international stock market liquidity

INTERNATIONAL STOCK MARKET LIQUIDITY DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Christof W Stahel, M.A ***** The Ohio State University 2004 Dissertation Committee: Approved by Ren´ M Stulz, Adviser e Kewei Hou Ingrid M Werner Adviser Graduate Program in Business Administration ABSTRACT This dissertation contributes to the international asset pricing literature The research it presents in its two essays is related to papers that investigate commonalities in individual stock liquidity in the domestic US setting, to research that estimates risk premia related to liquidity risk in the US, and to articles that explore properties and determinants of market-wide liquidity in the US, while expanding the scope to an international setting The first essay shows that individual liquidity exhibits commonalities in monthly measures of individual stock liquidity within and across countries for a sample from Japan, the UK, and the US from 1980 to 2001 An asset pricing analysis suggests that expected stock returns are cross-sectionally related to the sensitivity of returns to shocks in global liquidity in this sample and that global liquidity is a priced state variable in an international framework at the portfolio as well as at the individual stock level The second essay analyzes cross-regional and time-series properties of weekly market-wide liquidity measures from 1990 to 2002 for five regional aggregates: developed Asia, North America, Europe, emerging Asia, and emerging America The aggregates are calculated from a sample that contains 39 developed and emerging countries The results suggest that liquidity shocks are contemporaneously correlated and dynamically spread across regions However, there is only week evidence ii that liquidity affects returns in this sample An investigation of determinants of liquidity indicates that market-wide returns, market-wide averages of individual stock volatilities, and world net bond flows are fundamental drivers of market-wide liquidity There is little evidence that equity fund flows and interest rates consistently affect liquidity in the sample Even though changes in liquidity can to some extent be explained by returns and other determinants, shocks to liquidity continue to be contemporaneously correlated across markets But the empirical results from an application of extreme value theory offers evidence that extreme shocks to liquidity are asymmetrically correlated in the tail of the distribution In particular, it is mostly negative extreme liquidity shocks that are correlated between North America, Europe, and emerging America The overall conclusions from this dissertation are twofold First, changes in global liquidity constitutes an international risk factor, and financial assets with returns that are more sensitive to this factor reward investors with higher expected returns However, the contribution of liquidity risk to expected returns seems to be more relevant for developed markets Second, market-wide liquidity is contemporaneously and dynamically related across regions Furthermore, these relationships not simply reflect other variables that are related across markets but constitute a phenomenon by themselves iii This dissertation is dedicated to the ones I miss and love iv ACKNOWLEDGMENTS I would like to thank my advisor Ren´ Stulz and my committee members Kewei e Hou and Ingrid Werner for their guidance, insightful feedback, and encouragement that helped make this dissertation possible I also thank Tom Bates, Terry Campbell, Jeff Harris, Jean Helwege, David Hirshleifer, Roberto Ragozzino, and seminar participants at the University of Delaware, Drexel University, George Mason University, HEC Montreal, The Ohio State University, Queen’s University, and Texas Tech University for helpful comments and suggestions, and Laurie Pomerson for her help with too many versions of the manuscript v VITA June 25, 1964 Born Zărich, Switzerland u 1995 lic.oec.publ – University of Zărich, u Switzerland 1997 M.A Economics – The Ohio State University, USA PUBLICATIONS Research Publications Michel Peytrignet and Christof W Stahel, Stability of money demand in Switzerland: A comparison of the M2 and M3 cases, Empirical Economics, 23:437–454, 1998 FIELDS OF STUDY Major Field: Business Administration Concentration: Finance vi TABLE OF CONTENTS Page Abstract ii Dedication iv Acknowledgments v Vita vi List of Tables ix List of Figures xi Chapters: 1 1.1 1.2 1.3 1.4 Introduction Is there a Global Liquidity Factor? 11 2.1 2.2 19 24 24 27 32 33 38 39 2.3 International Asset Pricing Models Market Integration Capital Flows, Spillovers and Contagion Essay Summary Sample and Liquidity Measures Commonalities in Liquidity 2.2.1 Contemporaneous Variation 2.2.2 Sources of Commonalities Asset Pricing Implications 2.3.1 Liquidity Risk 2.3.2 Expected Returns and Trading Cost 2.3.3 Stock Level Fama-MacBeth Regressions vii 2.4 2.5 Alternative Sample Conclusion 41 41 Are liquidity Shocks correlated across Equity Markets? 43 3.1 48 51 52 56 61 65 Conclusions 67 Bibliography 70 3.2 3.3 3.4 3.5 Sample and Liquidity Measure 3.1.1 Sample Properties Dynamic Transmission of Liquidity Shocks Liquidity and Returns Correlation of Extreme Liquidity Shocks Conclusion Appendices: A Tables B Figures 108 viii 77 LIST OF TABLES Table Page A.1 Liquidity Measures 78 A.2 Commonality in Liquidity 80 A.3 Decomposition of Commonality 81 A.4 Variance Decomposition 82 A.5 Liquidity Risk Premium - country and industry portfolios 84 A.6 Liquidity Level and Return 85 A.7 Liquidity Risk Premium - size portfolios 86 A.8 Liquidity Risk Premium - country, industry, and size portfolios 87 A.9 Test of constant Risk Premiums 88 A.10 Risk Premium and Liquidity as an Asset Characteristic 89 A.11 Fama-MacBeth Risk Premiums 90 A.12 Commonalities and Decompositions - IPO 91 A.13 Variance Decomposition - IPO 92 A.14 Liquidity Risk Premium - IPO 93 A.15 Sample Summary 94 ix Asia Europe North America Emerging Asia Emerging America Constant 0.0003 (0.09) 0.0003 (0.15) 0.0002 (0.10) 0.0008 (0.25) 0.0005 (0.29) Asia -0.5620 (-13.84) -0.0217 (-0.97) A Lag -0.0633 (-2.49) 0.0129 (0.36) -0.0206 (-1.03) Europe -0.0040 (-0.04) -0.6200 (-11.30) -0.0247 (-0.40) -0.0494 (-0.57) -0.0443 (-0.90) North America 0.1789 (2.21) 0.1039 (2.33) -0.6550 (-12.96) 0.3734 (5.29) 0.0984 (2.47) Emerging Asia 0.0705 (1.50) 0.0289 (1.11) 0.0563 (1.92) -0.5621 (-13.67) 0.0327 (1.41) Emerging America -0.0979 (-1.01) -0.0207 (-0.39) -0.0340 (-0.56) -0.2326 (-2.75) -0.6107 (-12.77) Asia -0.3892 (-8.87) -0.0540 (-2.23) A Lag 0.0259 (0.94) -0.0579 (-1.51) -0.0296 (-1.37) Europe 0.2445 (2.23) -0.3629 (-5.98) -0.0267 (-0.39) -0.1983 (-2.07) 0.0133 (0.25) North America 0.0343 (0.36) -0.0374 (-0.71) -0.4206 (-7.05) 0.2412 (2.89) 0.0271 (0.57) Emerging Asia 0.0890 (1.77) 0.0091 (0.33) 0.0376 (1.20) -0.4366 (-9.96) 0.0061 (0.25) Emerging America -0.0268 (-0.25) 0.0215 (0.37) -0.0483 (-0.73) -0.0142 (-0.15) -0.3511 (-6.70) Asia -0.1982 (-4.92) -0.0425 (-1.91) A Lag 0.0273 (1.08) -0.0454 (-1.29) 0.0063 (0.32) Europe 0.1099 (1.11) -0.2143 (-3.93) -0.0435 (-0.70) -0.1046 (-1.21) 0.0413 (0.85) North America 0.1013 (1.26) -0.0024 (-0.05) -0.1953 (-3.88) 0.1416 (2.01) -0.0506 (-1.27) Emerging Asia 0.0704 (1.53) 0.0146 (0.58) 0.0141 (0.49) -0.2899 (-7.23) -0.0321 (-1.42) Emerging America 0.1219 (1.27) 0.0496 (0.93) -0.0149 (-0.25) 0.0439 (0.52) -0.0991 (-2.09) Adj R-squared 26.3% 25.9% 32.4% 32.0% 27.1% Table A.16: Table reports estimation results of a vector autoregressive system for the full sample of 624 weekly observations from 1/9/1990 to 1/2/2002 The lag length p = minimizes the Schwarz-Bayesian information criterion, BIC The estimated coefficients are in the first row with the corresponding t-statistics in parentheses below Bold and italic bold letters report significance at the 5% and the 10% level, respectively 96 Response Asia Asia Impulse Europe North America Emerging Asia Emerging America Europe 6.0681 [0.11] 6.6020 [0.09] 8.2570 [0.04] 4.2823 [0.23] 3.8891 [0.27] 12.0232 [0.01] 1.4027 [0.70] 1.4032 [0.70] North America Emerging Asia Emerging America 12.5448 [0.01] 0.5522 [0.91] 4.2590 [0.23] 4.5695 [0.21] 28.5114 [0.00] 3.1808 [0.36] 2.1442 [0.54] 10.2496 [0.02] 5.8121 [0.12] 3.8696 [0.28] 0.6021 [0.90] 10.3262 [0.02] Table A.17: Table reports Granger-Causality tests based on the VAR(3) model reported in table A.16 The first row reports the Wald-test statistic and the second row reports the corresponding p-value 97 Asia Asia Europe North America Emerging Asia Emerging America Europe 0.1731 [0.00] 0.1731 [0.00] 0.1009 [0.01] 0.2333 [0.00] 0.1853 [0.00] 0.6148 [0.00] 0.2912 [0.00] 0.5146 [0.00] North America Emerging Asia Emerging America 0.1009 [0.01] 0.6148 [0.00] 0.2333 [0.00] 0.2912 [0.00] 0.2614 [0.00] 0.1853 [0.00] 0.5146 [0.00] 0.3699 [0.00] 0.2432 [0.00] 0.2614 [0.00] 0.3699 [0.00] 0.2432 [0.00] Table A.18: Table reports estimated correlation matrix of residuals from the VAR(3) model reported in table A.16 The corresponding p-values for the hypothesis that the correlation is zero are in the brackets below 98 Panel A: Mean Equations Asia Europe North America Emerging Asia Emerging America Constant 0.0014 (0.24) 0.0003 (0.07) -0.0011 (-0.22) -0.0024 (-0.40) 0.0003 (0.10) Asia -0.5329 (-8.22) -0.0194 (-0.46) A Lag -0.0640 (-1.39) 0.0470 (0.80) -0.0036 (-0.10) Europe -0.0858 (-0.57) -0.5925 (-7.15) 0.0047 (0.05) -0.0618 (-0.48) -0.0564 (-0.81) North America 0.1480 (1.32) 0.0669 (0.88) -0.6610 (-7.80) 0.3627 (3.72) 0.0685 (1.20) Emerging Asia 0.0846 (1.33) 0.0176 (0.36) 0.0464 (0.95) -0.5708 (-8.47) 0.0491 (1.26) Emerging America -0.0234 (-0.19) -0.0333 (-0.37) -0.0639 (-0.63) -0.1851 (-1.56) -0.6093 (-8.41) Asia -0.3487 (-5.44) -0.0484 (-1.29) A Lag 0.0215 (0.55) -0.0145 (-0.22) -0.0166 (-0.46) Europe 0.1446 (1.04) -0.3558 (-3.70) -0.0159 (-0.14) -0.2414 (-1.84) -0.0089 (-0.11) North America 0.0175 (0.14) -0.0617 (-0.75) -0.4210 (-4.30) 0.2486 (2.28) -0.0021 (-0.03) Emerging Asia 0.0776 (1.14) -0.0027 (-0.05) 0.0244 (0.45) -0.4263 (-5.67) 0.0163 (0.42) Emerging America 0.0605 (0.42) 0.0458 (0.49) -0.0190 (-0.18) 0.0455 (0.34) -0.3141 (-3.98) Asia -0.1528 (-2.28) -0.0441 (-1.41) A Lag 0.0300 (0.88) -0.0241 (-0.43) 0.0076 (0.24) Europe 0.0671 (0.52) -0.2086 (-2.12) -0.0362 (-0.33) -0.1141 (-0.87) 0.0451 (0.61) North America 0.0840 (0.80) -0.0128 (-0.18) -0.1787 (-1.97) 0.1524 (1.57) -0.0704 (-1.21) Emerging Asia 0.0448 (0.60) 0.0129 (0.31) 0.0017 (0.03) -0.2898 (-4.56) -0.0234 (-0.79) Emerging America 0.1229 (1.08) 0.0327 (0.36) -0.0076 (-0.07) 0.0504 (0.38) -0.0974 (-1.37) Continued Table A.19: Table reports estimation results for a multivariate GARCH - BEKK(1,1) model for the sample of 624 weekly observations from 1/9/1990 to 1/2/2002 The estimated coefficients are in the first row and the corresponding t-statistics in parentheses below Panel A and B report the estimation results for the mean and variance equation, respectively, and Panel C reports Likelihood Ratio tests for the existence of meteor showers See text for further details 99 Table A.19 continued Panel B: Variance Equations ARCH Term A Asia 0.2505 (3.75) 0.0107 (0.23) -0.0012 (-0.02) 0.0225 (0.29) 0.0333 (0.81) Europe 0.0257 (0.16) 0.1725 (1.58) -0.0547 (-0.48) -0.0561 (-0.35) -0.0039 (-0.04) North America -0.0740 (-0.53) -0.0514 (-0.54) 0.1566 (1.72) 0.0250 (0.17) -0.0448 (-0.55) Emerging Asia -0.0364 (-0.39) 0.0142 (0.21) -0.0040 (-0.06) 0.3345 (2.74) -0.0244 (-0.52) Emerging America -0.0080 (-0.05) 0.0343 (0.31) 0.0895 (0.90) -0.0320 (-0.16) 0.2751 (3.15) Asia 0.8829 (12.63) 0.0273 (0.60) 0.0190 (0.45) 0.0238 (0.21) -0.0097 (-0.26) Europe -0.0849 (-0.55) 0.9074 (11.89) -0.0453 (-0.71) -0.0453 (-0.23) -0.0524 (-0.76) North America 0.0014 (0.01) 0.1170 (1.11) 0.9721 (11.10) 0.1495 (0.67) 0.0706 (0.83) Emerging Asia 0.0363 (0.41) -0.0313 (-0.54) 0.0117 (0.23) 0.8035 (5.87) 0.0074 (0.16) Emerging America 0.0639 (0.53) -0.0316 (-0.41) -0.0357 (-0.67) -0.0005 (-0.00) 0.9277 (16.75) GARCH Term B Panel C: Likelihood Ratio Tests 112.7925 [0.00] 127.8499 [0.00] 124.0348 [0.00] 118.1525 [0.00] Europe 115.9540 [0.00] 137.9742 [0.00] 115.3177 [0.00] 100.4178 [0.00] North America 116.9866 [0.00] 123.7880 [0.00] 118.5501 [0.00] 124.9365 [0.00] Emerging Asia 118.3014 [0.00] 115.5729 [0.00] 118.5349 [0.00] 116.7439 [0.00] Emerging America 148.2822 [0.00] 114.2453 [0.00] 85.2325 [0.00] 109.7952 [0.00] Asia 100 Table A.20: Table reports the estimation results for a dynamic simultaneous equations model for the full sample from 1/9/1990 to 1/2/2002 using an SUR framework The model contains five liquidity and five return equations and includes three lags of the endogenous variables The liquidity equations also contain the absolute return Additional determinants are the average individual stock volatility in the respective market, the net equity flow to the market, the world net bond flow, the change in the US TBill rate, and the change in the US Term spread The coefficients are in the first row and the corresponding t-statistic in parentheses below Bold and italic bold coefficients indicate significance at the 5% and the 10% level, respectively 101 Table A.20 continued ∆ Market Liquidity Equations Asia Europe North America Emerging Asia Emerging America Intercept -0.1338 (-10.01) -0.0456 (-7.22) -0.0599 (-5.36) -0.1170 (-10.04) Asia -0.5628 (-14.84) ∆ Market Liquidity – Lag -0.0230 -0.0527 0.0249 (-1.09) (-2.15) (0.76) -0.0468 (-7.70) Europe -0.0767 (-0.84) -0.6553 (-12.36) -0.0548 (-0.92) -0.1322 (-1.64) -0.0292 (-0.65) North America 0.1415 (1.92) 0.1058 (2.50) -0.6591 (-13.64) 0.3310 (5.04) 0.0779 (2.13) Emerging Asia 0.0476 (1.11) 0.0230 (0.93) 0.0467 (1.65) -0.5921 (-15.41) 0.0286 (1.34) Emerging America -0.0774 (-0.87) -0.0006 (-0.01) -0.0170 (-0.29) -0.1468 (-1.85) Asia -0.3625 (-8.83) ∆ Market Liquidity – Lag -0.0480 0.0364 -0.0286 (-2.10) (1.38) (-0.81) -0.6186 (-13.42) Europe 0.1625 (1.61) -0.3798 (-6.46) -0.0584 (-0.88) -0.2268 (-2.55) 0.0197 (0.39) North America 0.0234 (0.27) -0.0174 (-0.35) -0.3982 (-6.96) 0.2330 (3.02) 0.0293 (0.68) Emerging Asia 0.0462 (1.01) 0.0037 (0.14) 0.0307 (1.02) -0.4361 (-10.68) 0.0050 (0.22) Emerging America -0.0041 (-0.04) 0.0147 (0.26) -0.0473 (-0.74) 0.0257 (0.30) Asia -0.1732 (-4.62) ∆ Market Liquidity – Lag -0.0367 0.0335 -0.0187 (-1.74) (1.38) (-0.57) -0.3819 (-7.63) Europe 0.0428 (0.47) -0.1847 (-3.55) -0.0426 (-0.71) -0.1207 (-1.51) 0.0326 (0.73) North America 0.0998 (1.35) -0.0009 (-0.02) -0.1825 (-3.78) 0.1483 (2.26) -0.0264 (-0.72) Emerging Asia 0.0407 (0.97) 0.0198 (0.83) 0.0159 (0.58) -0.2638 (-7.08) -0.0273 (-1.31) Emerging America 0.1644 (1.88) 0.0177 (0.35) -0.0221 (-0.38) 0.0578 (0.74) -0.1287 (-2.89) -0.0183 (-1.00) -0.0240 (-1.21) 0.0164 (0.90) continued 102 Table A.20 continued ∆ Market Liquidity – Cross Equation and Exogenous Variables Asia Europe North America Emerging Asia Emerging America |Market Return| t-1 -0.3967 (-1.96) -0.1019 (-0.52) -0.0233 (-0.14) -0.2309 (-1.35) -0.7441 (-5.59) |Market Return| t-2 -0.7086 (-3.70) -0.3920 (-1.98) -0.1257 (-0.75) -0.4383 (-2.68) -0.3775 (-2.88) |Market Return| t-3 -0.7077 (-3.72) -0.8026 (-4.25) -0.0308 (-0.19) -0.5151 (-3.18) -0.1979 (-1.51) Market Return t-1 0.3897 (2.96) 0.3074 (2.22) 0.0961 (0.81) 0.1173 (1.06) 0.3298 (3.59) Market Return t-2 0.3208 (2.45) 0.1300 (0.90) 0.1927 (1.59) 0.1217 (1.10) -0.0428 (-0.47) Market Return t-3 -0.1288 (-1.01) 0.0463 (0.33) 0.1726 (1.41) 0.0082 (0.07) 0.0737 (0.82) avg Volatility 0.7426 (12.04) 0.3440 (9.08) 0.1620 (6.12) 0.5148 (11.66) 0.3737 (10.63) ∆ TBill Rate 4.9053 (1.42) -0.7808 (-0.39) 3.0751 (1.35) 1.7248 (0.56) -2.4559 (-1.43) ∆ US Term spread 1.0432 (0.38) -1.1233 (-0.72) -1.8582 (-1.04) 1.2827 (0.53) -1.0661 (-0.79) Net Equity Flow -0.0277 (-0.20) -0.0001 (-0.02) 0.0899 (1.30) 0.0054 (0.34) 0.0021 (0.63) Total Net World Bond Flow -0.4940 (-4.12) -0.1079 (-1.55) 0.0861 (1.07) -0.2326 (-2.17) -0.1130 (-1.89) Adj R-squared 38.9% 34.0% 38.1% 42.3% 36.8% continued 103 Table A.20 continued Market Return Equations Asia Europe North America Emerging Asia Emerging America -0.0015 (-1.30) -0.0003 (-0.52) -0.0017 (-2.20) -0.0017 (-1.45) 0.0019 (2.58) Asia 0.1271 (2.89) Market Return – Lag -0.0127 -0.0492 0.0334 (-0.63) (-1.67) (0.75) 0.0190 (0.68) Europe -0.0554 (-0.47) 0.3678 (6.82) 0.1164 (1.48) 0.2343 (1.99) 0.1875 (2.53) North America 0.0969 (1.24) -0.0025 (-0.07) 0.2156 (4.12) 0.0233 (0.30) -0.0735 (-1.49) Emerging Asia -0.0010 (-0.02) -0.0359 (-1.81) -0.0084 (-0.29) 0.0004 (0.01) -0.0040 (-0.15) Emerging America -0.0897 (-1.32) -0.0086 (-0.27) 0.0292 (0.63) 0.0182 (0.26) 0.1648 (3.70) Asia 0.0795 (1.79) Market Return – Lag 0.0258 0.0162 0.0358 (1.26) (0.54) (0.80) -0.0111 (-0.40) Europe 0.0259 (0.21) 0.0834 (1.48) -0.0251 (-0.31) -0.0976 (-0.79) 0.0975 (1.26) North America -0.0039 (-0.05) 0.0195 (0.53) 0.1336 (2.50) 0.0975 (1.21) -0.0288 (-0.57) Emerging Asia -0.0143 (-0.33) 0.0121 (0.61) 0.0393 (1.36) 0.0216 (0.50) 0.0256 (0.94) Emerging America -0.0338 (-0.49) -0.0019 (-0.06) -0.0314 (-0.68) 0.0289 (0.42) 0.1315 (2.94) Asia 0.0281 (0.64) Market Return – Lag 0.0114 0.0250 -0.0609 (0.56) (0.85) (-1.38) -0.0306 (-1.11) Europe 0.1680 (1.42) 0.0470 (0.86) 0.1350 (1.70) -0.3552 (-2.97) 0.0187 (0.25) North America -0.0518 (-0.66) 0.0075 (0.21) 0.0194 (0.37) 0.1354 (1.72) 0.0624 (1.27) Emerging Asia -0.0382 (-0.88) -0.0116 (-0.59) -0.0137 (-0.47) 0.0806 (1.85) 0.0194 (0.71) Emerging America 0.0720 (1.07) 0.0213 (0.68) 0.0032 (0.07) 0.1299 (1.90) -0.0045 (-0.10) Intercept continued 104 Table A.20 continued Market Return – Cross Equation Variables Asia Europe North America Emerging Asia Emerging America ∆ Market Liquidity t-1 0.0131 (1.26) -0.0026 (-0.34) -0.0056 (-0.57) -0.0006 (-0.05) -0.0010 (-0.07) ∆ Market Liquidity t-2 -0.0049 (-0.44) -0.0039 (-0.47) -0.0255 (-2.27) -0.0035 (-0.29) -0.0015 (-0.10) ∆ Market Liquidity t-3 0.0028 (0.28) -0.0018 (-0.23) -0.0177 (-1.80) 0.0005 (0.05) -0.0124 (-0.89) 1.6% 15.7% 12.4% 3.0% 9.0% Adj R-squared 105 Asia Europe North America Emerging Emerging Asia America Asia Europe North America Emerging Asia Emerging America Asia Europe North America Emerging Asia Emerging America Market Return Equations Asia ∆ Market Liquidity Equations ∆ Market Liquidity Equations 0.1766 [0.00] 0.1005 [0.01] 0.2792 [0.00] 0.1742 [0.00] 0.0754 [0.06] 0.0798 [0.05] 0.0450 [0.26] 0.0806 [0.04] 0.0021 [0.96] 0.1766 [0.00] 0.6015 [0.00] 0.2882 [0.00] 0.5220 [0.00] -0.0581 [0.15] -0.0672 [0.09] 0.0101 [0.80] -0.0115 [0.77] -0.0447 [0.27] 0.1005 [0.01] 0.6015 [0.00] 0.2477 [0.00] 0.3680 [0.00] 0.0424 [0.29] -0.0269 [0.50] -0.0041 [0.92] -0.0028 [0.94] -0.0575 [0.15] 0.2792 [0.00] 0.2882 [0.00] 0.2477 [0.00] 0.2453 [0.00] -0.0080 [0.84] -0.0369 [0.36] 0.0042 [0.92] 0.0252 [0.53] 0.0238 [0.55] Europe North America Emerging Emerging Asia America Market Return Equations 0.1742 [0.00] 0.5220 [0.00] 0.3680 [0.00] 0.2453 [0.00] 0.0543 [0.18] 0.0925 [0.02] 0.1375 [0.00] 0.1098 [0.01] 0.1903 [0.00] 0.0754 [0.06] -0.0581 [0.15] 0.0424 [0.29] -0.0080 [0.84] 0.0543 [0.18] 0.3599 [0.00] 0.2803 [0.00] 0.2960 [0.00] 0.1725 [0.00] 0.0798 [0.05] -0.0672 [0.09] -0.0269 [0.50] -0.0369 [0.36] 0.0925 [0.02] 0.3599 [0.00] 0.6153 [0.00] 0.3496 [0.00] 0.3561 [0.00] 0.0450 [0.26] 0.0101 [0.80] -0.0041 [0.92] 0.0042 [0.92] 0.1375 [0.00] 0.2803 [0.00] 0.6153 [0.00] 0.3284 [0.00] 0.3683 [0.00] 0.0806 [0.04] -0.0115 [0.77] -0.0028 [0.94] 0.0252 [0.53] 0.1098 [0.01] 0.2960 [0.00] 0.3496 [0.00] 0.3284 [0.00] 0.1899 [0.00] 0.0021 [0.96] -0.0447 [0.27] -0.0575 [0.15] 0.0238 [0.55] 0.1903 [0.00] 0.1725 [0.00] 0.3561 [0.00] 0.3683 [0.00] 0.1899 [0.00] Table A.21: Table reports the estimated correlation matrix of residuals from the model reported in table A.20 The corresponding p-values for the hypothesis that the correlation is zero are in the brackets below 106 -50 ex [DA] Europe [DE] North America [DN] Emerging Asia [EA] Emerging America [ES] [DE] North America [DN] Emerging Asia [EA] [EA] [DN] Emerging America [ES] Emerging Asia [EA] Emerging America [ES] Emerging America [ES] 0.2077 0.1793 0.1587 0.1358 0.2749 0.2439 0.2310 0.1710 0.6846 0.4515 0.3225 0.2338 0.6386 0.3803 -100 ex ** ** ** ** ** + ** 0.3185 0.2934 0.2883 0.2585 0.4089 0.3453 0.3374 0.2918 ** ** ** ** 100 ex 0.3298 0.2934 0.2337 0.2585 0.3329 0.3453 0.2847 0.2918 ** ** ** ** ** ** 50 ex 0.2774 0.1793 0.1583 0.1358 0.2633 0.2439 0.2723 0.1710 ** ** ** ** ** + 0.5831 ** 0.5575 0.3480 ** 0.3479 0.5773 ** 0.4942 0.4597 0.5575 0.3177 0.3479 0.4273 0.4942 0.3073 ** 0.2216 0.5415 ** 0.2804 + 0.3103 ** 0.3277 0.4914 ** 0.3901 + 0.3164 ** 0.3277 0.3595 ** 0.3901 0.2081 ** 0.2216 0.2549 ** 0.2804 0.2310 ** 0.1710 0.3782 ** 0.3335 0.3537 ** 0.3335 0.2179 ** 0.2154 ** 0.4128 ** 0.4515 0.2110 ** 0.2338 0.3568 ** 0.3803 Table A.22: Table reports estimated tail correlations for threshold values with 50 and 100 positive and negative exceedences The first row reports the tail correlation and the second row reports the average tail correlation from a Monte Carlo simulation of 100 simulated samples of bivariate normally distributed values with matching means and covariances ** and * indicate significance at the 5% and the 10% level for the estimated tail correlation, respectively + and - indicate that the estimated tail correlation is above or below the 95% range of the simulated estimates, respectively 107 APPENDIX B FIGURES 108 0.015 Asia 0.01 Europe Weekly Change in Liquidity 0.005 -0.005 North America emerging Asia -0.01 emerging America -0.015 October-93 October-94 October-95 October-96 October-97 October-98 October-99 October-00 October-01 Figure B.1: Figure presents a 50-week moving average of market-wide changes in five regional aggregates over the full sample of liquidity from 12/18/1990 to 1/2/2002 See table A.15 and the text for more details 109 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 -50 -100 -150 -200 200 150 100 DA - DE DA - DN DA - EA DA - ES DE - EA DE - ES DN - EA DN - ES 50 DE - DN EA - ES Figure B.2: The figure reports the estimated tail correlations ρ = 1−α2 from bivariate peak over threshold models, where α is the dependence parameter from a logistic dependence function D which maps the marginal extreme value distributions Gi of each variables into the one-dimensional space D(DL1 , DL2 ) = (G1 (DL1 )−1/α + G2 (DL2 )−1/α )α The results are calculated for threshold values with exceedences of positive and negative 200, 150, 100, and 50 values See text for a discription of the estimation method 110 ... determinants of liquidity indicates that market- wide returns, market- wide averages of individual stock volatilities, and world net bond flows are fundamental drivers of market- wide liquidity There... that changes in market valuations affect liquidity, and market- wide averages of individual stock volatilities and world net bond flows are further fundamental drivers of market- wide liquidity Beyond... integration of financial markets and the large scale international portfolio flows, it is a natural question to investigate the existence, nature, and impact of market- wide liquidity in an international