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R Programming and Its Applications in Financial Mathematics
R Programming and Its Applications in Financial Mathematics
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Author(s): Yoshikawa, Daisuke
ISBN No.: 9781498766098
Pages: 248
Year: 201803
Format: Trade Cloth (Hard Cover)
Price: £114.00
Dispatch delay: Dispatched between 7 to 15 days
Status: Available (On Demand)

Preface Introduction to R programming Installation of R Operators Data structure Functions Control statements Graphics Reading and writing data Reading program Packages SECTION I: STATISTICS IN FINANCE Statistical Analysis with R Basic Statistics Probability distribution and random numbers Hypothesis testing Regression Analysis Yield curve analysis using principal component analysis? Time Series Analysis with R Preparation of time series data Before applying for models The application for AR model Models extended from AR Application of the time series analysis to finance: Pair trading Nonlinear statistics with R ARCH and GARCH Nonparametric Functional Data Analysis SECTION II: BASIC THEORY OF FINANCE Modern Portfolio Theory and CAPM Mean-variance portfolio Market portfolio Derivation of CAPM The extension of CAPM: Multi-factor model The form of efficient frontier Interest Rate Swap and Discount Factor Interest rate swap Pricing of interest rate swap and derivation of discount factors Valuation of interest rate swap and risk analysis Discrete Time Model: Tree Model Single period binomial model Multi period binomial model Trinomial model Continuous time model and the Black-Scholes Formula Continuous rate of return It^o's lemma The Black-Scholes formula Implied volatility SECTION III: NUMERICAL METHODS IN FINANCE Monte Carlo Simulation The basic concept of Monte Carlo simulation Variance reduction method Exotic option Multi asset options Control variates method Derivative Pricing with Partial Differential Equation Explicit method Implicit method Noise reduction via Kalman Filter Introduction to Kalman filter Nonlinear Kalman filter SECTION IV: APPENDIX 237 A Optimization with R A.1 Multi variate optimization problem A.2 Efficient frontier by optimization problem B Noise reduction via Kalman Filter B.1 Introduction to Kalman filter B.2 Nonlinear Kalman filter C The other references on R C.1 Information sources on R C.2 R package on finance References Index.


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