Buch, Englisch, 544 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 767 g
ISBN: 978-0-691-13479-6
Verlag: Princeton University Press
This book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions.Stephen Taylor provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions.Asset Price Dynamics, Volatility, and Prediction is ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Preface xiii
Chapter 1: Introduction 1
1.1 Asset Price Dynamics 1
1.2 Volatility 1
1.3 Prediction 2
1.4 Information 2
1.5 Contents 3
1.6 Software 5
1.7 Web Resources 6
PART I: Foundations 7
Chapter 2: Prices and Returns 9
2.1 Introduction 9
2.2 Two Examples of Price Series 9
2.3 Data-Collection Issues 10
2.4 Two Returns Series 13
2.5 Definitions of Returns 14
2.6 Further Examples of Time Series of Returns 19
Chapter 3: Stochastic Processes: Definitions and Examples 23
3.1 Introduction 23
3.2 Random Variables 24
3.3 Stationary Stochastic Processes 30
3.4 Uncorrelated Processes 33
3.5 ARMA Processes 36
3.6 Examples of ARMA 1 1 Specifications 44
3.7 ARIMA Processes 46
3.8 ARFIMA Processes 46
3.9 Linear Stochastic Processes 48
3.10 Continuous-Time Stochastic Processes 49
3.11 Notation for Random Variables and Observations 50
Chapter 4: Stylized Facts for Financial Returns 51
4.1 Introduction 51
4.2 Summary Statistics 52
4.3 Average Returns and Risk Premia 53
4.4 Standard Deviations 57
4.5 Calendar Effects 59
4.6 Skewness and Kurtosis 68
4.7 The Shape of the Returns Distribution 69
4.8 Probability Distributions for Returns 73
4.9 Autocorrelations of Returns 76
4.10 Autocorrelations of Transformed Returns 82
4.11 Nonlinearity of the Returns Process 92
4.12 Concluding Remarks 93
4.13 Appendix: Autocorrelation Caused by Day-of-the-Week Effects 94
4.14 Appendix: Autocorrelations of a Squared Linear Process 95
PART II: Conditional Expected Returns 97
Chapter 5: The Variance-Ratio Test of the Random Walk Hypothesis 99
5.1 Introduction 99
5.2 The Random Walk Hypothesis 100
5.3 Variance-Ratio Tests 102
5.4 An Example of Variance-Ratio Calculations 105
5.5 Selected Test Results 107
5.6 Sample Autocorrelation Theory 112
5.7 Random Walk Tests Using Rescaled Returns 115
5.8 Summary 120
Chapter 6: Further Tests of the Random Walk Hypothesis 121
6.1 Introduction 121
6.2 Test Methodology 122
6.3 Further Autocorrelation Tests 126
6.4 Spectral Tests 130
6.5 The Runs Test 133
6.6 Rescaled Range Tests 135
6.7 The BDS Test 136
6.8 Test Results for the Random Walk Hypothesis 138
6.9 The Size and Power of Random Walk Tests 144
6.10 Sources of Minor Dependence in Returns 148
6.11 Concluding Remarks 151
6.12 Appendix: the Correlation between Test Values for Two Correlated Series 153
6.13 Appendix: Autocorrelation Induced by Rescaling Returns 154
Chapter 7: Trading Rules and Market Efficiency 157
7.1 Introduction 157
7.2 Four Trading Rules 158
7.3 Measures of Return Predictability 163
7.4 Evidence about Equity Return Predictability 166
7.5 Evidence about the Predictability of Currency and Other Returns 168
7.6 An Example of Calculations for the Moving-Average Rule 172
7.7 Efficient Markets: Methodological Issues 175
7.8 Breakeven Costs for Trading Rules Applied to Equities 176
7.9 Trading Rule Performance for Futures Contracts 179
7.10 The Efficiency of Currency Markets 181
7.11 Theoretical Trading Profits for Autocorrelated Return Processes 184
7.12 Concluding Remarks 186
PART III: Volatility Processes 187
Chapter 8: An Introduction to Volatility 189
8.1 Definitions of Volatility 189
8.2 Explanations of Changes in Volatility 191
8.3 Volatility and Information Arrivals 193
8.4 Volatility and the Stylized Facts for Returns 195
8.5 Concluding Remarks 196
Chapter 9: ARCH Models: Definitions and Examples 197
9.1 Introduction 197
9.2 ARCH(1) 198
9.3 GARCH 1 1 199
9.4 An Exchange Rate Example of the GARCH 1 1 Model 205
9.5 A General ARCH Framework 212
9.6 Nonnormal Conditional Distributions 217
9.7 Asymmetric Volatility Models 220
9.8 Equity Examples of Asymmetric Volatility Models 222
9.9 Summary 233
Chapter 10: ARCH Models: Selection and Likelihood Methods 235
10.1 Introduction 235
10.2 Asymmetric Volatility: Further Specifications and Evide
