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Table of Contents
Intro; Preface; Contents; Part I The YUIMA Framework; 1 The YUIMA Package; 1.1 Overview of the Project; 1.2 Who Should Read This Book?; 1.3 Structure of the Book; 1.4 How to Get the R Code for This Book; 1.5 Main Contribution to the Yuima Package; 1.6 Further Developments of Yuima Package; 1.7 Things to Know About R; 1.7.1 How to Get R; 1.7.2 R and S4 Objects; 1.8 The Yuima Package; 1.8.1 How to Obtain the Package; 1.8.2 The Main Object and Classes; 1.8.3 The yuima.model Class; 1.9 On Model Specification; 1.9.1 Basic Model Specification; 1.9.2 User-Specified State and Time Variables
1.9.3 Specification of Parametric Models1.10 Basic Facts on Simulation; 1.10.1 Customization of Simulation Arguments; 1.10.2 Simulation of Models with User-Specified Notation; 1.10.3 Simulation of Parametric Models; 1.11 Sampling and Simulate; 1.11.1 Sampling and Subsampling; 1.12 How to Make Data Available into a yuima Object; 1.12.1 Getting Data from Data Providers; 1.13 How to Extract Data from a yuima Object; 1.14 Time Series Classes, Time Data and Time Stamps; 1.14.1 Review of Some Time Series Objects in R; 1.14.2 How to Handle Real Time Stamps; 1.14.3 Dates Manipulation
1.14.4 Using Dates to Index Time Series1.14.5 Joining Two or More Time Series; 1.14.6 Subsetting a Time Series; 1.15 Miscellanea; 1.15.1 From Yuima to LaTeX; 1.15.2 The Yuima GUI; Part II Models and Inference; 2 Diffusion Processes; 2.1 Model Specification; 2.1.1 Ornstein-Uhlenbeck (OU); 2.1.2 Geometric Brownian Motion (gBm); 2.1.3 Vasicek Model (VAS); 2.1.4 Constant Elasticity of Variance (CEV); 2.1.5 Cox-Ingersoll-Ross Process (CIR); 2.1.6 Chan-Karolyi-Longstaff-Sanders Process (CKLS); 2.1.7 Hyperbolic Diffusion Processes; 2.2 More About Simulation; 2.3 Multidimensional Processes
2.3.1 The Heston Model2.4 Parametric Inference; 2.4.1 Quasi-maximum Likelihood Estimation; 2.4.2 Adaptive Bayes Estimation; 2.5 Example of Real Data Estimation for gBm; 2.6 Example of Real Data Estimation for CIR; 2.7 Hypotheses Testing; 2.8 AIC Model Selection; 2.8.1 An Example of AIC Model Selection for Exchange Rates Data; 2.9 LASSO Model Selection; 2.9.1 An Example of Lasso Model Selection for Interest Rates Data; 2.10 Change Point Estimation; 2.10.1 Example of Volatility Change Point Estimation for Two-Dimensional SDEs; 2.10.2 An Example of Two-Stage Estimation
2.10.3 Example of Volatility Change Point Estimation in Real Data2.11 Asynchronous Covariance Estimation; 2.11.1 Example: Data Generation and Estimation by yuima Package; 2.11.2 Asynchronous Estimation for Nonlinear Systems; 2.11.3 Other Covariance Estimators; 2.12 Lead-Lag Estimation; 2.12.1 Application of the Lead-Lag Estimator to Real Data; 2.13 Asymptotic Expansion; 2.13.1 Asymptotic Expansion for General Stochastic Processes; 3 Compound Poisson Processes; 3.1 Inhomogeneous Compound Poisson Process; 3.1.1 Linear Intensity Function; 3.1.2 The Weibull Model
1.9.3 Specification of Parametric Models1.10 Basic Facts on Simulation; 1.10.1 Customization of Simulation Arguments; 1.10.2 Simulation of Models with User-Specified Notation; 1.10.3 Simulation of Parametric Models; 1.11 Sampling and Simulate; 1.11.1 Sampling and Subsampling; 1.12 How to Make Data Available into a yuima Object; 1.12.1 Getting Data from Data Providers; 1.13 How to Extract Data from a yuima Object; 1.14 Time Series Classes, Time Data and Time Stamps; 1.14.1 Review of Some Time Series Objects in R; 1.14.2 How to Handle Real Time Stamps; 1.14.3 Dates Manipulation
1.14.4 Using Dates to Index Time Series1.14.5 Joining Two or More Time Series; 1.14.6 Subsetting a Time Series; 1.15 Miscellanea; 1.15.1 From Yuima to LaTeX; 1.15.2 The Yuima GUI; Part II Models and Inference; 2 Diffusion Processes; 2.1 Model Specification; 2.1.1 Ornstein-Uhlenbeck (OU); 2.1.2 Geometric Brownian Motion (gBm); 2.1.3 Vasicek Model (VAS); 2.1.4 Constant Elasticity of Variance (CEV); 2.1.5 Cox-Ingersoll-Ross Process (CIR); 2.1.6 Chan-Karolyi-Longstaff-Sanders Process (CKLS); 2.1.7 Hyperbolic Diffusion Processes; 2.2 More About Simulation; 2.3 Multidimensional Processes
2.3.1 The Heston Model2.4 Parametric Inference; 2.4.1 Quasi-maximum Likelihood Estimation; 2.4.2 Adaptive Bayes Estimation; 2.5 Example of Real Data Estimation for gBm; 2.6 Example of Real Data Estimation for CIR; 2.7 Hypotheses Testing; 2.8 AIC Model Selection; 2.8.1 An Example of AIC Model Selection for Exchange Rates Data; 2.9 LASSO Model Selection; 2.9.1 An Example of Lasso Model Selection for Interest Rates Data; 2.10 Change Point Estimation; 2.10.1 Example of Volatility Change Point Estimation for Two-Dimensional SDEs; 2.10.2 An Example of Two-Stage Estimation
2.10.3 Example of Volatility Change Point Estimation in Real Data2.11 Asynchronous Covariance Estimation; 2.11.1 Example: Data Generation and Estimation by yuima Package; 2.11.2 Asynchronous Estimation for Nonlinear Systems; 2.11.3 Other Covariance Estimators; 2.12 Lead-Lag Estimation; 2.12.1 Application of the Lead-Lag Estimator to Real Data; 2.13 Asymptotic Expansion; 2.13.1 Asymptotic Expansion for General Stochastic Processes; 3 Compound Poisson Processes; 3.1 Inhomogeneous Compound Poisson Process; 3.1.1 Linear Intensity Function; 3.1.2 The Weibull Model