Wiedmann money, stock prices and central banks; a cointegrated VAR analysis (2011)

.155 after allowing for current effects standard deviations on diagonal ... .177 allowing for current effects standard deviations on diagonal ... .311 weak exogeneity imposed on real mon

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Contributions to Economics

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This book presents the results of my doctoral study It would not have been ble without the encouragement, guidance and support of my dissertation supervisorProfessor Dr Ansgar Belke I would also like to thank him for all the lively dis-cussions related to the thesis and to current events Our work together has alwaysbeen a pleasure I am also grateful to Professor Dr Gerhard Wagenhals, who acted

possi-as my secondary advisor and Professor Dr Hans-Peter Burghof for serving on myPhD committee

I am indebted to Professor Dr Katarina Juselius and Professor Dr SorenJohansen for hosting the Summer School in Econometrics at the University ofCopenhagen These three very intense weeks enabled me to apply the cointe-grated VAR model to my data in a meaningful way and have buttressed myresults Thank you again, Katarina, for your enduring patience in responding to

my follow-up questions, even long after the course had ended

My heartfelt gratitude also goes out to my friend Denise Möbius for lending

me her organizational skills, which included, among others, the tedious but sary tasks of creating the bibliography and ensuring proper formatting You were aninvaluable help I also owe thanks to my friend Alexander Krieg for insightful dis-cussions, Latex tutoring and providing the necessary distractions during the course

neces-of this project

On a more personal level, I wish to thank my parents for their support Your beingthere makes everything so much easier Most importantly, I want to thank Margaritafor improving the flow of my thesis and, especially, for loving me and taking care

of our little family You have enabled me to accomplish this Thank you

Lastly, I wish to thank all of those who have supported me in any respect duringthe completion of this project “No man is an island unto himself”, John Donne(1624)

October 2010

v

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1 Introduction 1

1.1 Context, Motivation and Objectives 1

1.2 Structure 5

2 Previous Research 9

2.1 Money and Stock Prices 9

2.1.1 Historical Overview 9

2.1.2 Recent Research 12

2.1.3 Research of Money and Stock Prices in Cointegrated VAR Models on a National Level 14

2.2 Academic Void 16

3 Money and Stock Prices: Economic Theory 19

3.1 Chapter Overview 19

3.2 Effects of Money on Stock Prices 20

3.2.1 Effects Initiated by Changes in the Quantity of Money 20

3.2.2 Effects Initiated by Changes in the Price of Money 23

3.2.3 Effects Initiated by Changes in Either Quantity or Price of Money 25

3.3 Effects of Stock Prices on Money Demand 29

3.3.1 Money Demand 29

3.3.2 Money Demand Augmented with Stock Prices 31

3.4 Conclusion 32

4 Monetary Liquidity and International Capital Flows 33

4.2 Monetary Liquidity Versus Market Liquidity 34

4.3 The Connection Between Money Stock and Interest Rates 34

4.4 Monetary Aggregates 36

4.4.1 Monetary Liquidity Creation 36

vii

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4.4.2 Narrow Versus Broad Money 38

4.4.3 Total Liquidity Versus Excess Liquidity 40

4.5 Interest Rates 41

4.6 National Versus Global Focus 43

4.6.1 International Economic and Financial Integration 43

4.6.2 Aggregation Issues and Importance of Country-Level Analysis 45

4.6.3 International Capital Flows 46

4.7 Conclusion 53

5 Empirical Analysis: General Remarks 55

5.1 Econometric Approach: The Cointegrated VAR Framework 55

5.1.1 Methodological Motivation 55

5.1.2 The Cointegrated VAR Model 57

5.2 Introduction to Potential Long-Run Relations Between the Economic Variables 61

5.2.1 Necessary Economic Variables 61

5.2.2 Potential Long-Run Relations Between the Economic Variables 62

5.2.3 Summary of Potential Cointegration Relations 70

6 Empirical Analysis by Country 75

6.2 United States of America: Quarterly Data 76

6.2.1 Model Specification 76

6.2.2 Identification of the Long-Run Structure 89

6.2.3 Short-Run Dynamics .100

6.2.4 The Long-Run Impact of the Common Trends .104

6.2.5 Conclusion 106

6.3 Euro Area: Monthly Data .109

6.3.2 Identification of the Long-Run Structure .117

6.4 Japan: Quarterly Data .135

6.5 United Kingdom: Quarterly Data .160

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Contents ix

6.6 Australia: Quarterly Data .182

6.7 South Korea: Quarterly Data .205

6.8 Thailand: Quarterly Data .230

6.9 Brazil: Quarterly Data .253

7 Summary of Empirical Analysis and Policy Implications 275

7.1 Empirical Findings of Main Hypotheses: Cross-Country Comparisons 275

7.2 Policy Implications .284

7.2.1 Monetary Policy: Current State .284

7.2.2 Monetary Policy and Asset Prices: Recommendations 288

8 Concluding Remarks .297

A Details on the Calculation of the Capital Flows Time Series .301

B Additional Information of Empirical Analysis .305

B.1 United States of America: Quarterly Data .305

B.1.1 Data Sources (Table B.1) 305

B.1.2 Graphs of the Cointegrating Relations of the Unrestricted Model (Fig.B.1) 307

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B.1.3 Forward and Backward Recursive Tests

for Parameter Constancy of the Unidentified

B.3,B.4, andB.5) 308

of the Identified Long-Run Structure (Fig.B.6) 312

Long-Run Structure (Fig.B.7,B.8,B.9,B.10,

B.11,B.12,B.13, andB.14) 313

Exogeneity Imposed (TableB.4) 321

with Weak Exogeneity Imposed (TableB.5) 322

of the Unrestricted Model (Fig.B.15) 324

B.17,B.18, andB.19) .325

Structure 328

Long-Run Structure (Figs.B.21,B.22,B.23,

B.24,B.25,B.26,B.27,B.28,B.29, andB.30) 331

B.33,B.34, andB.35) .345

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Contents xi

Structure 348

B.40,B.41,B.42,B.43, andB.44) .351

B.47,B.48, andB.49) .361

Structure 364

B.54,B.55, andB.56) .366

B.59,B.60, andB.61) .375

Structure 378

B.66,B.67, andB.68) .380

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B.6.2 Graphs of the Cointegrating Relations

B.71,B.72, andB.73) .388

Structure 391

B.78,B.79,B.80,B.81, andB.82) .394

B.85,B.86, andB.87) .404

Structure 407

B.92,B.93, andB.94) .409

B.97,B.98, andB.99) .418

Structure 421

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Contents xiii

Identified Long-Run Structure (Fig.B.100) .422

B.8.7 Tests for Parameter Constancy of the Identified Long-Run Structure (Figs.B.101,B.102, B.103,B.104,B.105, andB.106) 423

C Impact of Macro Variables on Each Other: Summary Tables .429

References .439

Index .453

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List of Figures

statistics (forward, base sample 1984:02 to 1998:04,

depicted left; backward, base sample 2008:03 to 1994:01,

depicted right) 88

log-likelihood (forward, base sample 1984:02 to 1998:04,

depicted right) 89

depicted right) 116

depicted right) 117

depicted right) 143

depicted right) 144

xv

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6.11 UK quarterly data: recursively calculated trace test

depicted right) 166

depicted right) 167

depicted right) 189

depicted right) 190

test statistics (forward, base sample 1983:03 to 1997:04,

depicted right) 213

depicted right) 214

depicted right) 238

depicted right) 239

depicted right) 259

depicted right) 260

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List of Figures xvii

of the unrestricted model 307

(forward, base sample 1984:02 to 1998:04, depicted left;

backward, base sample 2008:03 to 1994:01, depicted right) 308

fluctuation test (forward, base sample 1984:02 to 1998:04,

depicted right) 308

constancy (forward, base sample 1984:02 to 1998:04,

depicted right) 309

a known ˇ (forward, base sample 1984:02 to 1998:04,

depicted right) 309

of the restricted model 312

cointegration relation (forward, base sample 1984:02 to

1998:04, depicted above; backward, base sample 2008:03

to 1994:01, depicted below) 313

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B.13 US quarterly data: recursively calculated ˛s of the fourth

i(forward, base sample 1999:03 to 2003:12, depicted

left; backward, base sample 2008:09 to 2003:12, depicted

right) 325

depicted right) 325

ˇ constancy (forward, base sample 1999:03 to 2003:12,

depicted right) 326

2003:12, depicted left; backward, base sample 2008:09 to

2003:12, depicted right) test of ˇtequals a known ˇ 326

first cointegration relation (forward, base sample 1999:03

to 2003:12, depicted above; backward, base sample

2008:09 to 2003:12, depicted below) 331

the second cointegration relation (forward, base sample

1999:03 to 2003:12, depicted above; backward, base

sample 2008:09 to 2003:12, depicted below) 333

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List of Figures xix

third cointegration relation (forward, base sample 1999:03

the fourth cointegration relation (forward, base sample

fifth cointegration relation (forward, base sample 1999:03

depicted right) 345

depicted right) 346

equals a known ˇ (forward, base sample 1984:01 to

1989:02, depicted right) .346

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B.37 Japan quarterly data: recursively calculated ˛s of the first

second cointegration relation (forward, base sample

fourth cointegration relation (forward, base sample

of the unrestricted model .360

depicted right) 361

depicted right) 362

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List of Figures xxi

a known ˇ (forward, base sample 1983:03 to 1995:04,

depicted right) 362

of the restricted model 365

left; backward, base sample 2008:03 to 1995:04, depicted right) .375

depicted right) 375

depicted right) 376

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B.61 Australia quarterly data: recursively calculated test of

eigenvalues i(forward, base sample 1983:03 to 1997:04,

depicted right) 388

eigenvalue fluctuation test (forward, base sample 1983:03

to 1997:04, depicted left; backward, base sample 2008:03

to 1996:01, depicted right) 388

constant ˇ (forward, base sample 1983:03 to 1997:04,

depicted right) 389

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List of Figures xxiii

of the first cointegration relation (forward, base sample

the third cointegration relation (forward, base sample

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B.85 Thailand quarterly data: recursively calculated eigenvalue

depicted right) 404

depicted right) 405

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List of Figures xxv

depicted right) 418

depicted right) 419

equals a known ˇ (forward, base sample 1995:03 to

1998:01, depicted right) .419B.100 Brazil quarterly data: the cointegration relations

B.101 Brazil quarterly data: recursively calculated ˛s of the first

to 1998:01, depicted below) 423B.102 Brazil quarterly data: recursively calculated ˇs of the first

to 1998:01, depicted below) 424B.103 Brazil quarterly data: recursively calculated ˛s of the

sample 2008:03 to 1998:01, depicted below) 425B.104 Brazil quarterly data: recursively calculated ˇs of the

sample 2008:03 to 1998:01, depicted below) 426B.105 Brazil quarterly data: recursively calculated ˛s of the third

to 1998:01, depicted below) 427B.106 Brazil quarterly data: recursively calculated ˇs of the third

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List of Tables

(p-values in brackets) 82

4 (t -values in brackets) 90

(t -values in brackets) 98

(standard deviations on diagonal) 102

xxvii

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6.20 Euro area monthly data: information to determine lag length .112

eigenvalue roots 115

(t -values in brackets) .125

of 4 (t -values in brackets) .144

in brackets) 146

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List of Tables xxix

after allowing for current effects (standard deviations on diagonal) .155

current effects (t -values in brackets) 156

in brackets) 157

3 (t -values in brackets) .168

allowing for current effects (standard deviations on diagonal) .178

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6.79 Australia quarterly data: multivariate misspecification tests

rank of 3 (t -values in brackets) 191

tests (p-values in brackets) 210

a rank of 4 (t -values in brackets) 215

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List of Tables xxxi

structure (t -values in brackets) 221

in brackets) 224

residuals (standard deviations on diagonal) 226

residuals after allowing for current effects (standard

deviations on diagonal) 226

for current effects (t -values in brackets) 227

rank of 3 (t -values in brackets) 240

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6.132 Thailand quarterly data: correlation of structural residuals

of 3 (t -values in brackets) .261

in brackets) 262

in brackets) 273

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List of Tables xxxiii

based on 4 cointegrating vectors (t -values in brackets) 310

long-run structure 311

weak exogeneity imposed on real money and the stock

market (t -values in brackets) 321

exogeneity imposed on real money and the stock market

…-matrices based on 5 cointegrating vectors (t -values in

brackets) 327

with weak exogeneity imposed on real money (t -values in

brackets) 341

weak exogeneity imposed on real money (t -values in

brackets) 342

brackets) 347

based on 4 cointegrating vectors (t -values in brackets) 363

weak exogeneity imposed on the stock market and the

bond rate (t -values in brackets) 372

exogeneity imposed on the stock market and the bond rate

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B.20 Australia quarterly data: the partitioned unrestricted

brackets) 377

brackets) 390

identified long-run structure 392

brackets) 406

with weak exogeneity imposed on real money (t -values in

brackets) 415

weak exogeneity imposed on real money (t -values in

brackets) 415

brackets) 420

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List of Abbreviations

xxxv

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OECD Organisation for Economic Co-operation and Development

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1.1 Context, Motivation and Objectives

Starting with the ‘Great Moderation’ in the mid-1980s, five phenomena have enced and characterized economic conditions and financial markets, especially indeveloped markets:

influ- Low and constant inflation rates.1

financial investment firms and ordinary people have to borrowing and foreignexchange.2

Massive increases in world trade, financial globalization and international capitalflows.3

Large asset price swings and an increased number of financial crises.4

Reduced output volatility.5

Many economic observers point to globalization and the resulting pricing-to-market

of companies to explain low inflation rates They hypothesize that, contrary toconventional theory, abundant liquidity in the system has not led to goods price

1 For an overview, see, for example, The Economist ( 2007 , p 4), which shows the reduced median and variation for inflation across 13 industrialized countries.

nominal gross domestic product (GDP) ‘only’ grew sixfold Accordingly, excess liquidity accounts for 65% of economic output.

26% for real GDP See also Hau and Rey ( 2004 , p 126), who find that gross cross-border flows based on bond and equity transactions in the United States of America (US) were equivalent to only 4% of GDP in 1975, 100% in the early 1990s and increased to 245% by 2000.

4 Bordo et al ( 2001 , pp 56–57) show that the chance of suffering a currency crisis, banking crisis

or twin crisis in a given year has more than doubled for the period 1973 to 1997 compared to during the Bretton Woods and the gold standard periods.

M Wiedmann, Money, Stock Prices and Central Banks, Contributions to Economics,

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2 1 Introductionincreases Instead it is the antecedent to excessive asset price rises and increasedvolatility, such as in housing, commodities and stocks (Rogoff 2006, p 2).

Price increases in real goods and services usually lead to reduced demand andsubstitution This is not true of asset prices For example, rising share prices areregarded as a sign of confidence and breed optimism Thus, ordinary people investmore money when prices go up and less when prices go down

Abundant liquidity can exacerbate this It is easier and cheaper for people, hedgefunds and companies to borrow under conditions of ample liquidity If portions

of these additional funds are invested, prices are pushed up further and optimismspreads Herding behavior and rational speculation are signs of this process Afterall, even if prices depart from justified long-run levels, it is still lucrative to bet onrising prices if stocks can be sold at a higher level before the market corrects itself.Thus, irrationally high levels on the stock market can result from rational specula-tion and people’s perception that they are smarter than others and able to get out ofthe market before it turns This type of thinking and rational speculation increasewith ample monetary liquidity, which, in turn, also increases market liquidity As aresult, there always seems to be a ready buyer

Additionally, confidence and optimism are also boosted because owners of assetsfeel richer when house or share prices increase This, in turn, results in increasedspending on goods and assets The former helps companies increase profits and,consequently, also leads to increases in share prices and the valuation of bonds,which justifies previous purchases ex post.6The number of defaults decreases andlenders want to lend more to participate in the upswing, thereby intensifying it.The same mechanisms apply once markets turn sour When prices decline, pre-vious overconfidence turns into paralyzing uncertainty and lenders demand thatborrowers hold more collateral At the same time, falling asset prices decreasethe amount of collateral, forcing borrowers to sell assets This drives prices downfurther

Whereas the above described play of optimism, confidence and asset pricesevolves independent of liquidity conditions, there is a strong case that high liquid-ity levels reinforce this process The procyclicality of credit markets influences thebusiness cycle, heightens stock market ups and exacerbates downs

In conclusion, rising asset prices, abundant credit and liquidity conditions, mism, confidence and rational speculation all feed into each other and amplify thenormal behavior of stock markets By this token, the same mechanisms apply in

opti-a downturn This reopti-asoning indicopti-ates opti-a long-run relopti-ationship between liquidity/

‘excess liquidity’ and stock market levels with a potential inclusion of economicactivity or other macro variables.7Four testable hypotheses can be derived from theabove discussion:

6 Farmer ( 2009 , pp 14–17) provides a methodological description of the self-fulfilling behavior of stock markets in the context of a labor market model.

only be the intensity that varies In addition, stock market behavior over the last 25 years is characterized by long boom and bust phases.

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