Diebold / Rudebusch | Yield Curve Modeling and Forecasting | E-Book | www2.sack.de
E-Book

E-Book, Englisch, 224 Seiten

Reihe: The Econometric and Tinbergen Institutes Lectures

Diebold / Rudebusch Yield Curve Modeling and Forecasting

The Dynamic Nelson-Siegel Approach
Course Book
ISBN: 978-1-4008-4541-5
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

The Dynamic Nelson-Siegel Approach

E-Book, Englisch, 224 Seiten

Reihe: The Econometric and Tinbergen Institutes Lectures

ISBN: 978-1-4008-4541-5
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)



Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed.

Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.

Diebold / Rudebusch Yield Curve Modeling and Forecasting jetzt bestellen!

Weitere Infos & Material


List of Illustrations ix

Introduction xi

Preface xiii

Additional Acknowledgment xvii

1 Facts, Factors, and Questions 1

- 1.1 Three Interest Rate Curves 2

- 1.2 Zero-Coupon Yields 3

- 1.3 Yield Curve Facts 4

- 1.4 Yield Curve Factors 7

- 1.5 Yield Curve Questions 13

- 1.6 Onward 22

2 Dynamic Nelson-Siegel 23

- 2.1 Curve Fitting 23

- 2.2 Introducing Dynamics 26

- 2.3 State-Space Representation 30

- 2.4 Estimation 34

- 2.5 Multicountry Modeling 42

- 2.6 Risk Management 46

- 2.7 DNS Fit and Forecasting 49

3 Arbitrage-Free Nelson-Siegel 55

- 3.1 A Two-Factor Warm-Up 58

- 3.2 The Duffie-Kan Framework 62

- 3.3 Making DNS Arbitrage-Free 64

- 3.4 Workhorse Models 78

- 3.5 AFNS Restrictions on A0(3) 83

- 3.6 Estimation 86

- 3.7 AFNS Fit and Forecasting 90

4 Extensions 96

- 4.1 Variations on the Basic Theme 96

- 4.2 Additional Yield Factors 105

- 4.3 Stochastic Volatility 113

- 4.4 Macroeconomic Fundamentals 117

5 Macro-Finance 126

- 5.1 Macro-Finance Yield Curve Modeling 126

- 5.2 Macro-Finance and AFNS 131

- 5.3 Evolving Research Directions 144

6 Epilogue 149

- 6.1 Is Imposition of No-Arbitrage Helpful? 151

- 6.2 Is AFNS the Only Tractable A0(3) Model? 153

- 6.3 Is AFNS Special? 155

Appendixes 159

Appendix A Two-Factor AFNS Calculations 161

- A.1 Risk-Neutral Probability 161

- A.2 Euler Equation 163

Appendix B Details of AFNS Restrictions 166

- B.1 Independent-Factor AFNS 168

- B.2 Correlated-Factor AFNS 171

Appendix C The AFGNS Yield-Adjustment Term 174

Bibliography 179

Index 197


Francis X. Diebold is the Paul F. and Warren S. Miller Professor of Economics at the University of Pennsylvania and professor of finance and statistics at the university's Wharton School. Glenn D. Rudebusch is executive vice president and director of economic research at the Federal Reserve Bank of San Francisco. They are the coauthors of Business Cycles: Durations, Dynamics, and Forecasting (Princeton).



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