Financial and Macroeconomic Connectedness : A Network Approach to Measurement and Monitoring

Présentation de l'éditeur : "Connections among different assets, asset classes, portfolios, and the stocks of individual institutions are critical in examining financial markets. Interest in financial markets implies interest in underlying macroeconomic fundamentals. In Financial and Macroeconomic Connectedness, Frank Diebold and Kamil Yilmaz propose a simple framework for defining, measuring, and monitoring connectedness, which is central to finance and macroeconomics. These measures of connectedness are theoretically rigorous yet empirically relevant. The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the 'connectedness table.' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authors also show that variance decompositions define weighted, directed networks, so that these proposed connectedness measures are intimately related to key measures of connectedness used in the network literature. After describing their methods in the first part of the book, the authors proceed to characterize daily return and volatility connectedness across major asset (stock, bond, foreign exchange and commodity) markets as well as the financial institutions within the U.S. and across countries since late 1990s. These specific measures of volatility connectedness show that stock markets played a critical role in spreading the volatility shocks from the U.S. to other countries. Furthermore, while the return connectedness across stock markets increased gradually over time the volatility connectedness measures were subject to significant jumps during major crisis events. This book examines not only financial connectedness, but also real fundamental connectedness. In particular, the authors show that global business cycle connectedness is economically significant and time-varying, that the U.S. has disproportionately high connectedness to others, and that pairwise country connectedness is inversely related to bilateral trade surpluses." Cover 1 Contents 8 Preface 12 Additional Acknowledgments 16 1 Measuring and Monitoring Financial and Macroeconomic Connectedness 20 1.1 Motivation and Background 21 1.1.1 Market Risk 21 1.1.2 Portfolio Concentration Risk 21 Exogenous Aspects 21 Endogenous Aspects 22 Factor Structure 22 Ignoring Connectedness 23 1.1.3 Credit Risk 23 1.1.4 Counterparty and ''Gridlock'' Risk 23 1.1.5 Systemic Risk 24 1.1.6 Business Cycle Risk 25 1.1.7 Financial and Macroeconomic Crisis Monitoring 25 1.1.8 A Final Remark 26 1.2 The Connectedness Table 27 1.2.1 Decomposing Variation 27 1.2.2 Perspectives on Our Approach 30 Nonstructural 30 Empirical/Statistical 31 Relationship to Stress Testing 32 1.2.3 Identifying Shocks 32 Orthogonal Shocks 32 Correlated Shocks 33 Choosing an Identification Method 35 1.2.4 Toward Dynamics 35 1.3 Estimating Dynamic Connectedness 36 1.3.1 x 36 Asset Returns 37 Asset Return Volatilities 37 Real Fundamentals 37 The Reference Universe 38 Additional Discussion 38 1.3.2 H 38 1.3.3 M1:T(θt) 39 Time-Varying Connectedness 40 Discussion 41 1.4 On the Connectedness of Connectedness 43 1.4.1 Financial Econometric Connectedness 43 Correlation Measures 44 Systemic Measures: CoVaR and MES 45 1.4.2 Network Connectedness 46 The Degree Distribution 47 The Distance Distribution 48 The Second Laplacian Eigenvalue 49 Variance Decompositions as Networks 50 1.4.3 ''Spillover'' and ''Contagion'' Connectedness 51 1.4.4 Concluding Remarks 52 2 U.S. Asset Classes 53 2.1 Volatility in U.S. Asset Markets 54 2.2 Unconditional Patterns: Full-Sample Volatility Connectedness 57 2.3 Conditional Patterns: Conditioning and Dynamics of Volatility Connectedness 59 2.3.1 Total Volatility Connectedness 59 2.3.2 Directional Volatility Connectedness 61 2.4 Concluding Remarks 67 2.A Appendix: Standard Errors and Robustness 67 3 Major U.S. Financial Institutions 70 3.1 Volatility of Bank Stock Returns 71 3.2 Static (Full-Sample, Unconditional) Analysis 72 3.3 Dynamic (Rolling-Sample, Conditional) Analysis 77 3.3.1 Total Connectedness 77 3.3.2 Total Directional Connectedness 80 3.3.3 Pairwise Directional Connectedness 84 3.4 The Financial Crisis of 2007–2009 84 3.4.1 Total Connectedness at Various Stages of the Crisis 85 3.4.2 Pairwise Connectedness of Troubled Financial Institutions 89 3.A Appendix: Standard Errors and Robustness 71 4 Global Stock Markets 103 4.1 Return and Volatility in Global Stock Markets 104 4.2 Full-Sample Return and Volatility Connectedness 108 4.2.1 Total Return and Volatility Connectedness 108 4.2.2 Directional Return and Volatility Connectedness 110 4.3 Dynamics of Return and Volatility Connectedness 113 4.3.1 Total Connectedness 113 4.3.2 Total Directional Connectedness 120 Return Connectedness 120 Volatility Connectedness 123 4.3.3 Pairwise Directional Connectedness 125 Return Connectedness 125 Volatility Connectedness 127 4.A Appendix: Standard Errors and Robustness 129 5 Sovereign Bond Markets 137 5.1 Bond Market Data 140 5.2 Full-Sample Return and Volatility Connectedness 143 5.3 Dynamics of Return Connectedness 146 5.4 Dynamics of Volatility Connectedness 153 5.4.1 Total Connectedness 153 5.4.2 Total and Pairwise Directional Connectedness 157 5.A Appendix: Standard Errors and Robustness 163 6 Foreign Exchange Markets 171 6.1 Globalization and FX Market Volatility 172 6.1.1 Recent Developments in FX Markets 172 6.1.2 Literature on FX Market Volatility 172 6.1.3 Interest Rate Differentials and the Exchange Rates 175 6.1.4 Data 177 6.2 Full-Sample Volatility Connectedness 179 6.3 Dynamics of Volatility Connectedness 183 6.3.1 Total Volatility Connectedness 183 6.3.2 Total Directional Volatility Connectedness 190 6.3.3 Pairwise Directional Connectedness 195 6.A Appendix: Standard Errors and Robustness 198 7 Assets Across Countries 201 7.1 Four Asset Classes in Four Countries 202 7.2 Full-Sample Volatility Connectedness 202 7.3 Dynamics of Volatility Connectedness 205 7.3.1 Total Connectedness 205 7.3.2 Pairwise Directional Connectedness 211 7.A Appendix: Standard Errors and Robustness 215 8 Global Business Cycles 219 8.1 Data, Unit Roots, and Co-integration 221 8.2 The Empirics of Business Cycle Connectedness 222 8.2.1 The Business Cycle Connectedness Table 222 8.2.2 The Business Cycle Connectedness Plot 224 8.2.3 Sensitivity Analysis 228 8.2.4 The Dynamics of Directional Business Cycle Connectedness 230 8.3 International Trade and Directional Connectedness 234 8.4 Alternative Measures: Country Factors 236 8.5 The Analysis with BRIC Countries 238 8.6 Concluding Remarks 242 8.A Appendix: Additional Tables and Figures 243 References 252 Author Index 260 General Index 264 Présentation de l'éditeur : "Connections among different assets, asset classes, portfolios, and the stocks of individual institutions are critical in examining financial markets. Interest in financial markets implies interest in underlying macroeconomic fundamentals. In Financial and Macroeconomic Connectedness, Frank Diebold and Kamil Yilmaz propose a simple framework for defining, measuring, and monitoring connectedness, which is central to finance and macroeconomics. These measures of connectedness are theoretically rigorous yet empirically relevant. The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the 'connectedness table.' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authors also show that variance decompositions define weighted, directed networks, so that these proposed connectedness measures are intimately related to key measures of connectedness used in the network literature. After describing their methods in the first part of the book, the authors proceed to characterize daily return and volatility connectedness across major asset (stock, bond, foreign exchange and commodity) markets as well as the financial institutions within the U.S. and across countries since late 1990s. These specific measures of volatility connectedness show that stock markets played a critical role in spreading the volatility shocks from the U.S. to other countries. Furthermore, while the return connectedness across stock markets increased gradually over time the volatility connectedness measures were subject to significant jumps during major crisis events. This book examines not only financial connectedness, but also real fundamental connectedness. In particular, the authors show that global business cycle connectedness is economically significant and time-varying, that the U.S. has disproportionately high connectedness to others, and that pairwise country connectedness is inversely related to bilateral trade surpluses." "The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the 'connectedness table.' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authors also show that variance decompositions define weighted, directed networks, so that these proposed connectedness measures are intimately related to key measures of connectedness used in the network literature. After describing their methods in the first part of the book, the authors proceed to characterize daily return and volatility connectedness across major asset (stock, bond, foreign exchange and commodity) markets as well as the financial institutions within the U.S. and across countries since late 1990s. These specific measures of volatility connectedness show that stock markets played a critical role in spreading the volatility shocks from the U.S. to other countries. Furthermore, while the return connectedness across stock markets increased gradually over time the volatility connectedness measures were subject to significant jumps during major crisis events. This book examines not only financial connectedness, but also real fundamental connectedness. In particular, the authors show that global business cycle connectedness is economically significant and time-varying, that the U.S. has disproportionately high connectedness to others, and that pairwise country connectedness is inversely related to bilateral trade surpluses"--The publisher