An introduction to analysis of financial data with R /
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| Author / Creator: | Tsay, Ruey S., 1951- |
|---|---|
| Imprint: | Hoboken, N.J. : Wiley, c2013. |
| Description: | xiv, 390 p. : ill. ; 25 cm. |
| Language: | English |
| Series: | Wiley series in probability in statistics Wiley series in probability and statistics. |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/8943612 |
Table of Contents:
- Preface
- 1. Financial Data and their Properties
- 1.1. Asset Returns
- 1.2. Bond Yields and Prices
- 1.3. Implied Volatility
- 1.4. R Packages and Demonstrations
- 1.4.1. Installation of R Packages
- 1.4.2. The Quantmod Package
- 1.4.3. Some Basic R Commands
- 1.5. Examples of Financial Data
- 1.6. Distributional Properties of Returns
- 1.6.1. Review of Statistical Distributions and Their Moments
- 1.7. Visualization of Financial Data
- 1.8. Some Statistical Distributions
- 1.8.1. Normal Distribution
- 1.8.2. Lognormal Distribution
- 1.8.3. Stable Distribution
- 1.8.4. Scale Mixture of Normal Distributions
- 1.8.5. Multivariate Returns
- Exercises
- References
- 2. Linear Models for Financial Time Series
- 2.1. Stationarity
- 2.2. Correlation and Autocorrelation Function
- 2.3. White Noise and Linear Time Series
- 2.4. Simple Autoregressive Models
- 2.4.1. Properties of AR Models
- 2.4.2. Identifying AR Models in Practice
- 2.4.3. Goodness of Fit
- 2.4.4. Forecasting
- 2.5. Simple Moving Average Models
- 2.5.1. Properties of MA Models
- 2.5.2. Identifying MA Order
- 2.5.3. Estimation
- 2.5.4. Forecasting Using MA Models
- 2.6. Simple ARMA Models
- 2.6.1. Properties of ARMA (1,1) Models
- 2.6.2. General ARMA Models
- 2.6.3. Identifying ARMA Models
- 2.6.4. Forecasting Using an ARMA Model
- 2.6.5. Three Model Representations for an ARMA Model
- 2.7. Unit-Root Nonstationarity
- 2.7.1. Random Walk
- 2.7.2. Random Walk with Drift
- 2.7.3. Trend-Stationary Time Series
- 2.7.4. General Unit-Root Nonstationary Models
- 2.7.5. Unit-Root Test
- 2.8. Exponential Smoothing
- 2.9. Seasonal Models
- 2.9.1. Seasonal Differencing
- 2.9.2. Multiplicative Seasonal Models
- 2.9.3. Seasonal Dummy Variable
- 2.10. Regression Models with Time Series Errors
- 2.11. Long-Memory Models
- 2.12. Model Comparison and Averaging
- 2.12.1. In-sample Comparison
- 2.12.2. Out-of-sample Comparison
- 2.12.3. Model Averaging
- Exercises
- References
- 3. Case Studies of Linear Time Series
- 3.1. Weekly Regular Gasoline Price
- 3.1.1. Pure Time Series Model
- 3.1.2. Use of Crude Oil Prices
- 3.1.3. Use of Lagged Crude Oil Prices
- 3.1.4. Out-of-Sample Predictions
- 3.2. Global Temperature Anomalies
- 3.2.1. Unit-Root Stationarity
- 3.2.2. Trend-Nonstationarity
- 3.2.3. Model Comparison
- 3.2.4. Long-Term Prediction
- 3.2.5. Discussion
- 3.3. US Monthly Unemployment Rates
- 3.3.1. Univariate Time Series Models
- 3.3.2. An Alternative Model
- 3.3.3. Model Comparison
- 3.3.4. Use of Initial Jobless Claims
- 3.3.5. Comparison
- Exercises
- References
- 4. Asset Volatility and Volatility Models
- 4.1. Characteristics of Volatility
- 4.2. Structure of a Model
- 4.3. Model Building
- 4.4. Testing for ARCH Effect
- 4.5. The ARCH Model
- 4.5.1. Properties of ARCH Models
- 4.5.2. Advantages and Weaknesses of ARCH Models
- 4.5.3. Building an ARCH Model
- 4.5.4. Some Examples
- 4.6. The GARCH Model
- 4.6.1. An Illustrative Example
- 4.6.2. Forecasting Evaluation
- 4.6.3. A Two-Pass Estimation Method
- 4.7. The Integrated GARCH Model
- 4.8. The GARCH-M Model
- 4.9. The Exponential Garch Model
- 4.9.1. An Illustrative Example
- 4.9.2. An Alternative Model Form
- 4.9.3. Second Example
- 4.9.4. Forecasting Using an EGARCH Model
- 4.10. The Threshold Garch Model
- 4.11. Asymmetric Power ARCH Models
- 4.12. Nonsymmetric GARCH Model
- 4.13. The Stochastic Volatility Model
- 4.14. Long-Memory Stochastic Volatility Models
- 4.15. Alternative Approaches
- 4.15.1. Use of High Frequency Data
- 4.15.2. Use of Daily Open, High, Low, and Close Prices
- Exercises
- References
- 5. Applications of Volatility Models
- 5.1. Garch Volatility Term Structure
- 5.1.1. Term Structure
- 5.2. Option Pricing and Hedging
- 5.3. Time-Varying Correlations and Betas
- 5.3.1. Time-Varying Betas
- 5.4. Minimum Variance Portfolios
- 5.5. Prediction
- Exercises
- References
- 6. High Frequency Financial Data
- 6.1. Nonsynchronous Trading
- 6.2. Bid-Ask Spread of Trading Prices
- 6.3. Empirical Characteristics of Trading Data
- 6.4. Models for Price Changes
- 6.4.1. Ordered Probit Model
- 6.4.2. A Decomposition Model
- 6.5. Duration Models
- 6.5.1. Diurnal Component
- 6.5.2. The ACD Model
- 6.5.3. Estimation
- 6.6. Realized Volatility
- 6.6.1. Handling Microstructure Noises
- 6.6.2. Discussion
- Appendix A. Some Probability Distributions
- Appendix B. Hazard Function
- Exercises
- References
- 7. Value at Risk
- 7.1. Risk Measure and Coherence
- 7.1.1. Value at Risk (VaR)
- 7.1.2. Expected Shortfall
- 7.2. Remarks on Calculating Risk Measures
- 7.3. Riskmetrics
- 7.3.1. Discussion
- 7.3.2. Multiple Positions
- 7.4. An Econometric Approach
- 7.4.1. Multiple Periods
- 7.5. Quantile Estimation
- 7.5.1. Quantile and Order Statistics
- 7.5.2. Quantile Regression
- 7.6. Extreme Value Theory
- 7.6.1. Review of Extreme Value Theory
- 7.6.2. Empirical Estimation
- 7.6.3. Application to Stock Returns
- 7.7. An Extreme Value Approach to Var
- 7.7.1. Discussion
- 7.7.2. Multiperiod VaR
- 7.7.3. Return Level
- 7.8. Peaks Over Thresholds
- 7.8.1. Statistical Theory
- 7.8.2. Mean Excess Function
- 7.8.3. Estimation
- 7.8.4. An Alternative Parameterization
- 7.9. The Stationary Loss Processes
- Exercises
- References
- Index
