Applied probabilistic calculus for financial engineering : an introduction using R / by Bertram K.C. Chan.

Format:

Book

Author/Creator:

Chan, B. K. C. (Bertram Kim-Cheong), author.

Language:

English

Subjects (All):

Financial engineering--Mathematical models.

Financial engineering.

Probabilities.

Calculus.

R (Computer program language).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Hoboken, New Jersey : Wiley, 2017.

System Details:

text file

Summary:

Illustrates 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 engineering—walking 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 discussed—along with the Two Mutual Fund Theorem and The Sharpe Ratio. The book also looks at R as a calculator and using R in data analysis in financial engineering. Additionally, it covers asset allocation using R, financial risk modeling and portfolio optimization using R, global and local optimal values, locating functional maxima and minima, and portfolio optimization by performance analytics in CRAN. Covers optimization methodologies in probabilistic calculus for financial engineering Answers the question: What does a "Random Walk" Financial Theory look like? Covers the GBM Model and the Random Walk Model Examines modern theories of portfolio optimization, including The Markowitz Model of Modern Portfolio Theory (MPT), The Black-Litterman Model, and The Black-Scholes Option Pricing Model Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R s an ideal reference for professionals and students in economics, econometrics, and finance, as well as for financial investment quants and financial engineers.

Contents:

Intro

Title Page

Copyright

Dedication

Preface

About the Companion Website

Chapter 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

1.5 Asset Class Assumptions Modeling

1.6 Some 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

Review Questions

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 A Numerical Study of the Geometric Brownian Motion (GBM) Model and the Random Walk Model Using R

Review Questions and Exercises

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

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

3.2 Modern Theories of Portfolio Optimization

3.3 The Black-Litterman Model

3.4 The Black-Scholes Option Pricing Model

3.5 The Black-Litterman Model

3.6 The Black-Litterman Model

3.7 The Black-Scholes Option Pricing Model

3.8 Some Worked Examples

Solutions to Exercise 3: The Black-Scholes Equation

Chapter 4: Data Analysis Using R Programming

4.1 Data and Data Processing

Review Questions for Section 4.1

4.2 Beginning R

Review Questions for Section 4.2

4.3 R as a Calculator.

Review Questions for Section 4.3

Exercises for Section 4.3

4.4 Using R in Data Analysis in Financial Engineering

Review Questions for Section 4.4

4.5 Univariate, Bivariate, and Multivariate Data Analysis

Review Questions for Section 4.5

Exercise for Section 4.5

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

6.4 Portfolio Optimization Using R1

References

Index

End User License Agreement.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781119388081

1119388082

9781119388050

1119388058

9781119388043

111938804X

OCLC:

1000150840