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Analysis of Financial Time Series 1st Edition by Ruey Tsay ISBN 978-0471415442 0471415448

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Author(s): Ruey S. Tsay
ISBN(s): 9780471461616, 047146161X
Edition: 1st
File Details: PDF, 3.34 MB
Year: 2002
Language: english
SKU: EB-897984 Category: Tag:

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Product details: 

ISBN 10:   0471415448

ISBN 13: 978-0471415442

Author:  Ruey S. Tsay

Fundamental topics and new methods in time series analysis
Analysis of Financial Time Series provides a comprehensive and systematic introduction to financial econometric models and their application to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.
The author begins with basic characteristics of financial time series data before covering three main topics: analysis and application of univariate financial time series; the return series of multiple assets; and Bayesian inference in finance methods. Timely topics and recent results include:
* Value at Risk (VaR)
* High-frequency financial data analysis
* Markov Chain Monte Carlo (MCMC) methods
* Derivative pricing using jump diffusion with closed-form formulas
* VaR calculation using extreme value theory based on a non-homogeneous two-dimensional Poisson process
* Multivariate volatility models with time-varying correlations
Ideal as a fundamental introduction to time series for MBA students or as a reference for researchers and practitioners in business and finance, Analysis of Financial Time Series offers an in-depth and up-to-date account of these vital methods.

Table of contents: 

Chapter 1: Financial Time Series and Their Characteristics

  • Asset Returns

  • Distributional Properties of Returns

  • Processes Considered

Chapter 2: Linear Time Series Analysis and Its Applications

  • Stationarity

  • Correlation and Autocorrelation Function

  • White Noise and Linear Time Series

  • Simple Autoregressive Models

  • Simple Moving-Average Models

  • Simple ARMA Models

  • Unit-Root Nonstationarity

  • Seasonal Models

  • Regression Models with Time Series Errors

  • Long-Memory Models

  • Appendix A: Some SCA Commands

Chapter 3: Conditional Heteroscedastic Models

  • Characteristics of Volatility

  • Structure of a Model

  • The ARCH Model

  • The GARCH Model

  • The Integrated GARCH Model

  • The GARCH-M Model

  • The Exponential GARCH Model

  • The CHARMA Model

  • Random Coefficient Autoregressive Models

  • The Stochastic Volatility Model

  • The Long-Memory Stochastic Volatility Model

  • An Alternative Approach

  • Application

  • Kurtosis of GARCH Models

  • Appendix A: Some RATS Programs for Estimating Volatility Models

Chapter 4: Nonlinear Models and Their Applications

  • Nonlinear Models

  • Nonlinearity Tests

  • Modeling

  • Forecasting

  • Application

  • Appendix A: Some RATS Programs for Nonlinear Volatility Models

  • Appendix B: S-Plus Commands for Neural Network

Chapter 5: High-Frequency Data Analysis and Market Microstructure

  • Nonsynchronous Trading

  • Bid-Ask Spread

  • Empirical Characteristics of Transactions Data

  • Models for Price Changes

  • Duration Models

  • Nonlinear Duration Models

  • Bivariate Models for Price Change and Duration

  • Appendix A: Review of Some Probability Distributions

  • Appendix B: Hazard Function

  • Appendix C: Some RATS Programs for Duration Models

Chapter 6: Continuous-Time Models and Their Applications

  • Options

  • Some Continuous-Time Stochastic Processes

  • Ito’s Lemma

  • Distributions of Stock Prices and Log Returns

  • Derivation of Black-Scholes Differential Equation

  • Black-Scholes Pricing Formulas

  • An Extension of Ito’s Lemma

  • Stochastic Integral

  • Jump Diffusion Models

  • Estimation of Continuous-Time Models

  • Appendix A: Integration of Black-Scholes Formula

  • Appendix B: Approximation to Standard Normal Probability

Chapter 7: Extreme Values, Quantile Estimation, and Value at Risk

  • Value at Risk

  • RiskMetrics

  • An Econometric Approach to VaR Calculation

  • Quantile Estimation

  • Extreme Value Theory

  • An Extreme Value Approach to VaR

  • A New Approach Based on the Extreme Value Theory

Chapter 8: Multivariate Time Series Analysis and Its Applications

  • Weak Stationarity and Cross-Correlation Matrices

  • Vector Autoregressive Models

  • Vector Moving-Average Models

  • Vector ARMA Models

  • Unit-Root Nonstationarity and Co-Integration

  • Threshold Co-Integration and Arbitrage

  • Principal Component Analysis

  • Factor Analysis

  • Appendix A: Review of Vectors and Matrices

  • Appendix B: Multivariate Normal Distributions

Chapter 9: Multivariate Volatility Models and Their Applications

  • Reparameterization

  • GARCH Models for Bivariate Returns

  • Higher Dimensional Volatility Models

  • Factor-Volatility Models

  • Application

  • Multivariate Distribution

  • Appendix A: Some Remarks on Estimation

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