Description
Book SynopsisIllustrates how R may be used successfully to solve problems in quantitative finance
Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R provides R recipes for asset allocation and portfolio optimization problems. It begins by introducing all the necessary probabilistic and statistical foundations, before moving on to topics related to asset allocation and portfolio optimization with R codes illustrated for various examples. This clear and concise book covers financial engineering, using R in data analysis, and univariate, bivariate, and multivariate data analysis. It examines probabilistic calculus for modeling financial engineeringwalking the reader through building an effective financial model from the Geometric Brownian Motion (GBM) Model via probabilistic calculus, while also covering Ito Calculus. Classical mathematical models in financial engineering and modern portfolio theory are discussedalong with the Two Mutual Fund Theo
Table of Contents
Preface
Dedication
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
References
Index
