Asset price dynamics, volatility, and prediction /

Asset price dynamics, volatility, and prediction /

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Bibliographic Details
Author / Creator:Taylor, Stephen (Stephen J.)
Imprint:Princeton, N.J. : Princeton University Press, c2005.
Description:xv, 525 p. : ill. ; 24 cm.
Language:English
Subject:
Capital assets pricing model.
Finance -- Mathematical models.
Capital assets pricing model.
Finance -- Mathematical models.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/5723807
Hidden Bibliographic Details
ISBN:0691115370 (alk. paper)
Notes:Includes bibliographical references (p. [473]-501) and indexes.
Table of Contents:
  • Preface
  • 1. Introduction
  • 1.1. Asset Price Dynamics
  • 1.2. Volatility
  • 1.3. Prediction
  • 1.4. Information
  • 1.5. Contents
  • 1.6. Software
  • 1.7. Web Resources
  • I. Foundations
  • 2. Prices and Returns
  • 2.1. Introduction
  • 2.2. Two Examples of Price Series
  • 2.3. Data-Collection Issues
  • 2.4. Two Returns Series
  • 2.5. Definitions of Returns
  • 2.6. Further Examples of Time Series of Returns
  • 3. Stochastic Processes: Definitions and Examples
  • 3.1. Introduction
  • 3.2. Random Variables
  • 3.3. Stationary Stochastic Processes
  • 3.4. Uncorrelated Processes
  • 3.5. ARMA Processes
  • 3.6. Examples of ARMA(1, 1) Specifications
  • 3.7. ARIMA Processes
  • 3.8. ARFIMA Processes
  • 3.9. Linear Stochastic Processes
  • 3.10. Continuous-Time Stochastic Processes
  • 3.11. Notation for Random Variables and Observations
  • 4. Stylized Facts for Financial Returns
  • 4.1. Introduction
  • 4.2. Summary Statistics
  • 4.3. Average Returns and Risk Premia
  • 4.4. Standard Deviations
  • 4.5. Calendar Effects
  • 4.6. Skewness and Kurtosis
  • 4.7. The Shape of the Returns Distribution
  • 4.8. Probability Distributions for Returns
  • 4.9. Autocorrelations of Returns
  • 4.10. Autocorrelations of Transformed Returns
  • 4.11. Nonlinearity of the Returns Process
  • 4.12. Concluding Remarks
  • 4.13. Appendix: Autocorrelation Caused by Day-of-the-Week Effects
  • 4.14. Appendix: Autocorrelations of a Squared Linear Process
  • II. Conditional Expected Returns
  • 5. The Variance-Ratio Test of the Random Walk Hypothesis
  • 5.1. Introduction
  • 5.2. The Random Walk Hypothesis
  • 5.3. Variance-Ratio Tests
  • 5.4. An Example of Variance-Ratio Calculations
  • 5.5. Selected Test Results
  • 5.6. Sample Autocorrelation Theory
  • 5.7. Random Walk Tests Using Rescaled Returns
  • 5.8. Summary
  • 6. Further Tests of the Random Walk Hypothesis
  • 6.1. Introduction
  • 6.2. Test Methodology
  • 6.3. Further Autocorrelation Tests
  • 6.4. Spectral Tests
  • 6.5. The Runs Test
  • 6.6. Rescaled Range Tests
  • 6.7. The BDS Test
  • 6.8. Test Results for the Random Walk Hypothesis
  • 6.9. The Size and Power of Random Walk Tests
  • 6.10. Sources of Minor Dependence in Returns
  • 6.11. Concluding Remarks
  • 6.12. Appendix: the Correlation between Test Values for Two Correlated Series
  • 6.13. Appendix: Autocorrelation Induced by Rescaling Returns
  • 7. Trading Rules and Market Efficiency
  • 7.1. Introduction
  • 7.2. Four Trading Rules
  • 7.3. Measures of Return Predictability
  • 7.4. Evidence about Equity Return Predictability
  • 7.5. Evidence about the Predictability of Currency and Other Returns
  • 7.6. An Example of Calculations for the Moving-Average Rule
  • 7.7. Efficient Markets: Methodological Issues
  • 7.8. Breakeven Costs for Trading Rules Applied to Equities
  • 7.9. Trading Rule Performance for Futures Contracts
  • 7.10. The Efficiency of Currency Markets
  • 7.11. Theoretical Trading Profits for Autocorrelated Return Processes
  • 7.12. Concluding Remarks
  • III. Volatility Processes
  • 8. An Introduction to Volatility
  • 8.1. Definitions of Volatility
  • 8.2. Explanations of Changes in Volatility
  • 8.3. Volatility and Information Arrivals
  • 8.4. Volatility and the Stylized Facts for Returns
  • 8.5. Concluding Remarks
  • 9. ARCH Models: Definitions and Examples
  • 9.1. Introduction
  • 9.2. ARCH(1)
  • 9.3. GARCH(1, 1)
  • 9.4. An Exchange Rate Example of the GARCH(1, 1) Model
  • 9.5. A General ARCH Framework
  • 9.6. Nonnormal Conditional Distributions
  • 9.7. Asymmetric Volatility Models
  • 9.8. Equity Examples of Asymmetric Volatility Models
  • 9.9. Summary
  • 10. ARCH Models: Selection and Likelihood Methods
  • 10.1. Introduction
  • 10.2. Asymmetric Volatility: Further Specifications and Evidence
  • 10.3. Long Memory ARCH Models
  • 10.4. Likelihood Methods
  • 10.5. Results from Hypothesis Tests
  • 10.6. Model Building
  • 10.7. Further Volatility Specifications
  • 10.8. Concluding Remarks
  • 10.9. Appendix: Formulae for the Score Vector
  • 11. Stochastic Volatility Models
  • 11.1. Introduction
  • 11.2. Motivation and Definitions
  • 11.3. Moments of Independent SV Processes
  • 11.4. Markov Chain Models for Volatility
  • 11.5. The Standard Stochastic Volatility Model
  • 11.6. Parameter Estimation for the Standard SV Model
  • 11.7. An Example of SV Model Estimation for Exchange Rates
  • 11.8. Independent SV Models with Heavy Tails
  • 11.9. Asymmetric Stochastic Volatility Models
  • 11.10. Long Memory SV Models
  • 11.11. Multivariate Stochastic Volatility Models
  • 11.12. ARCH versus SV
  • 11.13. Concluding Remarks
  • 11.14. Appendix: Filtering Equations
  • IV. High-Frequency Methods
  • 12. High-Frequency Data and Models
  • 12.1. Introduction
  • 12.2. High-Frequency Prices
  • 12.3. One Day of High-Frequency Price Data
  • 12.4. Stylized Facts for Intraday Returns
  • 12.5. Intraday Volatility Patterns
  • 12.6. Discrete-Time Intraday Volatility Models
  • 12.7. Trading Rules and Intraday Prices
  • 12.8. Realized Volatility: Theoretical Results
  • 12.9. Realized Volatility: Empirical Results
  • 12.10. Price Discovery
  • 12.11. Durations
  • 12.12. Extreme Price Changes
  • 12.13. Daily High and Low Prices
  • 12.14. Concluding Remarks
  • 12.15. Appendix: Formulae for the Variance of the Realized Volatility Estimator
  • V. Inferences from Option Prices
  • 13. Continuous-Time Stochastic Processes
  • 13.1. Introduction
  • 13.2. The Wiener Process
  • 13.3. Diffusion Processes
  • 13.4. Bivariate Diffusion Processes
  • 13.5. Jump Processes
  • 13.6. Jump-Diffusion Processes
  • 13.7. Appendix: a Construction of the Wiener Process
  • 14. Option Pricing Formulae
  • 14.1. Introduction
  • 14.2. Definitions, Notation, and Assumptions
  • 14.3. Black-Scholes and Related Formulae
  • 14.4. Implied Volatility
  • 14.5. Option Prices when Volatility Is Stochastic
  • 14.6. Closed-Form Stochastic Volatility Option Prices
  • 14.7. Option Prices for ARCH Processes
  • 14.8. Summary
  • 14.9. Appendix: Heston's Option Pricing Formula
  • 15. Forecasting Volatility
  • 15.1. Introduction
  • 15.2. Forecasting Methodology
  • 15.3. Two Measures of Forecast Accuracy
  • 15.4. Historical Volatility Forecasts
  • 15.5. Forecasts from Implied Volatilities
  • 15.6. ARCH Forecasts that Incorporate Implied Volatilities
  • 15.7. High-Frequency Forecasting Results
  • 15.8. Concluding Remarks
  • 16. Density Prediction for Asset Prices
  • 16.1. Introduction
  • 16.2. Simulated Real-World Densities
  • 16.3. Risk-Neutral Density Concepts and Definitions
  • 16.4. Estimation of Implied Risk-Neutral Densities
  • 16.5. Parametric Risk-Neutral Densities
  • 16.6. Risk-Neutral Densities from Implied Volatility Functions
  • 16.7. Nonparametric RND Methods
  • 16.8. Towards Recommendations
  • 16.9. From Risk-Neutral to Real-World Densities
  • 16.10. An Excel Spreadsheet for Density Estimation
  • 16.11. Risk Aversion and Rational RNDs
  • 16.12. Tail Density Estimates
  • 16.13. Concluding Remarks
  • Symbols
  • References
  • Author Index
  • Subject Index
In Memory of Wesley J. Holland through the Holland Foundation bookplate

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