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Applied Probabilistic Calculus for Assets Allocation and Portfolio Optimization in Financial Engineering Using R

ISBN: 978-1-119-38761-9
672 pages
October 2017
Applied Probabilistic Calculus for Assets Allocation and Portfolio Optimization in Financial Engineering Using R (1119387612) cover image


This book provides R recipes for asset allocation and portfolio optimization problems. After introducing all the necessary probabilistic and statistical foundations, the author moves on to topics related to asset allocation and portfolio optimization with R codes illustrated for various examples. This book consists of six chapters. Chapter 1 provides an introduction, which covers financial engineering, using R in data analysis, and univariate, bivariate, and multivariate data analysis. Chapter 2 examines probabilistic calculus for modeling financial engineering. This chapter walks the reader through building an effective financial model from GBM via probalistic calculus, and also covers Ito Calculus. Chapter 3 describes classical mathematical models in financial engineering and modern portfolio theory. The Two Mutual Fund Theorem and The Sharpe Ratio are discussed. R as a calculator and using R in data analysis in financial engineering are the topics of Chapter 4. Next, Chapter 5 covers assets allocation using R. Finally, Chapter 6 examines financial risk modeling and portfolio optimization using R. In this chapter, the author discusses global and local optimal values, locating functional maxima and minima, and also portfolio optimization by performance analytics in CRAN.

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Table of Contents



Chapter 1: Introduction to Financial Engineering

1 Introduction to Financial Engineering

1.1 What is Financial Engineering?

1.2 The Meaning of the Title of this Book

1.3 The Continuing Challenge in Financial Engineering

1.4 “Financial Engineering 101”: Modern Portfolio Theory[2]

1.5 Asset Class Assumptions Modeling

1.6 Typical Examples of Proprietary Investment Funds

1.7 The Dow Jones Industrial Average (DJIA) and Inflation

1.8 Some Less Commendable Stock Investment Approaches

1.9 Developing Tools for Financial Engineering Analysis Solutions to Exercises in Chapter 1: 

Chapter 2: Probabilistic Calculus for Modeling Financial Engineering

2.1 Introduction to Financial Engineering

2.2 Mathematical Modeling in Financial Engineering

2.3 Building an Effective Financial Model from GBM via Probabilistic Calculus

2.4 A Continuous Financial Model Using Probabilistic Calculus (Stochastic Calculus, Ito Calculus)

2.5 Numerical Examples of Representation of Financial Data Using R

Chapter 3: Classical Mathematical Models in Financial Engineering and Modern Portfolio Theory

3.0 An Introduction to the Cost of Money in the Financial Market

3.1 Modern Theories of Portfolio Optimization

3.2 The Black-Litterman Model

3.3 The Black-Scholes Option Pricing Model

Chapter 4: Data Analysis Using R Programming

4.1 Data and Processing

4.2 Beginning R

4.3 R as a Calculator

4.4 Using R in Data Analysis in Financial Engineering

4.5 Univariate, Bivariate, and Multivariate Data Analysis

Appendix 1: Documentation for the plot function

Special References for Chapter 4

Chapter 5: Assets Allocation Using R 

5.1 Risk Aversion and the Assets Allocation Process

5.2 Classical Assets Allocation Approaches

5.3 Allocation with Time Varying Risk Aversion

5.4 Variable Risk Preference Bias

5.5 A Unified Approach for Time Varying Risk Aversion

5.6 Assets Allocation Worked Examples

Chapter 6: Financial Risk Modeling and Portfolio Optimization Using R

6.1 Introduction to the Optimization Process

6.2 Optimization Methodologies in Probabilistic Calculus for Financial Engineering

6.3 Financial Risk Modeling and Portfolio Optimization     



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