Statistical analysis of financial data: with examples in R
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton
CRC Press
2020
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| Schriftenreihe: | Chapman & Hall/CRC texts in statistical science series
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xix, 645 Seiten Illustrationen |
| ISBN: | 9781138599499 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents Preface vii 1 The Nature of Financial Data 1.1 Financial Time Series ................................................................ 1.1.1 Autocorrelations............................................................. 1.1.2 Stationarity...................................................................... 1.1.3 Time Scales and Data Aggregation............................... 1.2 Financial Assets and Markets.................................................... 1.2.1 Markets and Regulatory Agencies ............................... 1.2.2 Interest............................................................................ 1.2.3 Returns on Assets.......................................................... 1.2.4 Stock Prices; Fair Market Value..................................... 1.2.5 Splits, Dividends, and Return of Capital..................... 1.2.6 Indexes and “the Market”.............................................. 1.2.7 Derivative Assets............................................................. 1.2.8 Short Positions................................................................ 1.2.9 Portfolios of Assets: Diversification and Hedging ... 1.3 Frequency Distributions of Returns ........................................ 1.3.1 Location and Scale.......................................................... 1.3.2 Skewness......................................................................... 1.3.3 Kurtosis......................................................... i............... 1.3.4 Multivariate Data.......................................................... 1.3.5 The
Normal Distribution .............................................. 1.3.6 Q-Q Plots................................. 1.3.7 Outliers............................................................................ 1.3.8 Other Statistical Measures ..................... 1.4 Volatility .................................... 1.4.1 The Time Series of Returns........................................... 1.4.2 Measuring Volatility: Historical and Implied............... 1.4.3 Volatility Indexes: The VIX........................................... 1.4.4 The Curve of Implied Volatility..................................... 1.4.5 Risk Assessment and Management............................... 1.5 Market Dynamics ................................................... 1.6 Stylized Facts about FinancialData...................... Notes and Further Reading................................................................ Exercises and Questions for Review ................................. 1 5 7 7 8 12 16 20 29 32 45 48 63 65 66 76 79 81 81 82 87 91 93 94 98 99 102 108 112 113 120 129 130 132 XV
Contents XVI Appendix Al: Accessing and Analyzing Financial Data in R ... Al.l R Basics........................................................................... Al.2 Data Repositories and Inputting Data into R............. A1.3 Time Series and Financial Data in R............................ Al.4 Data Cleansing............................................................... Notes, Comments, and Further Reading on R........................ Exercises in R............................................................................. 2 Exploratory Financial Data Analysis 2.1 Data Reduction ....................................................................... 2.1.1 Simple Summary Statistics......................................... 2.1.2 Centering and Standardizing Data.............................. 2.1.3 Simple Summary Statistics for Multivariate Data . . . 2.1.4 Transformations........................................................... 2.1.5 Identifying Outlying Observations.............................. 2.2 The Empirical Cumulative Distribution Function.................. 2.3 Nonparametric Probability Density Estimation..................... 2.3.1 Binned Data................................................................. 2.3.2 Kernel Density Estimator............................................ 2.3.3 Multivariate Kernel Density Estimator........................ 2.4 Graphical Methods in Exploratory Analysis.......................... 2.4.1 Time Series Plots........................................................... 2.4.2 Histograms...................................... 2.4.3
Boxplots.......................................................................... 2.4.4 Density Plots...................................... 2.4.5 Bivariate Data.............................................................. 2.4.6 Q-Q Plots....................................................................... 2.4.7 Graphics in R................................................................. Notes and Further Reading.............................................................. Exercises ............................................................................................ 3 Probability Distributions in Models of Observable Events 3.1 139 140 158 172 183 187 191 205 207 207 208 208 209 210 211 217 217 219 220 221 222 222 224 226 227 229 234 238 239 245 Random Variables and Probability Distributions.................. 247 3.1.1 Discrete Random Variables......................................... 248 3.1.2 Continuous Random Variables .................................... 252 3.1.3 Linear Combinations of Random Variables; Expectations and Quantiles......................................... 256 3.1.4 Survival and Hazard Functions.................................... 257 3.1.5 Multivariate Distributions............................................ 258 3.1.6 Measures of Association in Multivariate Distributions 261 3.1.7 Copulas .......................................................................... 264 3.1.8 Transformations of Multivariate Random Variables . . 267 3.1.9 Distributions of Order Statistics ................................. 269 3.1.10 Asymptotic Distributions;
The CentralLimit Theorem 270 3.1.11 The Tails of Probability Distributions . ..................... 273
Contents 4 xvii 3.1.12 Sequences of Random Variables; Stochastic Processes 3.1.13 Diffusion of Stock Prices and Pricing of Options . . . 3.2 Some Useful Probability Distributions..................................... 3.2.1 Discrete Distributions.................................................... 3.2.2 Continuous Distributions .............................................. 3.2.3 Multivariate Distributions.............................................. 3.2.4 General Families of Distributions Usefulin Modeling . 3.2.5 Constructing Multivariate Distributions..................... 3.2.6 Modeling of Data-Generating Processes..................... 3.2.7 R Functions for Probability Distributions................... 3.3 Simulating Observations of a Random Variable..................... 3.3.1 Uniform Random Numbers........................................... 3.3.2 Generating Nonuniform Random Numbers.................. 3.3.3 Simulating Data in R.................................................... Notes and FurtherReading.................................................................. Exercises .............................................................................................. 278 279 282 283 285 294 295 309 311 311 314 315 317 321 323 325 StatisticalModels andMethods of Inference 335 4.1 336 340 340 343 344 345 346 346 347 347 349 Models ........................................................................................ 4.1.1 Fitting Statistical Models.............................................. 4.1.2 Measuring and Partitioning Observed Variation .... 4.1.3 Linear Models
................................................................ 4.1.4 Nonlinear Variance-Stabilizing Transformations .... 4.1.5 Parametric and Nonparametric Models........................ 4.1.6 Bayesian Models............................................................. 4.1.7 Models for Time Series.................................................... 4.2 Criteria and Methods for Statistical Modeling ..................... 4.2.1 Estimators and Their Properties.................................. 4.2.2 Methods of Statistical Modeling .................................. 4.3 Optimization in Statistical Modeling; Least Squares and Maximum Likelihood.......................................................... 4.3.1 The General Optimization Problem ............................ 4.3.2 Least Squares................................................................... 4.3.3 Maximum Likelihood....................................................... 4.3.4 R Functions for Optimization........................................ 4.4 Statistical Inference ................................................................... 4.4.1 Confidence Intervals ........................................................ 4.4.2 Testing Statistical Hypotheses ..................................... 4.4.3 Prediction......................................................................... 4.4.4 Inference in Bayesian Models........................................ 4.4.5 Resampling Methods; The Bootstrap............................ 4.4.6 Robust Statistical Methods........................................... 4.4.7 Estimation of
the Tail Index ........................................ 4.4.8 Estimation of VaR and Expected Shortfall................... 4.5 Models of Relationships among Variables . . . ...................... 358 358 363 371 375 376 379 381 385 386 393 396 399 404 408
xviii Contents 4.5.1 4.5.2 4.5.3 4.5.4 4.5.5 Principal Components.................................................... Regression Models.......................................................... Linear Regression Models.............................................. Linear Regression Models: The Regressors.................. Linear Regression Models: Individual Observations and Residuals.......................................................................... 4.5.6 Linear Regression Models: An Example...................... 4.5.7 Nonlinear Models............................................................. 4.5.8 Specifying Models in R ................................................. 4.6 Assessing the Adequacy of Models........................................... 4.6.1 Goodness-of-Fit Tests; Tests for Normality............... 4.6.2 Cross-Validation .............................................................. 4.6.3 Model Selection and Model Complexity...................... Notes and Further Reading................................................................. Exercises ............................................................................................... 409 413 418 422 428 435 449 454 455 456 463 467 469 472 5 Discrete Time Series Models and Analysis 487 5.1 Basic Linear Operations............................................................. 495 5.1.1 The Backshift Operator................................................. 495 5.1.2 The Difference Operator................................................. 497 5.1.3 The Integration
Operator.............................................. 500 5.1.4 Summation of an Infinite Geometric Series ............... 500 5.1.5 Linear Difference Equations ............................................ 501 5.1.6 Trends and Detrending ................................................. 505 5.1.7 Cycles and Seasonal Adjustment . ............................... 508 5.2 Analysis of Discrete Time Series Models.................................. 510 5.2.1 Stationarity....................................................................... 514 5.2.2 Sample Autocovariance and Autocorrelation Functions; Stationarity and Estimation........................................... 518 5.2.3 Statistical Inference in Stationary Time Series............ 523 5.3 Autoregressive and Moving Average Models ......................... 528 5.3.1 Moving Average Models; MA(q) .................................. 529 5.3.2 Autoregressive Models; AR(p)........................................ 534 5.3.3 The Partial Autocorrelation Function, PACF............ 547 5.3.4 ARMA and ARIMA Models........................................... 549 5.3.5 Simulation of ARMA and ARIMA Models.................. 555 5.3.6 Statistical Inference in ARMA and ARIMA Models . 556 5.3.7 Selection of Orders in ARIMA Models......................... 560 5.3.8 Forecasting in ARIMA Models..................................... 561 5.3.9 Analysis of ARMA and ARIMA Models in R............ 561 5.3.10 Robustness of ARMA Procedures; Innovations with Heavy Tails....................................................................... 566 5.3.11
Financial Data.............................................. 568 5.3.12 Linear Regression with ARMA Errors......................... 571 5.4 Conditional Heteroscedasticity ........................... 575
Contents 5.4.1 ARCH Models................................................................ 5.4.2 GARCH Models and Extensions.................................. 5.5 Unit RootsandCointegration ................................................. 5.5.1 Spurious Correlations; TheDistribution of the Correlation Coefficient.................................................... 5.5.2 Unit Roots...................................................................... 5.5.3 Cointegrated Processes.................................................... Notes andFurther Reading................................................................. Exercises ......................................................................................... xix 576 580 584 584 592 599 603 604 References 615 Index 623
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| adam_txt |
Contents Preface vii 1 The Nature of Financial Data 1.1 Financial Time Series . 1.1.1 Autocorrelations. 1.1.2 Stationarity. 1.1.3 Time Scales and Data Aggregation. 1.2 Financial Assets and Markets. 1.2.1 Markets and Regulatory Agencies . 1.2.2 Interest. 1.2.3 Returns on Assets. 1.2.4 Stock Prices; Fair Market Value. 1.2.5 Splits, Dividends, and Return of Capital. 1.2.6 Indexes and “the Market”. 1.2.7 Derivative Assets. 1.2.8 Short Positions. 1.2.9 Portfolios of Assets: Diversification and Hedging . 1.3 Frequency Distributions of Returns . 1.3.1 Location and Scale. 1.3.2 Skewness. 1.3.3 Kurtosis. i. 1.3.4 Multivariate Data. 1.3.5 The
Normal Distribution . 1.3.6 Q-Q Plots. 1.3.7 Outliers. 1.3.8 Other Statistical Measures . 1.4 Volatility . 1.4.1 The Time Series of Returns. 1.4.2 Measuring Volatility: Historical and Implied. 1.4.3 Volatility Indexes: The VIX. 1.4.4 The Curve of Implied Volatility. 1.4.5 Risk Assessment and Management. 1.5 Market Dynamics . 1.6 Stylized Facts about FinancialData. Notes and Further Reading. Exercises and Questions for Review . 1 5 7 7 8 12 16 20 29 32 45 48 63 65 66 76 79 81 81 82 87 91 93 94 98 99 102 108 112 113 120 129 130 132 XV
Contents XVI Appendix Al: Accessing and Analyzing Financial Data in R . Al.l R Basics. Al.2 Data Repositories and Inputting Data into R. A1.3 Time Series and Financial Data in R. Al.4 Data Cleansing. Notes, Comments, and Further Reading on R. Exercises in R. 2 Exploratory Financial Data Analysis 2.1 Data Reduction . 2.1.1 Simple Summary Statistics. 2.1.2 Centering and Standardizing Data. 2.1.3 Simple Summary Statistics for Multivariate Data . . . 2.1.4 Transformations. 2.1.5 Identifying Outlying Observations. 2.2 The Empirical Cumulative Distribution Function. 2.3 Nonparametric Probability Density Estimation. 2.3.1 Binned Data. 2.3.2 Kernel Density Estimator. 2.3.3 Multivariate Kernel Density Estimator. 2.4 Graphical Methods in Exploratory Analysis. 2.4.1 Time Series Plots. 2.4.2 Histograms. 2.4.3
Boxplots. 2.4.4 Density Plots. 2.4.5 Bivariate Data. 2.4.6 Q-Q Plots. 2.4.7 Graphics in R. Notes and Further Reading. Exercises . 3 Probability Distributions in Models of Observable Events 3.1 139 140 158 172 183 187 191 205 207 207 208 208 209 210 211 217 217 219 220 221 222 222 224 226 227 229 234 238 239 245 Random Variables and Probability Distributions. 247 3.1.1 Discrete Random Variables. 248 3.1.2 Continuous Random Variables . 252 3.1.3 Linear Combinations of Random Variables; Expectations and Quantiles. 256 3.1.4 Survival and Hazard Functions. 257 3.1.5 Multivariate Distributions. 258 3.1.6 Measures of Association in Multivariate Distributions 261 3.1.7 Copulas . 264 3.1.8 Transformations of Multivariate Random Variables . . 267 3.1.9 Distributions of Order Statistics . 269 3.1.10 Asymptotic Distributions;
The CentralLimit Theorem 270 3.1.11 The Tails of Probability Distributions . . 273
Contents 4 xvii 3.1.12 Sequences of Random Variables; Stochastic Processes 3.1.13 Diffusion of Stock Prices and Pricing of Options . . . 3.2 Some Useful Probability Distributions. 3.2.1 Discrete Distributions. 3.2.2 Continuous Distributions . 3.2.3 Multivariate Distributions. 3.2.4 General Families of Distributions Usefulin Modeling . 3.2.5 Constructing Multivariate Distributions. 3.2.6 Modeling of Data-Generating Processes. 3.2.7 R Functions for Probability Distributions. 3.3 Simulating Observations of a Random Variable. 3.3.1 Uniform Random Numbers. 3.3.2 Generating Nonuniform Random Numbers. 3.3.3 Simulating Data in R. Notes and FurtherReading. Exercises . 278 279 282 283 285 294 295 309 311 311 314 315 317 321 323 325 StatisticalModels andMethods of Inference 335 4.1 336 340 340 343 344 345 346 346 347 347 349 Models . 4.1.1 Fitting Statistical Models. 4.1.2 Measuring and Partitioning Observed Variation . 4.1.3 Linear Models
. 4.1.4 Nonlinear Variance-Stabilizing Transformations . 4.1.5 Parametric and Nonparametric Models. 4.1.6 Bayesian Models. 4.1.7 Models for Time Series. 4.2 Criteria and Methods for Statistical Modeling . 4.2.1 Estimators and Their Properties. 4.2.2 Methods of Statistical Modeling . 4.3 Optimization in Statistical Modeling; Least Squares and Maximum Likelihood. 4.3.1 The General Optimization Problem . 4.3.2 Least Squares. 4.3.3 Maximum Likelihood. 4.3.4 R Functions for Optimization. 4.4 Statistical Inference . 4.4.1 Confidence Intervals . 4.4.2 Testing Statistical Hypotheses . 4.4.3 Prediction. 4.4.4 Inference in Bayesian Models. 4.4.5 Resampling Methods; The Bootstrap. 4.4.6 Robust Statistical Methods. 4.4.7 Estimation of
the Tail Index . 4.4.8 Estimation of VaR and Expected Shortfall. 4.5 Models of Relationships among Variables . . . . 358 358 363 371 375 376 379 381 385 386 393 396 399 404 408
xviii Contents 4.5.1 4.5.2 4.5.3 4.5.4 4.5.5 Principal Components. Regression Models. Linear Regression Models. Linear Regression Models: The Regressors. Linear Regression Models: Individual Observations and Residuals. 4.5.6 Linear Regression Models: An Example. 4.5.7 Nonlinear Models. 4.5.8 Specifying Models in R . 4.6 Assessing the Adequacy of Models. 4.6.1 Goodness-of-Fit Tests; Tests for Normality. 4.6.2 Cross-Validation . 4.6.3 Model Selection and Model Complexity. Notes and Further Reading. Exercises . 409 413 418 422 428 435 449 454 455 456 463 467 469 472 5 Discrete Time Series Models and Analysis 487 5.1 Basic Linear Operations. 495 5.1.1 The Backshift Operator. 495 5.1.2 The Difference Operator. 497 5.1.3 The Integration
Operator. 500 5.1.4 Summation of an Infinite Geometric Series . 500 5.1.5 Linear Difference Equations . 501 5.1.6 Trends and Detrending . 505 5.1.7 Cycles and Seasonal Adjustment . . 508 5.2 Analysis of Discrete Time Series Models. 510 5.2.1 Stationarity. 514 5.2.2 Sample Autocovariance and Autocorrelation Functions; Stationarity and Estimation. 518 5.2.3 Statistical Inference in Stationary Time Series. 523 5.3 Autoregressive and Moving Average Models . 528 5.3.1 Moving Average Models; MA(q) . 529 5.3.2 Autoregressive Models; AR(p). 534 5.3.3 The Partial Autocorrelation Function, PACF. 547 5.3.4 ARMA and ARIMA Models. 549 5.3.5 Simulation of ARMA and ARIMA Models. 555 5.3.6 Statistical Inference in ARMA and ARIMA Models . 556 5.3.7 Selection of Orders in ARIMA Models. 560 5.3.8 Forecasting in ARIMA Models. 561 5.3.9 Analysis of ARMA and ARIMA Models in R. 561 5.3.10 Robustness of ARMA Procedures; Innovations with Heavy Tails. 566 5.3.11
Financial Data. 568 5.3.12 Linear Regression with ARMA Errors. 571 5.4 Conditional Heteroscedasticity . 575
Contents 5.4.1 ARCH Models. 5.4.2 GARCH Models and Extensions. 5.5 Unit RootsandCointegration . 5.5.1 Spurious Correlations; TheDistribution of the Correlation Coefficient. 5.5.2 Unit Roots. 5.5.3 Cointegrated Processes. Notes andFurther Reading. Exercises . xix 576 580 584 584 592 599 603 604 References 615 Index 623 |
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| isbn | 9781138599499 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032067865 |
| oclc_num | 1155089148 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-739 |
| owner_facet | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-739 |
| physical | xix, 645 Seiten Illustrationen |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Chapman & Hall/CRC texts in statistical science series |
| spelling | Gentle, James E. 1943- Verfasser (DE-588)126727031 aut Statistical analysis of financial data with examples in R James E. Gentle Boca Raton CRC Press 2020 xix, 645 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC texts in statistical science series Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Kreditmarkt (DE-588)4073788-3 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Finance / Mathematical models Finance / Econometric models R (Computer program language) BUSINESS & ECONOMICS / Operations Research MATHEMATICS / General MATHEMATICS / Probability & Statistics / General (DE-588)4123623-3 Lehrbuch gnd-content Kreditmarkt (DE-588)4073788-3 s Statistische Analyse (DE-588)4116599-8 s R Programm (DE-588)4705956-4 s b DE-604 Erscheint auch als Online-Ausgabe 978-0-429-48560-2 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032067865&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gentle, James E. 1943- Statistical analysis of financial data with examples in R Statistische Analyse (DE-588)4116599-8 gnd Kreditmarkt (DE-588)4073788-3 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4116599-8 (DE-588)4073788-3 (DE-588)4705956-4 (DE-588)4123623-3 |
| title | Statistical analysis of financial data with examples in R |
| title_auth | Statistical analysis of financial data with examples in R |
| title_exact_search | Statistical analysis of financial data with examples in R |
| title_exact_search_txtP | Statistical analysis of financial data with examples in R |
| title_full | Statistical analysis of financial data with examples in R James E. Gentle |
| title_fullStr | Statistical analysis of financial data with examples in R James E. Gentle |
| title_full_unstemmed | Statistical analysis of financial data with examples in R James E. Gentle |
| title_short | Statistical analysis of financial data |
| title_sort | statistical analysis of financial data with examples in r |
| title_sub | with examples in R |
| topic | Statistische Analyse (DE-588)4116599-8 gnd Kreditmarkt (DE-588)4073788-3 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Statistische Analyse Kreditmarkt R Programm Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032067865&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT gentlejamese statisticalanalysisoffinancialdatawithexamplesinr |