Essentials of Excel VBA, Python, and R: Volume II: financial derivatives, risk management and machine learning

By:

Lee, John

Contributor(s):

Material type: Text

TextPublication details: Springer Cham 2023Edition: 2ndDescription: xv, 523 pISBN:

9783031142857

Subject(s):

DDC classification:

005.54 LEE

Summary: This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis. (https://search.worldcat.org/title/1374247494)

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks		Available		008282

Table of contents:
Table of contents (23 chapters)
Front Matter
Pages i-xv
Download chapter PDF
Introduction
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 1-3
Excel VBA
Front Matter
Pages 5-5
Download chapter PDF
Introduction to Excel Programming and Excel 365 Only Features
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 7-37
Introduction to VBA Programming
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 39-74
Professional Techniques Used in Excel and VBA
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 75-111
Financial Derivatives
Front Matter
Pages 113-113
Download chapter PDF
Binomial Option Pricing Model Decision Tree Approach
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 115-135
Microsoft Excel Approach to Estimating Alternative Option Pricing Models
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 137-156
Alternative Methods to Estimate Implied Variance
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 157-189
Greek Letters and Portfolio Insurance
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 191-203
Portfolio Analysis and Option Strategies
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 205-226
Simulation and Its Application
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 227-246
Applications of Python, Machine Learning for Financial Derivatives and Risk Management
Front Matter
Pages 247-247
Download chapter PDF
Linear Models for Regression
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 249-259
Kernel Linear Model
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 261-277
Neural Networks and Deep Learning Algorithm
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 279-284
Alternative Machine Learning Methods for Credit Card Default Forecasting*
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 285-298
Deep Learning and Its Application to Credit Card Delinquency Forecasting
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 299-312
Binomial/Trinomial Tree Option Pricing Using Python
John Lee, Jow-Ran Chang, Lie-Jane Kao, Cheng-Few Lee
Pages 313-334

[https://link.springer.com/book/10.1007/978-3-031-14283-3]

This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry.

This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.

(https://search.worldcat.org/title/1374247494)

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