direct cause of 26 The long-run restrictions can be interpreted as constraints on the cumulative impulse response function. 27 Sprites et al. (1993) have developed Tetrad project that allows the specification of contemporaneous causal relationships among a series of variables including the existence of latent variables to be statistically test. Tetrad III (recently, Tetrad IV) conducts simple or conditional independence tests for a pair of variables under the multivariate normal distribution assumption. Moreover, Swanson and Granger (1997) introduced a graphical approach to the identification of causality in VAR models based on the Sprites et al. (1993)’s method. Note that a causal Markov condition does not hold if there exists a serial correlation in data, so that they solve this problem by eliminating a serial correlation in the time series data (See the manual of Tetrad III in detail at http://www.phil.cmu.edu/projects/tetrad/tet3/master.htm). 28 Figure 6 is based on an empirical result by Tetrad III (See Table 14 in the appendix).

25

CALL, IPI, respectively, third, there is a feedback effect between FEH and CPI, fourth,

there is a feed back relationship between CBI and IPI as well, and finally, there is a

multilateral effect on CALL and IPI. Hence, a pattern matrix for the short-run

restrictions of SVARact model can be written as 29

(2)

11 12 13 14

21 22 23 283

33

41 44 45

55

66 67

77 78

82 85 88

0 0 0 00 0 0 0

0 0 0 0 0 0 00 0 0 0 0

0 0 0 0 0 0 00 0 0 0 0 00 0 0 0 0 00 0 0 0 0

IPIt tCPItMtCALLtCBYtDBRtWONtFEHt

a a a a ea a a a e

a ea a a e

a ea a e

a a ea a a e

ε⎡ ⎤⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥ = Β⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦ ⎣ ⎦

3

IPI

CPItMtCALLtCBYtDBRtWONtFEHt

εεεεεεε

⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

where,

ija and ijb elements in Α and Β matrices should be estimated; and 1, ,8i j = … ;

11

88

0

0

b

b

⎡ ⎤⎢ ⎥Β = ⎢ ⎥⎢ ⎥⎣ ⎦

; Main diagonal elements ( jjb ) in the matrix Β are not zero.

Note that the main diagonal elements of matrixΑ normalize as one in order to

reduce the number of restrictions. Table 15 reports the estimated result of short-run (Α )

and long-run (Β ) pattern matrices for the SVARact model that are maximized by the

Marquardt algorithm.


Here

29 In order to exactly identify the system from the eight equations of VAR, it is necessary to impose twenty eight restrictions [ 2(8 8) / 2 28− = ] on the structural model (See Enders; 1995).

26

In Table 15, a null hypothesis can be rejected at the 5% significant level by a LR

test for an over-identification. Hence, it does not arise against an over-identification

problem for the SVARact model.

2) Impulse Response for SVARact

Figure 7 presents the plots of impulse response in eight variables to a one percent

contractionary monetary policy shock (CALL) with two standard error bands. The

response is based on a SVARact(4) model with a structural dummy (D97).

Figure 7: Impulse Response by the SVARact Model (1981:01-2002:12)

Here

Basic features in the response of 8 variables to CALL are similar to that of the

standard VAR model except for the response of CBY, WON and FEH (See Figure 8 in

the appendix).30 In other words, there is a different pattern in the response of CBY,

WON and FEH to a contractionary monetary policy shock for roughly seven (response of

CBY and WON) to eighteen (response of FEH) months between the VAR and SVARact

models (See Figure 8 in the appendix).

Figure 8: Comparison Impulse Responses by VAR and SVARact Models

Here

First, the response of CBY to CALL initially keeps increasing and reaches its

peak at seven months. After that, it quickly dies out and converges to zero around

twenty months later. Hence, the movement of CALL against a contractionary monetary

policy shock has a strongly positive effect on the trend of CBY in short-run. This is

matched with a traditional monetary propagation mechanism in the short run. 31

30 Note that the response of CPI to CALL between VAR and SVARact models has a similar shape, but the response of CPI in SVARact model initially decreases over two months. Moreover, the size of response in SVARact shows much smaller than its of VAR. This means that the impulse response by the SVARact model is noticeably improved a price puzzling problem. 31 In general, monetary policy affects from short-run interest rates to long-run interest rates in short-run.

27

Second, the response of WON to CALL continuously decreases during the first

four months, and then quickly increases up to nine months.32 The effect gradually shows

down until around twenty four months and there is no longer any effect after the periods.

This implies that a sharply increasing CALL in a short period has a potential to accelerate

the depreciation of exchange.

Finally, the response of FEH to CALL continuously decreases for seven months,

and then it keeps increasing during the rest of the period. Hence, there is improved a

time lag of FEH by a high interest rate policy. The high interest rate will be effective in

the relative long term, but not in the short term.

In summary, the SVARact model, unlike the recursive VAR model, explains well

the features of a monetary policy transmission mechanism for a contractionary shock. It

also eliminates the three major puzzles (price, interest rate and exchange rates puzzles) in

Korea.33 To defend an exchange rate during the currency crisis, however, a hyper-

interest rate policy may have a strong negative effect on the economy after the currency

crisis by the response of WON to CALL.

32 This is matched up with an uncovered interest parity condition (UIP). According to the UIP, the expected exchange rate can be decide as

1 1 ( )e d ft t t tWON R R WON+ ⎡ ⎤= + −⎣ ⎦

where, dtR and f

tR are domestic and foreign interest rates, respectively. Hence, if domestic interest rates are increased and foreign interest rates are maintained a fixed level, then expected exchange rate is increased as well after some lags. 33 According to Christiano et al. (1996), they adopt a VAR model to derive stylized facts on the effect of a contractionary policy shock as follows: 1) price as a proxy of inflation initially responds very little, 2) interest rates initially rise, and 3) output initially falls that has a j-shaped respond and converges to zero in the long run. The impulse response of a contractionary shock to CALL by the SVARact model successfully contains these facts compared with a standard VAR (See Figure 8 in the appendix).

28

2. Counterfactual Experiment

In the introduction, I mentioned that it has proved a difficult evaluate IMF’s

tightening monetary policy during the Korean currency crisis using statistical or

econometric methods. Related to this issue, one might be faced with the following

question: How can one conduct a counterfactual analysis in order to compare the effect

of monetary policy on a real economy under different policy rules? Usually, a

counterfactual analysis evaluates the behavior of related macroeconomic variables under

the assumption of changes in a certain policy rule (decision) during a specific period

(event).

For example, Rudebusch and Svensson (1999) simulated a small macroeconomic

model of the U.S. economy. They evaluated a number of monetary policy rules on the

basis of a dynamic old-fashioned Keynesian model.34 Bernanke et al. (1997) followed a

Sims and Zha (1996)’s approach to analyze a relationship between the effect of an oil

price shock and a systematic tightening monetary policy. They investigated whether or

not oil price shocks generated the U.S. recessions during the past thirty years.35

Specifically, Bernanke et al. (1997) introduced a new method to partially overcome the

Lucas critique. They analyzed systematic monetary policy shocks on the basis of VAR

models. They also assumed that financial variables such as interest rates, exchange rates,

and stock price indexes rationally respond to policy changes, while other macroeconomic

variables such as output, inflation, labor and oil prices are relatively less affected than the

variables in financial sectors. 36

34 They add two monetary policy rules (inflation and interest rates) as a type of estimated single equation into their AD and AS systems that are based on non-VAR models. Then, they conducted a historical simulation of reduced form of AS and AD with two policy rules, two other equations, AD and AS shocks that the shocks were generated by random number generator. 35 Actually, they tried to identify whether the tightening monetary policy or oil price shock generated periodical recessions or in the U.S. To do this, they shut off a certain policy response (federal fund rate) in their VAR system to analyze counterfactual scenarios. Technically, they did not directly include the federal fund rates into the non-policy equations of their VAR model. 36 Sims (1998) argued on the Lucas Critique in SVAR models to exemplify the three cases of version of Lucas Critique in relation to an empirical analysis in macroeoconomics (See the section of What about the Lucas Critique (pp. 152-156) in Sims (1998) for more details).

29

However, Rudebusch and Svensson (1999)’s simulation results mainly depended

on the form of exogenous policy rules, since the rules were estimated by a single equation

including related variables. However, their model had a problem of which policy rules

should be included in the simulation experiments. Unlike VAR models, their model did

not consider any feedback effect on other variables in the system, since they assumed a

given exogenous policy reaction function.

On the other hand, Sims and Zha (1996), and Bernanke et al. (1997) cut off a

certain policy variable’s effect (federal fund rates) from their original VAR models.

They showed an alternative policy effect on a real economy with the consideration of

dynamic relationships among endogenous variables.

To examine the effects of an alternative policy reaction in the VAR framework, I

conducted a counterfactual experiment to follow the Sims and Zha (1996) and Bernanke

et al (1997)’s methods. Detailed procedures about their methods are as follows:

First, I separately estimated the eight equations using OLS, and computed a

standard error for each equation. Second, I divided the eight estimated equations into

two groups. One consisted of the eight estimated equations including a monetary policy

reaction equation (CALL), and the other made up of the seven estimated equations except

for the policy reaction equation. Third, I separately simulated the two groups of

estimated equations. Finally, I carried out forecasting shocks to obtain conditional IRs

of each variable to a CALL shock as a counterfactual monetary policy effect.

Figure 9 presents the plots of baseline and counterfactual impulse responses in

eight variables based on the procedures above. The counterfactual impulse response

shows a similar pattern with the response of VAR or SVAR during the entire time

horizon. However, there are four differences in the counterfactual response of IPI, M3,

DBR, WON and FEH to CALL:

30

First, the counterfactual response of IPI to CALL initially increases for three

months, and quickly decreases until the 15th month. After the 15th month, the response

continuously increases with a positive trend. When the counterfactual response is

compared with baseline response, it has the properties of an initially positive effect on

output at the initial stage of crisis, a quick structural adjustment in the middle of crisis,

and a persistent boost of the economy with a rapid end of crisis. However, there is a

high cost of inflation in the response of CPI to CALL until the middle of crisis.

Second, the counterfactual response of DBR to CALL initially decreased for 2

months, and then the response followed a similar trend to the baseline response.

However, the response gradually decreased after the 16th month unlike the baseline

response. This implies that the counterfactual monetary policy may be able to help the

reduction of bankruptcy in the middle of the crisis.

Third, the counterfactual response of WON to CALL has a similar shape as the

baseline response during the entire time horizon. This implies that the counterfactual

monetary policy has the same effect on the suppression of the devaluation of exchange

rates.

Finally, the counterfactual response of FEH to CALL sharply increases for the 7th

month, and then gradually declines after that. The counterfactual monetary policy has

the same positive effect on foreign exchange holdings, but the magnitude shows a larger

effect than that of the contractionary monetary policy.

31

VI. Conclusion

The purpose of this paper is to examine the validity of the IMF’s austere monetary

policy at the beginning stage of Korean currency crisis. This is a particularly hot issue to

assess the role of the IMF in relation with the Asian crisis. Before investigating this

topic, the paper shows the basic features of the monetary transmission mechanism in

Korea using VAR and SVAR models. The aim of this preliminary analysis is for a

deeper understanding about the monetary policy effects on the economy.

The main issue of this paper is to evaluate the IMF’s hyper-interest rate policy to

support exchange rates using a counterfactual experiment. The purpose of this

counterfactual experiment compares the effect of an alternative monetary policy on the

Korean economy during the crisis.

The main findings are as follows:

First, the overall pattern of impulse responses by counterfactual experiment

follows the responses of VAR and SVAR models. Hence, the counterfactual experiment

is fairly well reflected in the basic features of monetary transmission mechanism for

Korean economy.

Second, the counterfactual response has an initially positive effect on output at the

beginning stage of crisis. In addition, the response shows a quick structural adjustment

process in the middle of crisis. These imply that much a lower interest rate policy

instead of IMF’s hyper interest rate policy during the beginning stage of crisis may have

more quickly boosted the economy and helped speed recovery from a deep recession

caused by a severe credit crunch.

Third, the counterfactual response shows an initially decreasing the ratio of a

dishonored bill rate at the beginning stage of the crisis. This means that the

counterfactual monetary policy has a less negative effect on the economy, and can

32

potentially reduce the bankruptcy of small and large firms including the five

conglomerates. Hence, this evidence suggests that the policy might be a possibility to

more rapidly climb out of a deep recession in the middle of the crisis as well.

Finally, the counterfactual monetary policy has the same effect to prevent the

sharp devaluation of Korean currency caused by a rapid outflow of foreign capital.

Unlike the IMF’s expectation, the policy has a strong positive effect on the inflows of

foreign capital.

According to the empirical findings above, it suggests that an unnecessary hyper-

interest rate policy and a massive restructuring of financial sectors by IMF may worsen

the economy during the early stages of the crisis, since the basic economic fundamentals

of a pre-crisis Korea were a very healthy except for the deficit of current account.

From this point of view, the IMF’s hyper-interest rate as an austere monetary

policy was not proper to quickly overcome the crisis at its initial stage. This also implies

that an alternative monetary policy may have more positive effects on the economy

without making a massive negative effect on the financial sectors. This conclusion

would be similar for the Thailand and Indonesian economies, since the IMF

recommended the same monetary and fiscal policies for these economies.

33

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36

J. C. and Schuh S., eds. Beyond Shocks: What Causes Business Cycles. Federal

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37

Appendix

A-1. Technical Note for SVAR Models

If the vector of stochastic processes ty is given in a finite order autoregressive

representation, it can be written as

(a1) ( ) t tZ L y e=

whrer,

te is a 1 n× vector of observed (or reduced form) residuals.

In addition, the reduced form residuals has a relation with an unobserved

structural innovations ( tε ).

(a2) Ct te ε=

where,

C is a n n× unknown invertible matrix.

The covariance matrix of observed residuals is

(a3) [ ]t tE e e′ = Ω

where,

Ω is the n n× covariance matrix of observed residuals.

On the contrary, the structural innovations tε are assumed to be orthonormal.

Hence, the covariance matrix of tε is an identity matrix.

38

(a4) [ ] It t nE ε ε ′ =

where,

In is a n n× identity matrix.

Premultiply the autoregressive representation by an invertible matrixΑ .

(a5) ( ) t tZ L y eΑ = Α

Then, the relationship between te and tε are changed as

(a6) t te εΑ = Β

where,

CΒ = Α ; Α and Β are n n× matrices to be estimated

The equation can be expressed as

(a7) t t t te e ε ε′ ′ ′ ′Α Α = Β Β

Taking expectation above, it yields

(a8) [ ] [ ]t t t tE e e E ε ε′ ′ ′ ′Α Α = Β Β′ ′ΑΩΑ = ΒΒ

Equation (a8) can be imposed a contemporaneous (short-run) restriction and a

long-run restriction on Α and Β matrices, respectively.

39

A-2. Tables Table 1: Major Economic Indicators before and after the Three Asian Crisis Countries

Indonesia South Korea Thailand

before After Before after before After

Real GDP Growth (1995 Constant Price,

billion US $

218 (7.92)

199 (-12.72)

522 (6.75)

512 (-6.57)

178 (5.95)

157 (-10.29)

CPI (1995=100) 107.97 181.66 104.93 118.82 105.81 120.77

Unemployment Rate (%) 3624.8 5062.5 2.00 6.84 1.10 3.40

Current Account (Current billion US $) -8 4 -23 40 -15 14

Exchange Rate (LUC/US $) 2,342.30 10,013.60 804.45 1,401.44 25.34 41.36

Deficit or Surplus (LCU, billion) 6,180 -28,191 1,099 -18,757 43,303 -128,951

Total Foreign Debt (billion US $) - - 164.49 148.71 - -

Notes: The figures in parentheses are percentage changes over the previous year. Sources: IFS CD ROM (2002), WDI CD ROM (2001), and BOK Statistics DataBase.

40


1993 1994 1995 1996 1997 1998 1999 2000 2001

GDP Growth

Rate (%) 5.5 8.3 8.9 6.8 5.0 -6.7 10.9 9.3 3.1

Exports (bill. US $)

82.24 (7.3)

96.01 (16.8)

125.06 (30.3)

129.72 (3.7)

136.16 (5.0)

132.31 (-2.8)

143.69 (8.6)

172.27 (19.9)

150.44 (-12.7)

Imports (bill. US $)

83.80 (2.5)

102.35 (22.1)

135.12 (32.0)

150.34 (11.3)

144.62 (-3.8)

93.28 (-35.5)

119.75 (28.4)

160.48 (34.0)

141.10 (-12.1)

Current Account

(bill. US $) 0.99 -3.87 -8.51 -23.01 -8.12 40.37 24.48 12.24 8.24

CPI Growth

Rate (%) 4.8 6.3 4.5 4.9 4.4 7.5 0.8 2.3 4.1

Deficits (or Surplus) (bill. Won)

813 (0.003)

1,384 (0.004)

1,241 0.003

1,099 0.003

-6,959 -0.002

-18,757 -0.042

-13,065 -0.027

6527 0.013

7,268 0.013

Notes: 1) GDP growth is constant prices; 2) Exports and Imports are based on the customs clearance;

3) Parentheses in Exports and Imports are percentage changes that are compared with the same period of the previous year;

3) Parentheses in Deficit (or Surplus) are the share of deficit (or surplus)/GDP on the basis of central government at current prices.

Source: BOK, Monthly Statistics, Various Issues. Table 3: Trends of Amount of Exports and Exports Price Index

1993 1994 1995 1996 1997 1998 1999 2000 2001

Amount of Exports

(billion US $) 82.24 96.01 125.06 129.72 136.16 132.31 143.69 172.27 150.44

Export Price Index (1995=100) 96.0 98.7 100.0 95.8 102.1 134.1 108.8 107.7 114.4

Source: BOK, Monthly Statistics, Various Issues.

41


1994 1995 1996 1997 1998 1999 2000 2001

Long-Term Foreign Debt (a) (billion US $)

43.50 [44.65]

55.60 [43.61]

70.17 [42.92]

95.68 [60.09]

118.01 [79.36]

97.85 [71.39]

83.75 [63.60]

77.81 [65.49]

Public Sector (%) (16.6) (12.0) ( 8.7) (23.3) (31.0) (30.1) (33.7) (26.7)

Financial Sector (%) (47.0) (55.7) (62.0) (49.7) (44.1) (39.3) (29.9) (29.3)

Private Sector (%) (36.4) (32.3) (29.3) (27.0) (24.9) (30.5) (36.3) (44.0)

Short-Term Foreign Debt (b) (billion US $)

53.93 [55.35]

71.89 [56.39]

93.32 [57.08]

63.56 [39.91]

30.70 [20.64]

39.22 [28.61]

47.92 [36.40]

41.01 [34.51]

Public Sector (%) ( 0.0) ( 0.0) ( 0.0) ( 0.0) ( 0.0) ( 0.0) ( 0.0) ( 0.0)

Financial Sector (%) (82.8) (81.6) (78.2) (66.7) (61.7) (57.3) (53.6) (54.2)

Private Sector (%) (17.2) (18.4) (21.8) (33.3) (38.3) (42.7) (46.4) (45.8)

Total Foreign Debt (a)+(b) (billion US $) 97.44 127.49 163.49 159.24 148.71 137.07 131.67 118.82

Notes: 1) The figures in parentheses are the share of each subsector in the short-term and long-term debts;

2) The figures in brackets are the share of short-term and long-term foreign debts in the total foreign debt.

Source: BOK, Statistics DataBase. Table 5: Trend of Debt Ratio in the Manufacturing Sector

1993 1994 1995 1996 1997 1998 1999 2000 2001

Debt Ratio (%) 294.88 302.52 286.75 317.11 396.25 303.02 214.66 210.57 182.20

Note: Debt Ratio = (Total Liabilities/Equity Capital)×100. Source: BOK, Business Survey, Various Issues.

42


NIR Lower bound

(billion $)

NDA Upper bound (billion Won)

Reserve Money M3 Year

ceiling Perform. ceiling Perform. ceiling Perform. ceiling Perform.

1997 -3 -3 26,571.0 25,819.3 23,270.0 (-9.5)

22,519.3 (-12.5) 15.4 13.9

1998 I 3.8 8.2 17,875.0 10,145.0 23,580.0

(15.2) 22,035.1

(7.7) 13.5 14.3

II 13.9 19.1 4,080.0 -5,941.1 23,540.0 (13.5)

20,798.9 (0.3) 14.1 13.2

III 15.0 24.5 248.0 -11,062.5 25,430.0 (14.2)

22,026.0 (-1.1) 14.0 13.5

IV 23.7 31.2 -5,170.0 -19,857.0 - 20,703.0 (-8.1) 13.5 12.5

1999 I 31.8 39.2 -17,341.0 -28,548.0 -

() - 13.1

II 40.3 48.4 -25,787.0 -36,646.0 - () - 12.0

III 55.2 58.5 -42.540.0 -45,361.0 - () - 9.3

IV 62.4 67.0 -47,917.0 -51,913.0 - () - 8.0

2000 III 84.0 86.5 -71,794.0 -77,111.0 -

() - 6.3

Notes: 1) All figures are based on the end of period;

2) Figures in parentheses are percentage changes over the same period of the previous year. 3) Net international reserves (NIR) of BOK is defined as the U.S. dollar value of gross foreign

assets in foreign currencies minus gross foreign liabilities. 4) Net domestic assets (NDA) is defined as a difference between reserve money and the won

equivalent (converted at the program exchange rate) of net international reserves (NIR). 5) M3 is defined as M2 plus deposits of other financial institutions, debentures issued,

commercial bills sold, “deposits of credit unions,” mutual credits of the National Federation of Fisheries, “Community Credit ‘cooperatives”, Mutual Savings and Finance Cooperatives situated in local and reserve life insurance company, certificates of deposit, repurchase agreements, and coverbills. M2 is defined as currency in circulation, plus deposit money (demand deposits at monetary institutions, time and savings deposits, and residents’ foreign currency deposits at monetary institutions).

Source: BOK, Annual Report, Various Issues, and Statistics DataBase.

43


before After

1 Month 2 Weeks 1 Week

IMF Even Day

1 Week 2 Weeks 1 Month

96.2 101.9 101.0 Aug. 11, 1997 Thailand 102.1 108.6 113.0

93.4 96.9 96.6 Aug. 20, 1997 Thailand 104.4 115.5 113.5

90.7 99.4 99.2 Oct. 31, 1997 Indonesia 91.7 95.4 101.2

80.3 86.5 92.8 Dec. 3, 1997 Korea 130.8 123.8 148.2

59.0 85.2 80.6 Dec. 24, 1997 Korea 92.1 95.1 95.0

64.8 61.4 112.8 Jan. 15, 1998 Indonesia 148.0 122.3 105.0

Source: Ito (1999). Table 8: Data for Empirical Studies

Variable Definition Source

IPI Industrial Product Index (2000=100, Seasonal Adjusted) KOSIS

CPI Consumer Price Index (2000=100) KOSIS

CBY Yield of Three-Month Corporate Bonds (%) BOK

CALL Call Rates (%, Overnights,) BOK

M0, M2, and M3 Money Supply (Billion Won) KOSIS

WON Exchange Rate (Won/Dollar, Average Market Rate) KOSIS

FRH Foreign Reserve Holdings (Billion Dollar) BOK

DBR Ratio of Dishonored Bills (%) BOK

D97 Structural Dummy 1:1997 : 08 2002 :120 : otherwises

−⎧= ⎨⎩

-

44

Table 9: Major Economic Events

Date Major Economic Event

1997. 1. 23 Bankruptcy of Hanbo Steel & General Construction

3. 19 Bankruptcy of Sammi Group

4. 21 Bankruptcy of Jinro Group

5. Bankruptcy of Daenong Group

7. 2 Thailand’s Currency Crisis started

7.15 Kia Motors ask for emergency bank loans to avoid bankruptcy.

8.23 South Korea's currency crisis started.

10.22 Near Bankruptcy of Kia Group

11. Indonesia’s Currency Crisis started.

12. 4 IMF approved stand-by credit ($5.56 billion) for Korea.

12.24 IMF and eight country leader agree to advance $10 billion.

12.30 Foreign banks agree to roll over certain short-term loans.

1998. 9. The redemption of IMF's Supplemental Reserve Facility (99.5 billion SDR) completed.

10. Ceiling set as an indicative limit on the supply of reserve money abolished.

1999. 7.19 Daewoo Group's structural adjustment announced.

8. Decision the Daewoo Group’s workout

9.21 Setting up the funds of stabilization in a bond market

1999 BOK did not set an indicative limit of reserve money supply, but had kept the performance criteria of NIR and NDA.

2000. 8 A redemption of IMF's Credit Tranche (44.6 billion SDR) completed.

2000.12. 3 IMF's stand-by treaty end.

2001 BOK changed M3 to monitoring indicator.

2001. 5.23 IMF's Post Program Monitoring (PPM) end.

45

Table 10: Monetary Transmission Mechanism

Channel Propagation Mechanism

Interest Rate Channel M i I Y↓ ⇒ ↑ ⇒ ↓ ⇒ ↑

Exchange Rate Channel M i E NX Y↓ ⇒ ↑ ⇒ ↑ ⇒ ↓ ⇒ ↓

Other Asset Price Effects eM P q I Y↓ ⇒ ↓ ⇒ ↓ ⇒ ↓ ⇒ ↓ eM P W C Y↓ ⇒ ↓ ⇒ ↓ ⇒ ↓ ⇒ ↓

Credit Channel

bank deposits bank loans M I Y↓ ⇒ ↓ ⇒ ↓ ⇒ ↓ ⇒ ↓

adverse selection & moral hazard lending

I eM P

Y

⎧ ↓ ⇒ ↓ ⇒ ↑ ↑ ⇒ ↓⎪⎨⇒ ↓ ⇒ ↓⎪⎩

cash flow adverse selection & moral hazard lending I

M iY

⎧ ↓ ⇒ ↑ ⇒ ↓ ⇒ ↑ ↑⎪⎨⇒ ↓ ⇒ ↓ ⇒ ↓⎪⎩

financial assets likelihood of financial distress

consumer dural & housing expenditure eM P

Y

⎧ ↓ ⇒ ↓ ⇒ ↓ ⇒ ↑⎪⎨⇒ ↓ ⇒ ↓⎪⎩

Source: Mishkin (1995). Table 11: VAR Lag Order Selection Criteria

Lag LogL LR FPE AIC SC HQ 0 -2001.214 NA 0.000598 15.28192 15.49865 15.36901 1 1170.231 6102.629 3.58E-14 -8.259326 -7.175705* -7.823894 2 1290.772 224.6445 2.33E-14 -8.687666 -6.737149 -7.903889* 3 1380.208 161.2554 1.93E-14 -8.880362 -6.062947 -7.748239 4 1458.957 137.2149 1.74E-14* -8.992100* -5.307789 -7.511632 5 1502.624 73.44025 2.05E-14 -8.838063 -4.286855 -7.009250 6 1565.922 102.6186 2.09E-14 -8.832741 -3.414636 -6.655582 7 1627.163 95.57282 2.18E-14 -8.811838 -2.526837 -6.286334 8 1687.075 89.86847 2.31E-14 -8.780871 -1.628973 -5.907021 9 1740.065 76.27391 2.61E-14 -8.697464 -0.678669 -5.475269

10 1801.802 85.12250* 2.78E-14 -8.680322 0.205370 -5.109781 11 1862.552 80.07827 3.02E-14 -8.655693 1.096895 -4.736807 12 1914.969 65.91835 3.54E-14 -8.567943 2.051542 -4.300711

Notes: 1) The list of endogenous (or exogenous) variables is IPI CPI CBY CALL M3 DBR WON, and

FEH (or constant term and D97); 2) FPE, AIC, SC and HQ are denoted as final prediction error, Akaike information criterion, Schwarz information criterion, and Hannan-Quinn information criterion, respectively; 3) LR is a sequential modified LR test statistic (each test at 5% level);

4) An asterisk (*) indicates an optimum lag selected by the LR, FPE, AIC, SC, and HQ criteria.

46


Variable Coefficient t-Statistic

Constant 0.399064 1.9371

M3 0.650609 27.7135

CPI -1.169831 -9.9012

FEH 0.142500 8.4792

CALL -0.008252 -4.0265

CBY 0.004678 2.2014

DBR 0.004125 0.2059

WON -3.38E-06 -0.1136

Adjusted R2 = 0.9915, S.E. of regression = 0.0537, S.S.R. = 0.7382, Log likelihood = 401.4865 D-W stat = 0.2387, Akaike info criterion = -2.9810, Schwarz criterion = -2.8726

F-stat. = 4355.281, Prob (F-stat.) = 0.0000

Notes: 1) Dependent variable is IPI;

2) The estimation result is based on the OLS.

47

Table 13: Estimation Result of a Standard VAR Model (1981:01-2002:12)

Dependent Variables Independent Variables IPI CPI M3 CALL CBY DBR WON FEH

IPI(-1) 0.678944 -0.005075 -0.010040 0.642853 -0.294629 -0.442974 -204.4793 -0.152961 (0.06637) (0.01514) (0.02435) (3.19151) (2.65916) (0.49590) (81.4983) (0.20420)

IPI(-2) 0.114509 0.026086 0.036632 0.591811 2.737252 0.780517 46.75494 0.102136 (0.08045) (0.01835) (0.02951) (3.86870) (3.22340) (0.60112) (98.7910) (0.24753)

IPI(-3) 0.057482 0.004655 -0.046319 3.161411 -0.284658 0.159061 56.28847 -0.622430 (0.08050) (0.01836) (0.02953) (3.87111) (3.22541) (0.60150) (98.8526) (0.24768)

IPI(-4) 0.003493 -0.028550 0.036735 -4.314347 -0.807033 -0.478800 107.0434 0.692557 (0.06320) (0.01441) (0.02318) (3.03892) (2.53203) (0.47219) (77.6018) (0.19444)

CPI(-1) 0.130508 1.209238 -0.207087 -5.744344 -12.18905 -1.295938 -450.9494 -0.407614 (0.27423) (0.06254) (0.10060) (13.1866) (10.9870) (2.04895) (336.732) (0.84370)

CPI(-2) 0.041134 -0.417753 -0.099462 26.79667 19.27160 -0.704744 62.91653 1.895088 (0.41182) (0.09391) (0.15107) (19.8027) (16.4996) (3.07698) (505.682) (1.26702)

CPI(-3) -0.220401 0.243780 0.439264 3.905378 18.66226 3.300434 340.4131 -0.937041 (0.40349) (0.09201) (0.14801) (19.4021) (16.1658) (3.01472) (495.451) (1.24139)

CPI(-4) -0.090498 -0.071284 -0.151498 -22.77383 -24.41535 -1.124516 89.49340 -0.331707 (0.25673) (0.05855) (0.09418) (12.3452) (10.2860) (1.91822) (315.247) (0.78987)

M3(-1) 0.123905 0.039476 0.898181 -12.78939 -1.046094 0.083195 464.8988 -0.886610 (0.17060) (0.03891) (0.06258) (8.20370) (6.83531) (1.27470) (209.489) (0.52489)

M3(-2) -0.073490 -0.089628 0.148659 20.99277 0.992409 -1.790104 -771.4800 0.957217 (0.22467) (0.05123) (0.08242) (10.8033) (9.00132) (1.67864) (275.873) (0.69122)

M3(-3) -0.080023 0.146243 0.165011 -14.09162 -11.17108 0.637583 115.4867 0.208101 (0.22788) (0.05197) (0.08359) (10.9577) (9.12995) (1.70263) (279.816) (0.70110)

M3(-4) 0.116879 -0.086798 -0.217255 5.913259 10.61140 1.076821 185.2566 -0.306557 (0.17022) (0.03882) (0.06244) (8.18533) (6.82001) (1.27185) (209.020) (0.52371)

CALL(-1) -0.002883 -0.000160 0.001168 0.824014 -0.026862 -0.023712 0.745573 -0.003530 (0.00158) (0.00036) (0.00058) (0.07592) (0.06326) (0.01180) (1.93870) (0.00486)

CALL(-2) 7.52E-05 0.000113 -0.000760 -0.065578 0.074771 0.019933 -2.565077 0.006926 (0.00205) (0.00047) (0.00075) (0.09840) (0.08199) (0.01529) (2.51285) (0.00630)

CALL(-3) 0.001677 0.000553 0.000307 0.124267 0.054801 0.011096 0.812324 -0.014309 (0.00202) (0.00046) (0.00074) (0.09700) (0.08082) (0.01507) (2.47703) (0.00621)

CALL(-4) -0.001617 -0.000662 -5.25E-05 -0.026019 -0.021826 -0.002678 1.722119 0.005410 (0.00149) (0.00034) (0.00055) (0.07157) (0.05963) (0.01112) (1.82752) (0.00458)

CBY(-1) 0.003914 -0.000282 -9.57E-05 0.109239 1.103001 -0.029372 -0.356488 0.014119 (0.00215) (0.00049) (0.00079) (0.10343) (0.08618) (0.01607) (2.64122) (0.00662)

CBY(-2) -0.000890 -8.89E-05 0.000647 -0.153750 -0.341580 0.024750 3.624108 -0.004577 (0.00312) (0.00071) (0.00115) (0.15025) (0.12518) (0.02335) (3.83665) (0.00961)

CBY(-3) -0.003099 0.000828 -0.002169 -0.010468 0.135243 0.003507 -10.19656 0.000409 (0.00298) (0.00068) (0.00109) (0.14315) (0.11927) (0.02224) (3.65541) (0.00916)

48

(Continue Table 13)

Dependent Variables Independent Variables IPI CPI M3 CALL CBY DBR WON FEH CBY(-4) 0.000805 8.11E-05 0.001557 0.052340 -0.076415 -0.005197 5.133434 -0.004443

(0.00198) (0.00045) (0.00073) (0.09544) (0.07952) (0.01483) (2.43725) (0.00611)

DBR(-1) -0.008285 -0.000213 0.000215 0.228706 -0.784545 0.426980 43.06144 0.003544 (0.01015) (0.00231) (0.00372) (0.48794) (0.40655) (0.07582) (12.4601) (0.03122)

DBR(-2) 0.010091 0.004364 -0.001121 -0.848969 -0.022699 0.091517 -4.296661 -0.004191 (0.01134) (0.00259) (0.00416) (0.54544) (0.45446) (0.08475) (13.9283) (0.03490)

DBR(-3) 0.006858 -0.007999 0.005210 -0.085079 0.083055 -0.214074 -57.23944 0.028326 (0.01098) (0.00250) (0.00403) (0.52794) (0.43988) (0.08203) (13.4816) (0.03378)

DBR(-4) -0.008858 0.005752 -0.008259 0.467702 0.256721 0.231069 42.91536 0.008194 (0.01016) (0.00232) (0.00373) (0.48848) (0.40700) (0.07590) (12.4739) (0.03125)

WON(-1) -8.81E-05 4.99E-05 -3.97E-05 0.010762 0.005681 0.001844 1.210215 -0.000419 (5.5E-05) (1.2E-05) (2.0E-05) (0.00263) (0.00220) (0.00041) (0.06728) (0.00017)

WON(-2) 3.31E-05 -4.08E-05 7.54E-05 -0.010003 -0.012450 -0.001879 -0.527183 0.000398 (7.5E-05) (1.7E-05) (2.7E-05) (0.00360) (0.00300) (0.00056) (0.09187) (0.00023)

WON(-3) 5.45E-05 -2.10E-07 -3.49E-05 0.000353 0.006154 0.000199 0.343981 4.32E-05 (7.3E-05) (1.7E-05) (2.7E-05) (0.00351) (0.00292) (0.00054) (0.08954) (0.00022)

WON(-4) 9.25E-06 -1.25E-05 3.53E-06 -0.005535 -0.002854 -0.000506 -0.168947 0.000112 (5.2E-05) (1.2E-05) (1.9E-05) (0.00252) (0.00210) (0.00039) (0.06426) (0.00016)

FEH(-1) -0.022724 -0.018597 0.004271 -2.228040 -2.333347 -0.256705 -75.87615 0.972586 (0.02122) (0.00484) (0.00778) (1.02025) (0.85007) (0.15853) (26.0530) (0.06528)

FEH(-2) 0.039662 0.012688 -0.001691 0.713244 1.827208 0.281995 6.790883 -0.148335 (0.02901) (0.00661) (0.01064) (1.39482) (1.16216) (0.21673) (35.6181) (0.08924)

FEH(-3) 0.016974 0.011246 0.001766 -0.044395 -0.292921 -0.130169 7.233086 0.157002 (0.02896) (0.00660) (0.01062) (1.39236) (1.16011) (0.21635) (35.5552) (0.08909)

FEH(-4) -0.017694 -0.002532 -0.002916 0.612855 -0.040883 0.004915 45.86303 -0.033818 (0.02177) (0.00497) (0.00799) (1.04703) (0.87239) (0.16269) (26.7370) (0.06699)

C 0.051816 0.039675 0.081619 -1.877317 3.506790 -0.208456 54.21634 -0.600249 (0.09164) (0.02090) (0.03362) (4.40650) (3.67149) (0.68469) (112.524) (0.28194)

D97 -0.006655 0.001397 -0.003617 1.493799 1.792134 0.284278 63.35566 -0.050110 (0.00984) (0.00224) (0.00361) (0.47313) (0.39421) (0.07351) (12.0817) (0.03027)

Adj. R-squared 0.998791 0.999796 0.999966 0.948850 0.961598 0.562898 0.986511 0.995613 Sum sq. resids 0.093726 0.004874 0.012613 216.7219 150.4527 5.232404 141321.2 0.887196 S.E. equation 0.020187 0.004604 0.007405 0.970705 0.808791 0.150830 24.78790 0.062108 F-statistic 6585.583 39129.65 231984.1 148.8397 200.5612 11.26334 583.8734 1809.685 Log likelihood 673.9193 1064.162 938.6694 -348.5517 -300.3762 142.9826 -1203.935 377.2245 Akaike AIC -4.847874 -7.804255 -6.853556 2.898119 2.533153 -0.825626 9.378294 -2.600186 Schwarz SC -4.387335 -7.343716 -6.393018 3.358658 2.993692 -0.365087 9.838833 -2.139647

Log Likelihood (d.f. adjusted) = 1313.367, Akaike Information Criteria = -7.889142, Schwarz Criteria = -4.204830 Note: Figures in parentheses are denoted as a standard error.

49

Table 14: Empirical Result in Contemporaneous Causal Relationships for SVARact

TETRAD II - Version 1.2 for DOS by Peter Spirtes, Richard Scheines, Christopher Meek, and Clark Glymour Copyright (C) 1994 by Lawrence Erlbaum Associates Output file: covact.out Data file: covact.txt Parameters: Sample Size: 264 Continuous Data Covariance Matrix ipi cpi m3 call cby dbr won feh 0.3358 0.1820 0.1037 0.7238 0.4022 1.5902 -1.1470 -0.5410 -2.1342 18.3518 -1.5389 -0.7970 -3.1577 15.3425 16.9693 0.0528 0.0342 0.1230 -0.0401 -0.0971 0.0518 79.7786 48.2383 175.1281 -432.5477 -486.7653 28.9904 45379.7000 0.5112 0.2871 1.1013 -2.1967 -2.6032 0.0899 147.4467 0.8759 Correlation Matrix ipi cpi m3 call cby dbr won feh 1.0000 0.9755 1.0000 0.9904 0.9906 1.0000 -0.4620 -0.3922 -0.3951 1.0000 -0.6447 -0.6009 -0.6079 0.8694 1.0000 0.4001 0.4672 0.4285 -0.0411 -0.1035 1.0000 0.6462 0.7033 0.6519 -0.4740 -0.5547 0.5976 1.0000 0.9426 0.9529 0.9331 -0.5479 -0.6752 0.4221 0.7395 1.0000 P-value for Correlations ipi cpi m3 call cby dbr won feh 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.5069 0.0936 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Significance: 0.0500 Settime: Unbounded

50

(Continue Table 14) ------------------------------------------------------ List of vanishing (partial) correlations that made TETRAD remove adjacencies. Corr. : Sample (Partial) Correlation Prob. : Probability that the absolute value of the sample (partial) correlation exceeds the observed value, on the assumption of zero (partial) correlation in the population, assuming a multinormal distribution. Edge (Partial) Removed Correlation Corr. Prob. ------- ----------- ----- ----- call -- dbr rho(call dbr) -0.0411 0.5069 cby -- dbr rho(cby dbr) -0.1035 0.0936 ipi -- cby rho(ipi cby . feh) -0.0334 0.5906 m3 -- cby rho(m3 cby . cpi) -0.1152 0.0620 cpi -- cby rho(cpi cby . m3) 0.0112 0.8565 cby -- won rho(cby won . feh) -0.1115 0.0712 call -- feh rho(call feh . cby) 0.1074 0.0825 call -- won rho(call won . cby) 0.0201 0.7455 m3 -- call rho(m3 call . cpi) -0.0518 0.4037 cpi -- call rho(cpi call . m3) -0.0071 0.9082 ipi -- dbr rho(ipi dbr . feh) 0.0076 0.9025 dbr -- feh rho(dbr feh . won) -0.0369 0.5521 m3 -- dbr rho(m3 dbr . feh) 0.1064 0.0851 cpi -- dbr rho(cpi dbr . won) 0.0822 0.1846 ipi -- won rho(ipi won . m3) 0.0054 0.9300 m3 -- won rho(m3 won . ipi) 0.1125 0.0687 cpi -- won rho(cpi won . feh) -0.0070 0.9095 m3 -- feh rho(m3 feh . ipi) -0.0103 0.8679 ipi -- feh rho(ipi feh . m3 call) 0.0976 0.1156 #: no orientation consistent with assumptions Significance Level = 0.0500 /Pattern ipi <> cpi m3 -> ipi ipi <> call m3 -> cpi cpi <> feh cby -> call cby -> feh won -> dbr feh -> won

51


Log likelihood = 1248.490 LR test for over-identification:

Chi-square (16) = 129.7527, Probability = 0.0000 Estimated A matrix:

1.000000 0.073989 -0.121239 -0.000343 0.000000 0.000000 0.000000 0.000000 -0.961152 1.000000 -5.726301 0.000000 0.000000 0.000000 0.000000 13.94815 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 5.627670 0.000000 0.000000 1.000000 -0.573113 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 -0.001230 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 81.06779 0.000000 549984.9 0.000000 0.000000 -395.6446 0.000000 0.000000 1.000000

Estimated B matrix: 0.020201 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.864962 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.007405 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.846755 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.808791 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.147717 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 24.27116 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2511.601

Notes: 1) This result is obtained by after 500 iterations and 1e-5 convergent levels;

2) The log-likelihood is maximized by the Marquardt algorithm that modifies the Gauss-Newton algorithm in exactly the same manner as quadratic hill climbing modifies the Newton- Raphson method (by adding a correction matrix (or ridge factor) to the Hessian approximation);

3) The number of degree of freedom in 2χ is equal to the number of over-identifying restrictions (28-12=16).

52

A-3. Figures Figure 1: Mechanism of IMF’s Tightening Monetary Policy

(1) Acceralating Firm’s Restructuring Reducing Equipment Investment (I ↓) Diminishing Demand for the Import of

New Equipment (IM↓)

Improving Current Account (EX-IM)↑

Tightening Monetary Policy (M↓)

Leading to Hyper- Interest Rates (r↑)

(2) Preventing Rapid Capital Outflows Accerating Capital Inflows

Improvement Balance of Payments

Stabilizing Exchange Rate


(a) Alternative

Lower Interest Rates →

Recapitalize and Restructuring Banking

Sectors → Exchange Rates Ease → Real Economy

Stabilizes

↓

Recovery ← Currencies and Asset Prices Stabilize ← Confidence Returns

(b) IMF

Tight

Monetary and Fiscal

Policies

→ Rapid External Adjustment → Confidence Returns → Banking Sector

Crisis Lessens

↓

Recovery ← Interest Rates Ease ← Exchange Rates Stabilize

53

Figure 3: Trend of Money Supply Instruments

-.3

-.2

-.1

.0

.1

.2

.3

.4

92 93 94 95 96 97 98 99 00 01 02

Notes: 1) The blue, red and green lines are indicated M0, M2, and M3, respectively; 2) The definitions of M0, M1, M2, and M3 are respectively as follows:

M0 = Bank Notes & Coins Issued (excludes Commemorative Notes & Coins) + Reserve Deposits;

M1 = Currency in Circulation + Demand Deposits & Savings Deposits with Transferability; M2 = M1 + Periodical Time Deposits & Installment Savings + Marketable Instruments (CD

+ RP + Cover Bills etc.) + Yield-Based Dividend Instruments (Money in Trust, Beneficial Certificates, etc.) + Financial Debentures + Others (Securities Investment Savings at Investment Trust Companies, Bills Issued by Merchant Banking Corporations, etc.) Financial Instruments with a Maturity of More Than Two Years are Excluded.

M3 = Currency in circulation + Bank & Non-Bank Financial Corporations deposits + Debentures issued + Commercial bills sold + CD + RP + Cover Bills. (Includes Cover bills since Nov. 1989.)×M1 and M2 include monetary liabilities issued by Depository Corporations (Central Bank, Commercials Banks Specialized Banks, Export-Import bank, Merchant Banking Corporations, Investment Trust Management Companies, Trust Accounts of Banks, Mutual Savings Banks, Community Credit Cooperatives, Credit Unions, Mutual Credits, Postal Savings) and M3 includes monetary liabilities issued by the Korea Securities Finance Corporation and Life Insurance Companies as well as depository corporations.

4) The light yellow shading area indicates the period of IMF program (1997:12-2000:12) in Korea.

54

Figure 4: Trends of Major Macroeconomic Variables before and after Five Years around the Crisis

0

5

10

15

20

25

30

92 93 94 95 96 97 98 99 00 01 02

CALL

4

8

12

16

20

24

28

92 93 94 95 96 97 98 99 00 01 02

CBY

.00

.02

.04

.06

.08

.10

92 93 94 95 96 97 98 99 00 01 02

CPI

0.0

0.4

0.8

1.2

1.6

2.0

2.4

92 93 94 95 96 97 98 99 00 01 02

DBR

0

20

40

60

80

100

120

140

92 93 94 95 96 97 98 99 00 01 02

FEH

-.2

-.1

.0

.1

.2

.3

.4

92 93 94 95 96 97 98 99 00 01 02

IPI

.04

.08

.12

.16

.20

.24

92 93 94 95 96 97 98 99 00 01 02

M3

600

800

1000

1200

1400

1600

1800

92 93 94 95 96 97 98 99 00 01 02

WON

Notes: 1) The light yellow shading intervals indicate the period of 1997:08-1998:08;

2) The light green shading intervals indicate the periods of 1999:02-2001:05 for CPI, 1999:07-1999:11 and 2000:06-2001:12 for DBR, 1999:07-2001:07 for IPI, 1998:09-2000:02 for M3, and 2000:08-2001:04 for WON;

3) Percentage changes in CPI, IPI, and M3 are the same month over the previous year.

55

Figure 5: Impulse Response by the Standard VAR Model (1981:01-2002:12)

-.008

-.006

-.004

-.002

.000

.002

.004

5 10 15 20 25 30 35 40 45

Response of IPI to CALL

-.002

-.001

.000

.001

.002

.003

5 10 15 20 25 30 35 40 45

Response of CPI to CALL

-.004

-.002

.000

.002

.004

.006

5 10 15 20 25 30 35 40 45

Response of M3 to CALL

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

5 10 15 20 25 30 35 40 45

Response of CALL to CALL

-.2

-.1

.0

.1

.2

.3

.4

.5

.6

5 10 15 20 25 30 35 40 45

Response of CBY to CALL

-.04

-.03

-.02

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35 40 45

Response of DBR to CALL

-8

-4

0

4

8

12

16

5 10 15 20 25 30 35 40 45

Response of WON to CALL

-.02

-.01

.00

.01

.02

5 10 15 20 25 30 35 40 45

Response of FEH to CALL

Response to Cholesky One S.D. Innovations ± 2 S.E.

Notes: 1) The dotted red lines are S.E. bands for the responses calculated by the Monte Carlo simulation after 1000 iterations;

2) The ordering of variables is IPI, CPI, M3, CALL, CBY, DBR, WON and FEH.

56


Notes: 1) The variable of IPI, CPI, CALL and FEH takes a natural log into the original data; 2) This causal diagram is based on Tetrad III at a 5% significant level

(See Table 14 in appendix); 3) If variable X is a direct cause of variable Y , it can be expressed as X Y→ ;

4) If variable X has a feedback causal relationship with variable Y , it can be expressed as X Y↔ .

CALLCBY IPI DBR

FEHWON CPI M3

57

Figure 7: Impulse Response by the SVARact Model (1981:01-2002:12)

-.008

-.004

.000

.004

5 10 15 20 25 30 35 40 45


-.002

-.001

.000

.001

.002

.003

5 10 15 20 25 30 35 40 45


-.002

-.001

.000

.001

.002

.003

.004

.005

.006

5 10 15 20 25 30 35 40 45


-0.2

0.0

0.2

0.4

0.6

0.8

1.0

5 10 15 20 25 30 35 40 45


-.2

-.1

.0

.1

.2

.3

.4

.5

5 10 15 20 25 30 35 40 45


-.05

-.04

-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35 40 45


-12

-8

-4

0

4

8

12

5 10 15 20 25 30 35 40 45


-.02

-.01

.00

.01

5 10 15 20 25 30 35 40 45


Response to Structural One S.D. Innovations ± 2 S.E.

Notes: 1) The dotted red lines are S.E. bands for the responses; 2) The ordering of impulse response is IPI, CPI, M3 CALL CBY, DBR WON and FEH.

58

Figure 8: Comparison Impulse Responses by the VAR and SVARact Models

-.005

-.004

-.003

-.002

-.001

.000

.001

5 10 15 20 25 30 35 40 45-.0002

.0000

.0002

.0004

.0006

.0008

.0010

.0012

5 10 15 20 25 30 35 40 45.0000

.0005

.0010

.0015

.0020

.0025

.0030

5 10 15 20 25 30 35 40 45

0.0

0.2

0.4

0.6

0.8

1.0

5 10 15 20 25 30 35 40 45-.1

.0

.1

.2

.3

.4

.5

5 10 15 20 25 30 35 40 45-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35 40 45

-2

0

2

4

6

8

10

5 10 15 20 25 30 35 40 45-.010

-.008

-.006

-.004

-.002

.000

.002

.004

5 10 15 20 25 30 35 40 45

Reponse to One S.D. Innovations

Response of IPI to CALL Response of CPI to CALL Response of M3 to CALL

Response of CALL to CALL Response of CBY to CALL Response of DBR to CALL

Response of WON to CALL Response of FEH to CALL

Notes: 1) The red and blue lines are impulse response based on the SVARact and VAR models, respectively;

2) The ordering of impulse response is IPI, CPI, M3 CALL CBY, DBR WON and FEH.

59

Figure 9: Counterfactual Experiment against IMF’s Monetary Policy

-.00004

-.00003

-.00002

-.00001

.00000

.00001

5 10 15 20 25 30 35 40 45


.000000

.000005

.000010

.000015

.000020

.000025

5 10 15 20 25 30 35 40 45


-.00004

-.00003

-.00002

-.00001

.00000

.00001

.00002

.00003

5 10 15 20 25 30 35 40 45


.000

.002

.004

.006

.008

.010

.012

5 10 15 20 25 30 35 40 45


-.002

.000

.002

.004

.006

.008

.010

.012

5 10 15 20 25 30 35 40 45


-.00015

-.00010

-.00005

.00000

.00005

.00010

.00015

5 10 15 20 25 30 35 40 45


-.04

.00

.04

.08

.12

.16

5 10 15 20 25 30 35 40 45


.00000

.00002

.00004

.00006

.00008

.00010

.00012

.00014

5 10 15 20 25 30 35 40 45


Note: The blue and red lines indicate baseline and counterfactual IRs, respectively.

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Is the IMF's Tightening Monetary Policy Right for the Currency...

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