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Introduction to Analysis of Financial Data with R

ISBN-10: 0470890819
ISBN-13: 9780470890813
Edition: 2013
Authors: Ruey S. Tsay
List price: $153.00 Buy it from $67.78 Rent it from $34.44
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Description: This book provides a systematic and mathematically accessible introduction to financial econometric models and their applications in modeling and predicting financial time series data. It emphasizes empirical financial data and focuses on real-world  More...

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Book details

List price: $153.00
Copyright year: 2013
Publisher: John Wiley & Sons Canada, Limited
Publication date: 10/29/2012
Binding: Hardcover
Pages: 416
Size: 6.25" wide x 9.75" long x 1.00" tall
Weight: 1.540
Language: English

This book provides a systematic and mathematically accessible introduction to financial econometric models and their applications in modeling and predicting financial time series data. It emphasizes empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure, and high-frequency financial data. S-Plus® commands and illustrations are used extensively throughout the book in order to highlight accurate interpretations and graphical representations of financial data. Exercises are included in order to provide readers with more opportunities to put the models and methods into everyday practice. The tools provided in the text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data, most importantly without needless computation.

Preface
Financial Data and their Properties
Asset Returns
Bond Yields and Prices
Implied Volatility
R Packages and Demonstrations
Installation of R Packages
The Quantmod Package
Some Basic R Commands
Examples of Financial Data
Distributional Properties of Returns
Review of Statistical Distributions and Their Moments
Visualization of Financial Data
Some Statistical Distributions
Normal Distribution
Lognormal Distribution
Stable Distribution
Scale Mixture of Normal Distributions
Multivariate Returns
Exercises
References
Linear Models for Financial Time Series
Stationarity
Correlation and Autocorrelation Function
White Noise and Linear Time Series
Simple Autoregressive Models
Properties of AR Models
Identifying AR Models in Practice
Goodness of Fit
Forecasting
Simple Moving Average Models
Properties of MA Models
Identifying MA Order
Estimation
Forecasting Using MA Models
Simple ARMA Models
Properties of ARMA (1,1) Models
General ARMA Models
Identifying ARMA Models
Forecasting Using an ARMA Model
Three Model Representations for an ARMA Model
Unit-Root Nonstationarity
Random Walk
Random Walk with Drift
Trend-Stationary Time Series
General Unit-Root Nonstationary Models
Unit-Root Test
Exponential Smoothing
Seasonal Models
Seasonal Differencing
Multiplicative Seasonal Models
Seasonal Dummy Variable
Regression Models with Time Series Errors
Long-Memory Models
Model Comparison and Averaging
In-sample Comparison
Out-of-sample Comparison
Model Averaging
Exercises
References
Case Studies of Linear Time Series
Weekly Regular Gasoline Price
Pure Time Series Model
Use of Crude Oil Prices
Use of Lagged Crude Oil Prices
Out-of-Sample Predictions
Global Temperature Anomalies
Unit-Root Stationarity
Trend-Nonstationarity
Model Comparison
Long-Term Prediction
Discussion
US Monthly Unemployment Rates
Univariate Time Series Models
An Alternative Model
Model Comparison
Use of Initial Jobless Claims
Comparison
Exercises
References
Asset Volatility and Volatility Models
Characteristics of Volatility
Structure of a Model
Model Building
Testing for ARCH Effect
The ARCH Model
Properties of ARCH Models
Advantages and Weaknesses of ARCH Models
Building an ARCH Model
Some Examples
The GARCH Model
An Illustrative Example
Forecasting Evaluation
A Two-Pass Estimation Method
The Integrated GARCH Model
The GARCH-M Model
The Exponential Garch Model
An Illustrative Example
An Alternative Model Form
Second Example
Forecasting Using an EGARCH Model
The Threshold Garch Model
Asymmetric Power ARCH Models
Nonsymmetric GARCH Model
The Stochastic Volatility Model
Long-Memory Stochastic Volatility Models
Alternative Approaches
Use of High Frequency Data
Use of Daily Open, High, Low, and Close Prices
Exercises
References
Applications of Volatility Models
Garch Volatility Term Structure
Term Structure
Option Pricing and Hedging
Time-Varying Correlations and Betas
Time-Varying Betas
Minimum Variance Portfolios
Prediction
Exercises
References
High Frequency Financial Data
Nonsynchronous Trading
Bid-Ask Spread of Trading Prices
Empirical Characteristics of Trading Data
Models for Price Changes
Ordered Probit Model
A Decomposition Model
Duration Models
Diurnal Component
The ACD Model
Estimation
Realized Volatility
Handling Microstructure Noises
Discussion
Some Probability Distributions
Hazard Function
Exercises
References
Value at Risk
Risk Measure and Coherence
Value at Risk (VaR)
Expected Shortfall
Remarks on Calculating Risk Measures
Riskmetrics
Discussion
Multiple Positions
An Econometric Approach
Multiple Periods
Quantile Estimation
Quantile and Order Statistics
Quantile Regression
Extreme Value Theory
Review of Extreme Value Theory
Empirical Estimation
Application to Stock Returns
An Extreme Value Approach to Var
Discussion
Multiperiod VaR
Return Level
Peaks Over Thresholds
Statistical Theory
Mean Excess Function
Estimation
An Alternative Parameterization
The Stationary Loss Processes
Exercises
References
Index

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