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Adaptive tests of significance using permutations of residuals with R and SAS [[electronic resource] /] / Thomas W. O'Gorman
Adaptive tests of significance using permutations of residuals with R and SAS [[electronic resource] /] / Thomas W. O'Gorman
Autore O'Gorman Thomas W
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2012
Descrizione fisica 1 online resource (365 p.)
Disciplina 519.5/36
Soggetto topico Regression analysis
Computer adaptive testing
R (Computer program language)
ISBN 1-280-58894-2
1-118-21825-6
9786613618771
1-118-21822-1
Classificazione MAT029030
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Adaptive Tests of Significance Using Permutations of Residuals with R and SAS®; CONTENTS; Preface; 1 Introduction; 1.1 Why Use Adaptive Tests?; 1.2 A Brief History of Adaptive Tests; 1.2.1 Early Tests and Estimators; 1.2.2 Rank Tests; 1.2.3 The Weighted Least Squares Approach; 1.2.4 Recent Rank-Based Tests; 1.3 The Adaptive Test of Hogg, Fisher, and Randles; 1.3.1 Level of Significance of the HFR Test; 1.3.2 Comparison of Power of the HFR Test to the t Test; 1.4 Limitations of Rank-Based Tests; 1.5 The Adaptive Weighted Least Squares Approach; 1.5.1 Level of Significance
1.5.2 Comparison of Power of the Adaptive WLS Test to the t Test and the HFR Test1.6 Development of the Adaptive WLS Test; 2 Smoothing Methods and Normalizing Transformations; 2.1 Traditional Estimators of the Median and the Interquartile Range; 2.2 Percentile Estimators that Use the Smooth Cumulative Distribution Function; 2.2.1 Smoothing the Cumulative Distribution Function; 2.2.2 Using the Smoothed c.d.f. to Compute Percentiles; 2.2.3 R Code for Smoothing the c.d.f.; 2.2.4 R Code for Finding Percentiles; 2.3 Estimating the Bandwidth
2.3.1 An Estimator of Variability Based on Traditional Percentiles2.3.2 R Code for Finding the Bandwidth; 2.3.3 An Estimator of Variability Based on Percentiles from the Smoothed Distribution Function; 2.4 Normalizing Transformations; 2.4.1 Traditional Normalizing Methods; 2.4.2 Normalizing Data by Weighting; 2.5 The Weighting Algorithm; 2.5.1 An Example of the Weighing Procedure; 2.5.2 R Code for Weighting the Observations; 2.6 Computing the Bandwidth; 2.6.1 Error Distributions; 2.6.2 Measuring Errors in Adaptive Weighting; 2.6.3 Simulation Studies; 2.7 Examples of Transformed Data
Exercises3 A Two-Sample Adaptive Test; 3.1 A Two-Sample Model; 3.2 Computing the Adaptive Weights; 3.2.1 R Code for Computing the Weights; 3.3 The Test Statistics for Adaptive Tests; 3.3.1 R Code to Compute the Test Statistic; 3.4 Permutation Methods for Two-Sample Tests; 3.4.1 Permutation of Observations; 3.4.2 Permutation of Residuals; 3.4.3 R Code for Permutations; 3.5 An Example of a Two-Sample Test; 3.6 R Code for the Two-Sample Test; 3.6.1 R Code for Computing the Test Statistics; 3.6.2 R Code to Compute the Traditional F Test Statistic and p-Value
3.6.3 An R Function that Computes the p-Value for the Adaptive Test3.6.4 R Code to Perform the Adaptive Test; 3.7 Level of Significance of the Adaptive Test; 3.8 Power of the Adaptive Test; 3.9 Sample Size Estimation; 3.10 A SAS Macro for the Adaptive Test; 3.11 Modifications for One-Tailed Tests; 3.12 Justification of the Weighting Method; 3.13 Comments on the Adaptive Two-sample Test; Exercises; 4 Permutation Tests with Linear Models; 4.1 Introduction; 4.2 Notation; 4.3 Permutations with Blocking; 4.4 Linear Models in Matrix Form; 4.5 Permutation Methods; 4.5.1 The Permute-Errors Method
4.5.2 The Permute-Residuals Method
Record Nr. UNINA-9910141323703321
O'Gorman Thomas W  
Hoboken, N.J., : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Adaptive tests of significance using permutations of residuals with R and SAS / / Thomas W. O'Gorman
Adaptive tests of significance using permutations of residuals with R and SAS / / Thomas W. O'Gorman
Autore O'Gorman Thomas W
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2012
Descrizione fisica 1 online resource (365 p.)
Disciplina 519.5/36
Soggetto topico Regression analysis
Computer adaptive testing
R (Computer program language)
ISBN 9786613618771
9781280588945
1280588942
9781118218259
1118218256
9781118218228
1118218221
Classificazione MAT029030
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Adaptive Tests of Significance Using Permutations of Residuals with R and SAS®; CONTENTS; Preface; 1 Introduction; 1.1 Why Use Adaptive Tests?; 1.2 A Brief History of Adaptive Tests; 1.2.1 Early Tests and Estimators; 1.2.2 Rank Tests; 1.2.3 The Weighted Least Squares Approach; 1.2.4 Recent Rank-Based Tests; 1.3 The Adaptive Test of Hogg, Fisher, and Randles; 1.3.1 Level of Significance of the HFR Test; 1.3.2 Comparison of Power of the HFR Test to the t Test; 1.4 Limitations of Rank-Based Tests; 1.5 The Adaptive Weighted Least Squares Approach; 1.5.1 Level of Significance
1.5.2 Comparison of Power of the Adaptive WLS Test to the t Test and the HFR Test1.6 Development of the Adaptive WLS Test; 2 Smoothing Methods and Normalizing Transformations; 2.1 Traditional Estimators of the Median and the Interquartile Range; 2.2 Percentile Estimators that Use the Smooth Cumulative Distribution Function; 2.2.1 Smoothing the Cumulative Distribution Function; 2.2.2 Using the Smoothed c.d.f. to Compute Percentiles; 2.2.3 R Code for Smoothing the c.d.f.; 2.2.4 R Code for Finding Percentiles; 2.3 Estimating the Bandwidth
2.3.1 An Estimator of Variability Based on Traditional Percentiles2.3.2 R Code for Finding the Bandwidth; 2.3.3 An Estimator of Variability Based on Percentiles from the Smoothed Distribution Function; 2.4 Normalizing Transformations; 2.4.1 Traditional Normalizing Methods; 2.4.2 Normalizing Data by Weighting; 2.5 The Weighting Algorithm; 2.5.1 An Example of the Weighing Procedure; 2.5.2 R Code for Weighting the Observations; 2.6 Computing the Bandwidth; 2.6.1 Error Distributions; 2.6.2 Measuring Errors in Adaptive Weighting; 2.6.3 Simulation Studies; 2.7 Examples of Transformed Data
Exercises3 A Two-Sample Adaptive Test; 3.1 A Two-Sample Model; 3.2 Computing the Adaptive Weights; 3.2.1 R Code for Computing the Weights; 3.3 The Test Statistics for Adaptive Tests; 3.3.1 R Code to Compute the Test Statistic; 3.4 Permutation Methods for Two-Sample Tests; 3.4.1 Permutation of Observations; 3.4.2 Permutation of Residuals; 3.4.3 R Code for Permutations; 3.5 An Example of a Two-Sample Test; 3.6 R Code for the Two-Sample Test; 3.6.1 R Code for Computing the Test Statistics; 3.6.2 R Code to Compute the Traditional F Test Statistic and p-Value
3.6.3 An R Function that Computes the p-Value for the Adaptive Test3.6.4 R Code to Perform the Adaptive Test; 3.7 Level of Significance of the Adaptive Test; 3.8 Power of the Adaptive Test; 3.9 Sample Size Estimation; 3.10 A SAS Macro for the Adaptive Test; 3.11 Modifications for One-Tailed Tests; 3.12 Justification of the Weighting Method; 3.13 Comments on the Adaptive Two-sample Test; Exercises; 4 Permutation Tests with Linear Models; 4.1 Introduction; 4.2 Notation; 4.3 Permutations with Blocking; 4.4 Linear Models in Matrix Form; 4.5 Permutation Methods; 4.5.1 The Permute-Errors Method
4.5.2 The Permute-Residuals Method
Record Nr. UNINA-9910811410703321
O'Gorman Thomas W  
Hoboken, N.J., : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Autore Stirrup Jen
Edizione [1st edition]
Pubbl/distr/stampa Birmingham : , : Packt, , 2017
Descrizione fisica 1 online resource (178 pages) : illustrations
Disciplina 658.4038011
Soggetto topico R (Computer program language)
Soggetto genere / forma Electronic books.
ISBN 1-5231-2523-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910467171903321
Stirrup Jen  
Birmingham : , : Packt, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Autore Stirrup Jen
Edizione [1st edition]
Pubbl/distr/stampa Birmingham : , : Packt, , 2017
Descrizione fisica 1 online resource (178 pages) : illustrations
Disciplina 658.4038011
Soggetto topico R (Computer program language)
ISBN 1-5231-2523-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910796534703321
Stirrup Jen  
Birmingham : , : Packt, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Advanced analytics with R and Tableau : advanced visual analytical solutions for your business / / Jen Stirrup, Ruben Oliva Ramos
Autore Stirrup Jen
Edizione [1st edition]
Pubbl/distr/stampa Birmingham : , : Packt, , 2017
Descrizione fisica 1 online resource (178 pages) : illustrations
Disciplina 658.4038011
Soggetto topico R (Computer program language)
ISBN 1-5231-2523-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910827493103321
Stirrup Jen  
Birmingham : , : Packt, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R / / Bharatendra Rai
Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R / / Bharatendra Rai
Autore Rai Bharatendra
Edizione [1st edition]
Pubbl/distr/stampa Birmingham, England ; ; Mumbai : , : Packt, , [2019]
Descrizione fisica 1 online resource (vii, 337 pages) : illustrations
Disciplina 519.502855133
Soggetto topico R (Computer program language)
ISBN 1-78953-498-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910793816103321
Rai Bharatendra  
Birmingham, England ; ; Mumbai : , : Packt, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R / / Bharatendra Rai
Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R / / Bharatendra Rai
Autore Rai Bharatendra
Edizione [1st edition]
Pubbl/distr/stampa Birmingham, England ; ; Mumbai : , : Packt, , [2019]
Descrizione fisica 1 online resource (vii, 337 pages) : illustrations
Disciplina 519.502855133
Soggetto topico R (Computer program language)
ISBN 1-78953-498-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910827078003321
Rai Bharatendra  
Birmingham, England ; ; Mumbai : , : Packt, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced Object-Oriented Programming in R : Statistical Programming for Data Science, Analysis and Finance / / by Thomas Mailund
Advanced Object-Oriented Programming in R : Statistical Programming for Data Science, Analysis and Finance / / by Thomas Mailund
Autore Mailund Thomas
Edizione [1st ed. 2017.]
Pubbl/distr/stampa Berkeley, CA : , : Apress : , : Imprint : Apress, , 2017
Descrizione fisica 1 online resource (XV, 110 p. 10 illus.)
Disciplina 005.11
Soggetto topico Computer programming
Programming languages (Electronic computers)
Mathematical statistics
R (Computer program language)
Programming Techniques
Programming Languages, Compilers, Interpreters
Probability and Statistics in Computer Science
ISBN 9781484229194
1484229193
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Classes and Generic Functions -- 2. Class Hierarchies -- 3. Implementation Reuse -- 4. Statistical Models -- 5. Operator Overloading -- 6. S4 Classes -- 7. R6 Classes -- 8. Conclusions.
Record Nr. UNINA-9910254567203321
Mailund Thomas  
Berkeley, CA : , : Apress : , : Imprint : Apress, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced sampling methods / / Raosaheb Latpate [and three others]
Advanced sampling methods / / Raosaheb Latpate [and three others]
Autore Latpate Raosaheb
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (XVII, 301 p. 23 illus., 13 illus. in color.)
Disciplina 519.52
Soggetto topico Sampling (Statistics)
R (Computer program language)
Mostreig (Estadística)
Soggetto genere / forma Llibres electrònics
ISBN 981-16-0622-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -1. Introduction -- 2. Simple Random Sampling -- 3. Stratied Random Sampling -- 4. Cluster Sampling -- 5. Double Sampling -- 6. Probability Proportional to Size Sampling -- 7. Systematic Sampling -- 8. Resampling Techniques -- 9. Adaptive Cluster Sampling -- 10. Two-Stage Adaptive Cluster Sampling -- 11. Adaptive Cluster Double Sampling -- 12. Inverse Adaptive Cluster Sampling -- 13. Two Stage Inverse Adaptive Cluster Sampling -- 14. Stratified Inverse Adaptive Cluster Sampling -- 15. Negative Adaptive Cluster Sampling -- 16. Negative Adaptive Cluster Double Sampling -- 17. Two- Stage Negative Adaptive Cluster Sampling -- 18. Balanced and Unbalanced Ranked Set Sampling -- 19. Ranked Set Sampling in Other Parameter Estimation and Non-Parametric Inference -- 20. Important Versions of Ranked Set Sampling -- 21. Sampling Errors.
Record Nr. UNISA-996466397403316
Latpate Raosaheb  
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Analyzing financial data and implementing financial models using R / / Clifford S. Ang
Analyzing financial data and implementing financial models using R / / Clifford S. Ang
Autore Ang Clifford S.
Edizione [2nd ed.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (476 pages)
Disciplina 332
Collana Springer Texts in Business and Economics
Soggetto topico Risk management
Economics, Mathematical
Statistics
R (Computer program language)
ISBN 3-030-64155-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Contents -- 1 Prices -- 1.1 Price Versus Value -- 1.2 Importing Price Data from Yahoo Finance -- 1.3 Checking the Data -- 1.3.1 Check the Start and End Data -- 1.3.2 Plotting the Data -- 1.3.3 Checking the Dimension -- 1.3.4 Outputting Summary Statistics -- 1.3.5 Checking the Ticker Symbol -- 1.4 Basic Data Manipulation Techniques -- 1.4.1 Keeping and Deleting One Row -- 1.4.2 Keeping First and Last Rows -- 1.4.3 Keeping Contiguous Rows -- 1.4.4 Keeping One Column -- 1.4.5 Deleting One Column -- 1.4.6 Keeping Non-Contiguous Columns -- 1.4.7 Keeping Contiguous Columns -- 1.4.8 Keeping Contiguous and Non-Contiguous Columns -- 1.4.9 Keeping Rows and Columns -- 1.4.10 Subsetting Time Series Data Using Dates -- 1.4.11 Converting to Weekly Prices -- 1.4.12 Converting to Monthly Prices -- 1.5 Comparing Capital Gains Between Securities -- 1.5.1 Alternative 1-Using xts-Style Chart -- 1.5.2 Alternative 2-Plotting Four Mini-Charts -- 1.5.3 Alternative 3-Using ggplot to Plot FourMini-Charts -- 1.6 Simple and Exponential Moving Averages -- 1.7 Volume-Weighted Average Price -- 1.8 Plotting a Candlestick Chart -- 1.9 2-Axis Price and Volume Chart -- 1.10 Further Reading -- References -- 2 Individual Security Returns -- 2.1 Price Returns -- 2.2 Total Returns -- 2.3 Logarithmic Total Returns -- 2.4 Winsorization and Truncation -- 2.5 Cumulating Multi-Day Returns -- 2.5.1 Cumulating Arithmetic Returns -- 2.5.2 Cumulating Logarithmic Returns -- 2.5.3 Comparing Price Return and Total Return -- 2.6 Weekly Returns -- 2.7 Monthly Returns -- 2.8 Comparing Performance of Multiple Securities -- 2.8.1 Using Normalized Prices -- 2.8.2 Using Cumulative Returns -- 3 Portfolio Returns -- 3.1 Portfolio Returns the Long Way -- 3.2 Portfolio Returns Using Matrix Algebra -- 3.3 Constructing Benchmark Portfolio Returns.
3.3.1 Quarterly Returns the Long Way -- 3.3.2 Quarterly Returns the Shorter Way -- 3.3.3 Equal-Weighted Portfolio -- 3.3.4 Value-Weighted Portfolio -- 3.3.5 Daily Portfolio Returns -- 3.4 Time-Weighted Rate of Return -- 3.5 Money-Weighted Rate of Return -- 3.6 Further Reading -- Reference -- 4 Risk -- 4.1 Risk-Return Trade-Off -- 4.2 Individual Security Risk -- 4.2.1 Standard Deviation and Variance -- 4.3 Portfolio Risk -- 4.3.1 Two Assets Using Manual Approach -- 4.3.2 Two Assets Using Matrix Algebra -- 4.3.3 Multiple Assets -- 4.4 Value-at-Risk -- 4.4.1 Gaussian VaR -- 4.4.2 Historical VaR -- 4.5 Expected Shortfall -- 4.5.1 Gaussian ES -- 4.5.2 Historical ES -- 4.5.3 Comparing VaR and ES -- 4.6 Alternative Risk Measures -- 4.6.1 Parkinson -- 4.6.2 Garman-Klass -- 4.6.3 Rogers, Satchell, and Yoon -- 4.6.4 Yang and Zhang -- 4.6.5 Comparing the Risk Measures -- 4.7 Further Reading -- References -- 5 Factor Models -- 5.1 CAPM -- 5.2 Market Model -- 5.3 Rolling Window Regressions -- 5.4 Betas on Different Reference Days -- 5.5 Fama-French Three Factor Model -- 5.6 Testing for Heteroskedasticity -- 5.7 Testing for Non-Normality -- 5.8 Testing for Autocorrelation -- 5.9 Event Studies -- 5.9.1 Example: Drop in Tesla Stock After 1Q 2019 Earnings Release on April 24, 2019 -- 5.9.2 Single Step Event Study -- 5.9.3 Two Stage Event Study -- 5.9.4 Sample Quantiles/Non-Parametric -- 5.10 Selecting Best Regression Variables -- 5.10.1 Create Dataset of Returns -- 5.10.2 Forward Step Approach -- 5.10.3 Backward Step Approach -- 5.10.4 Suppressing Steps in Output -- 5.11 Further Reading -- References -- 6 Risk-Adjusted Portfolio Performance Measures -- 6.1 Portfolio and Benchmark Data -- 6.2 Sharpe Ratio -- 6.3 Roy's Safety First Ratio -- 6.4 Treynor Ratio -- 6.5 Sortino Ratio -- 6.6 Information Ratio -- 6.7 Combining Results -- 6.8 Further Reading -- References.
7 Markowitz Mean-Variance Optimization -- 7.1 Two Assets the Long Way -- 7.2 Two Assets Using Quadratic Programming -- 7.3 Multiple Assets Using Quadratic Programming -- 7.4 Effect of Allowing Short Selling -- 7.5 Further Reading -- References -- 8 Equities -- 8.1 Company Financials -- 8.2 Projections -- 8.2.1 Projecting Based on Historical Trends -- 8.2.2 Analyzing Projections Prepared by Third Parties -- 8.2.3 Analyzing Growth Rates Embedded in Projections -- 8.2.4 Analyzing Projections Using Regression Analysis -- 8.3 Equity Risk Premium -- 8.4 Unlevering Betas -- 8.5 Sensitivity Analysis -- 8.6 Relative Valuation Using Regression Analysis -- 8.7 Identifying Significant Shifts in Stock Returns -- 8.7.1 t-Test: Testing Difference in Average Returns -- 8.7.2 Identifying Breakpoints -- 8.7.3 Chow Test -- 8.7.4 Test Equality of Two Betas -- 8.8 Further Reading -- References -- 9 Fixed Income -- 9.1 Economic Analysis -- 9.1.1 Real GDP -- 9.1.2 Unemployment Rate -- 9.1.3 Inflation Rate -- 9.2 US Treasuries -- 9.2.1 Shape of the US Treasury Yield Curve -- 9.2.2 Slope of the US Treasury Yield Curve -- 9.2.3 Real Yields on US Treasuries -- 9.2.4 Expected Inflation Rates -- 9.2.5 Mean Reversion -- 9.3 Principal Components Analysis -- 9.4 Investment Grade Bond Spreads -- 9.4.1 Time Series of Spreads -- 9.4.2 Spreads and Real GDP Growth -- 9.5 Bond Valuation -- 9.5.1 Valuing Bonds with Known Yield to Maturity -- 9.5.2 Bond Valuation Function -- 9.5.3 Finding the Yield to Maturity -- 9.6 Duration and Convexity -- 9.6.1 Calculating Duration and Convexity -- 9.6.2 Duration and Convexity Functions -- 9.6.3 Comparing Estimates of Value to Full Valuation -- 9.7 Short Rate Models -- 9.7.1 Vasicek -- 9.7.2 Cox, Ingersoll, and Ross -- 9.8 Further Reading -- References -- 10 Options -- 10.1 Obtaining Options Chain Data -- 10.2 Black-Scholes-Merton Options Pricing Model.
10.2.1 BSM Function -- 10.2.2 Put-Call Parity -- 10.2.3 The Greeks -- 10.3 Implied Volatility -- 10.3.1 Implied Volatility Function -- 10.3.2 Volatility Smile -- 10.3.3 Gauging Market Risk -- 10.4 The Cox, Ross, and Rubinstein Binomial OPM -- 10.4.1 CRR: The Long Way -- 10.4.2 CRR Function -- 10.5 American Option Pricing -- 10.5.1 CRR Binomial Tree -- 10.5.2 Bjerksund-Stensland Approximation -- 10.6 Further Reading -- References -- 11 Simulation -- 11.1 Simulating Stock Prices Using Geometric Brownian Motion -- 11.1.1 Simulating Multiple Ending Stock Price Paths -- 11.1.2 Comparing Theoretical to Empirical Distributions -- 11.2 Simulating Stock Prices with and Without Dividends -- 11.3 Simulating Stocks with Correlated Prices -- 11.4 Value-at-Risk Using Simulation -- 11.5 Monte Carlo Pricing of European Options -- 11.6 Monte Carlo Option Pricing Using Antithetic Variables -- 11.7 Exotic Option Valuation -- 11.7.1 Asian Options -- 11.7.2 Lookback Options -- 11.7.3 Barrier Options -- 11.8 Further Reading -- References -- 12 Trading Strategies -- 12.1 Efficient Markets Hypothesis -- 12.1.1 Autocorrelation Test -- 12.1.2 Variance Ratio Test -- 12.1.3 Runs Test -- 12.2 Technical Analysis -- 12.2.1 Trend: Simple Moving Average Crossover -- 12.2.2 Volatility: Bollinger Bands -- 12.2.3 Momentum: Relative Strength Index -- 12.3 Building a Simple Trading Strategy -- 12.4 Machine Learning Techniques -- 12.4.1 General Steps to Apply ML -- 12.4.2 k-Nearest Neighbor Algorithm -- 12.4.3 Regression and k-Fold Cross Validation -- 12.4.4 Artificial Neural Networks -- 12.5 Further Reading -- References -- A Getting Started with R -- A.1 Installing R -- A.2 The R Working Directory -- A.3 R Console Output -- A.4 R Editor -- A.5 Packages -- A.6 Basic Commands -- A.7 The R Workspace -- A.8 Vectors -- A.9 Combining Vectors -- A.10 Matrices -- A.11 data.frame.
A.12 Date Formats -- B Pre-Loaded Code -- C Constructing a Hypothetical Portfolio (Monthly Returns) -- D Constructing a Hypothetical Portfolio (Daily Returns) -- Index.
Record Nr. UNISA-996464519103316
Ang Clifford S.  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui