Financial data analytics with R: Monte-Carlo validation
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton
CRC Press, Taylor & Francis Group
2025
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| Edition: | First edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxii, 275 Seiten Illustrationen, Diagramme |
| ISBN: | 9781032741499 9781032745114 |
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MARC
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Contents List of Figures xi List of Tables xili Preface xv About the Author xxi 1Introduction to R 1.1 What is R?. 1.2 Steps on Installing R and Updating R Packages . 1.2.1 First Step: Install R Base System. 1.2.2 Second Step: Installing and Updating R Packages . . . 1.2.3 Steps to Get Help and Documentation. 1.3 Database Management and Data Manipulations . 1.3.1 Data Management with RMySQL . 1.3.2 Data Management with Microsoft Excel and R Package gdata. 1.3.3 Data Management with Microsoft Excel and R Package xlsx . 1.3.4 Data Management with Microsoft Excel and R Package readxl. 1.3.5 Default Methods to Read Data into R. 1.3.6 R Package foreign. 1.4 Monte-Carlo Simulation Illustration . 1.4.1 Central Limit Theorem. 1.4.2 R Capacity on Random Number Generation. 1.4.3 R to Prove (Empirically) the Central Limit Theorem. 1.5 Summary and Recommendations for Further Reading . 1.6
Exercises . 2Linear Regression 2.1 Descriptive Data Analysis. 2.2 Review for Multiple Linear Regression Models . 2.2.1 Multiple Linear Regression Model . 1 1 3 3 4 5 7 7 7 8 8 8 9 10 10 12 12 19 20 21 21 24 24
Contents 2.3 2.4 2.5 2.6 2.7 2.2.2 The Method of Least Squares Estimation (LSE) . . . 2.2.3 The Properties of LSE. 2.2.4 The Fundamental Assumptions. Simple Linear Regression: One Independent Variable . 2.3.1 Model Fitting. 2.3.2 Model Diagnostics. Multiple Linear Regression . 2.4.1 Model Fitting. 2.4.2 Regression Model Diagnostics: Checking Assumptions Model Diagnostics with Monte-Carlo Simulations . 2.5.1 Violation of Normality of Residuals. 2.5.2 Violation of Homoscedasticity. Discussions. Exercises . 3 Transition from Linear to Nonlinear Regression 3.1 The Financial Compounding Model . 3.1.1 The Basic Concept of Compounding. 3.1.2 Data Available. 3.1.3 Computation and R Implementation. 3.2 Depreciation of Non-Current Assets . 3.2.1 The Reduced Balance Method . 3.2.2
Data Available. 3.2.3 Computation and R Implementation. 3.3 Pros and Cons of Linearization. 3.3.1 Theoretical Differences in Residual Patterns. 3.3.2 Numerical Differences in Residual Patterns. 3.3.3 Additive Errors and Multiplicative Errors. 3.4 Discussions. 3.5 Exercises . 4 Nonlinear Regression Modeling 4.1 Logistic GrowthModel . 4.2 Theory of NonlinearRegression . 4.2.1 R Packages for Nonlinear Regression. 4.2.2 R Function nls for Nonlinear Regression. 4.2.3 Theory of Nonlinear Regression in nls. 4.3 Nonlinear Regression for Mobile Banking . 4.3.1 Data on Customer Size on Mobile Banking. 4.3.2 Fitting the Mobile Banking Data Using R Function nls 4.3.3 Possible Consequences Shown in dMobile Data . 4.4 Monte-Carlo Simulation-Based Investigation . 4.4.1 Simulation Setup. 4.4.2 Numerical Investigation with Monte-Carlo Simulations 25 26 28 29 29 31 32 33 35 40 40 49 57 57 59 59 60 61 63 65 65 66 67 7o 71 72 77 SO 80 81 81 83
83 84 85 86 86 88 92 93 93 94
vii Contents 4.5 4.6 Discussions. Exercises . 5 The Logistic Regression 5.1 5.2 5.3 5.4 5.5 6.2 6.3 6.4 6.5 7.2 124 Why Poisson Regression to Model Count Data. 125 6.1.1 Data Description. 125 6.1.2 Why Not Linear Regression. 127 6.1.3 Why Not the Log-Linear Regression. 129 Poisson Regression. 134 6.2.1 Conventional Poisson Regression. 134 6.2.2 Models for Overdispersed Count Data. 136 6.2.3 Models for Underdispersed Count Data.141 Monte-Carlo Simulation Study. 143 6.3.1 Scenerio 1: No Dispersion. 144 6.3.2 Scenario 2: Overdispersion. 147 6.3.3 Scenario 3: Underdispersion. 151 Discussions. 155 Exercises . 156 7 Autoregressive Integrated Moving-Average Models 7.1 101 The Logistic Regression. 101 5.1.1 Common Misconceptions
. 101 5.1.2 Logistic Regression with MLE. 103 S P Stock Market Data Analysis . 105 5.2.1 Descriptive Data Analysis. 105 5.2.2 Model Fitting with Logistic Regression. 108 5.2.3 Prediction using Logistic Regression. Ill Monte-Carlo Simulation-Based Validation Study. 112 5.3.1 Simulation Design. 113 5.3.2 Simulation Implementation in R. 114 5.3.3 A Full-Scale Monte-Carlo SimulationValidation. . . . 118 Discussions. 121 Exercises . 122 6 The Poisson Regression: Models for Count Data 6.1 98 99 157 Autocorrelation Function and Partial Autocorrelation Function 158 7.1.1 Autocorrelation Function. 158 7.1.2 Partial Autocorrelation Function. 158 7.1.3 Ljung-Box Test for Significance. 159 7.1.4 Illustration with Wages Data. 160 Auto-Regressive Integrated Moving-Average Time Series . 161 7.2.1 Auto-Regressive Process with Order p. 161 7.2.2 Moving-Average Process with Order q. 162 7.2.3 ARMA Process
with Order of p and q. 163
νϋί Conte7its 7.3 7.4 7.5 7.6 8 7.2.4 Auto-Regressive Integrated Moving-Average Process . 163 7.2.5 ARIMA with Regression. 164 Fitting ARIMA using R. 165 7.3.1 Fitting AR(p) Model. 165 7.3.2 Fitting MA(q) Models. 168 7.3.3 Fitting ARIMA model. 169 7.3.4 Fitting Time Scries Regression. 171 Monte-CarloSimulation Validation . 173 7.4.1 Simulation Design. 174 7.4.2 Simulation Implementation. 175 7.4.3 Summary. 180 Discussions. ISO 7.5.1 Stationary Process. 181 7.5.2 White Noise Process is a Stationary Process. 182 7.5.3 Random Walk Process is a Non-Stationary Process . . 182 7.5.4 Stationary to Heteroskedasticity . 182 Exercises . 183 Generalized AutoRegressive Conditional Heteroskedasticity Model 185 8.1 The Volatility Index Data. ISO
8.1.1 The Volatility Index BrieflyExplained. 186 8.1.2 The VIX Data . 187 8.1.3 Descriptive Analysis. 188 8.1.4 ARIMA Modeling . 1W 8.2 The GARCH Modeling . 193 8.2.1 From General Regression to ARCHModels. 193 8.2.2 From ARCH to GARCH . 195 8.2.3 R Packages. 196 8.3 Data Analysis with GARCH Modeling . 197 8.3.1 Model Fitting. 198 8.3.2 Summary of Model Fitting . 214 8.4 Discussions and Recommendations . 218 8.4.1 Discussions. 218 8.4.2 Recommendations. 219 8.5 Exercises . 221 9 Cointegration 222 What R Packages for CoRRegration . 222 Statistical Methods . 223 9.2.1 The Augmented Dickey-Fuller Test for Stationarity . . 223 9.2.2 Johansen Test Procedure . 224
9.3 Standard Poor Data . 226 9.3.1 Descriptive Analysis. 226 9.3.2 Test for Non-Stationarity. 227 9.1 9.2
ix Contents 9.3.3 Cointegration Regression . 230 9.3.4 Vector Error Correction Model. 233 9.4 The Volatility Index Data. 238 9.4.1 Descriptive Analysis. 238 9.4.2 Test for Non-Stationarity. 240 9.4.3 Vector Error Correction Model. 244 9.5 Discussions. 249 9.5.1 How to Do Cointegration in R. 250 9.5.2 Cointegration in Financial Analysis. 251 9.5.3 Cointegration in Investment among Stocks to Mutual Funds. 252 10 Financial Statistical Modeling in Risk and Wealth Management 254 10.1 Statistical Modeling in Risk Management. 254 10.1.1 Understanding Risk. 254 10.1.2 Key Financial Statistics. 255 10.1.3 Statistical Tools and Models . 258 10.2 Statistical Modeling in Wealth Management . 259 10.2.1 The Role of Financial Statistical Modeling. 259 10.2.2 Key Statistical Models in Wealth Management . 260 10.2.3 Portfolio Construction and Optimization. 261 10.3 Discussions and
Recommendations. 262 Bibliography 265 Index 269 |
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| language | English |
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| spelling | Chen, Jenny K. Verfasser (DE-588)1239446888 aut Financial data analytics with R Monte-Carlo validation Jenny K. Chen First edition Boca Raton CRC Press, Taylor & Francis Group 2025 xxii, 275 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Finanzanalyse (DE-588)4133000-6 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Finanzanalyse (DE-588)4133000-6 s Mathematisches Modell (DE-588)4114528-8 s Ökonometrisches Modell (DE-588)4043212-9 s R Programm (DE-588)4705956-4 s b DE-604 Erscheint auch als 978-1-003-46970-4 Online-Ausgabe 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=035218246&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Chen, Jenny K. Financial data analytics with R Monte-Carlo validation Finanzanalyse (DE-588)4133000-6 gnd R Programm (DE-588)4705956-4 gnd Mathematisches Modell (DE-588)4114528-8 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| subject_GND | (DE-588)4133000-6 (DE-588)4705956-4 (DE-588)4114528-8 (DE-588)4043212-9 |
| title | Financial data analytics with R Monte-Carlo validation |
| title_auth | Financial data analytics with R Monte-Carlo validation |
| title_exact_search | Financial data analytics with R Monte-Carlo validation |
| title_full | Financial data analytics with R Monte-Carlo validation Jenny K. Chen |
| title_fullStr | Financial data analytics with R Monte-Carlo validation Jenny K. Chen |
| title_full_unstemmed | Financial data analytics with R Monte-Carlo validation Jenny K. Chen |
| title_short | Financial data analytics with R |
| title_sort | financial data analytics with r monte carlo validation |
| title_sub | Monte-Carlo validation |
| topic | Finanzanalyse (DE-588)4133000-6 gnd R Programm (DE-588)4705956-4 gnd Mathematisches Modell (DE-588)4114528-8 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| topic_facet | Finanzanalyse R Programm Mathematisches Modell Ökonometrisches Modell |
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