An introduction to analysis of financial data with R
A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between the...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley
2013.
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| Edición: | 1st ed |
| Colección: | Wiley series in probability and statistics.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849114606719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright
- 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.
