Introduction to Financial Derivatives with Python

Length: 228 pages
Edition: 1
Language: English
Publisher: Chapman and Hall/CRC
Publication Date: 2022-12-16
ISBN-10: 1032211032
ISBN-13: 9781032211039
Sales Rank: #0 (See Top 100 Books)

Introduction to Financial Derivatives with Python is an ideal textbook for an undergraduate course on derivatives, whether on a finance, economics, or financial mathematics programme. As well as covering all of the essential topics one would expect to be covered, the book also includes the basis of the numerical techniques most used in the financial industry, and their implementation in Python.

Features

Connected to a Github repository with the codes in the book. The repository can be accessed at https://bit.ly/3bllnuf
Suitable for undergraduate students, as well as anyone who wants a gentle introduction to the principles of quantitative finance
No pre-requisites required for programming or advanced mathematics beyond basic calculus.

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
List of Figures
Foreword
Preface
CHAPTER 1: Introduction
	1.1. FINANCIAL MARKETS
	1.2. DERIVATIVES
	1.3. TIME HAS A VALUE
	1.4. NO-ARBITRAGE PRINCIPLE
	1.5. CHAPTER’S DIGEST
	1.6. EXERCISES
CHAPTER 2: Futures and Forwards
	2.1. FORWARD CONTRACTS: DEFINITIONS
	2.2. FUTURES
	2.3. WHY TO USE FORWARDS AND FUTURES?
	2.4. THE FAIR DELIVERY PRICE: THE FORWARD PRICE
		2.4.1. The General Approach
		2.4.2. Some Special Cases
			2.4.2.1. Assets that Provide a Known Income
			2.4.2.2. Assets that Provide an Income Proportional to Its Price
		2.4.3. The Price of a Forward Contract
		2.4.4. The general case
			2.4.4.1. The Case of a Known Income
			2.4.4.2. Assets that Provide an Income Proportional to Its Price
	2.5. CHAPTER’S DIGEST
	2.6. EXERCISES
CHAPTER 3: Options
	3.1. CALL AND PUT OPTIONS
	3.2. THE INTRINSIC VALUE OF AN OPTION
	3.3. SOME PROPERTIES OF OPTION PRICES
		3.3.1. The Price of an Option vs the Price of an Asset
		3.3.2. The Role of the strike price
		3.3.3. The Role of the Price of the Underlying Asset
		3.3.4. The Role of Interest Rates
		3.3.5. The Role of Volatility
		3.3.6. The Role of Time to Maturity
		3.3.7. The Put-Call Parity
	3.4. SPECULATION WITH OPTIONS
	3.5. SOME CLASSICAL STRATEGIES
		3.5.0.1. Bull Spread
		3.5.0.2. Bear Spread
	3.6. DRAW YOUR STRATEGY WITH PYTHON
	3.7. CHAPTER’S DIGEST
	3.8. EXERCISES
CHAPTER 4: Exotic Options
	4.1. BINARY OPTIONS
	4.2. FORWARD START OPTIONS
		4.2.1. Compound Options
	4.3. PATH-DEPENDENT OPTIONS
		4.3.1. Barrier Options
		4.3.2. Lookback Options
		4.3.3. Asian Options
	4.4. SPREAD AND BASKET OPTIONS
	4.5. BERMUDA OPTIONS
	4.6. CHAPTER’S DIGEST
	4.7. EXERCISES
CHAPTER 5: The Binomial Model
	5.1. THE SINGLE-PERIOD BINOMIAL MODEL
		5.1.1. Relationship between European Options and Their Underlying in the Binomial Model
		5.1.2. Replication Portfolio for European Options
		5.1.3. The Risk-neutral Valuation
		5.1.4. Link the Model to the Market
	5.2. THE MULTI-PERIOD BINOMIAL MODEL
		5.2.1. Adjusting the Parameters
		5.2.2. Pricing a European Option
			5.2.2.1. Extended Framework
			5.2.2.2. Simplified Framework
		5.2.3. Early Exercise
	5.3. THE GREEKS IN THE BINOMIAL MODEL
		5.3.1. Delta
		5.3.2. Gamma
		5.3.3. Theta
		5.3.4. Vega
		5.3.5. Rho
		5.3.6. Approximating the Price Function
	5.4. CODING THE BINOMIAL MODEL
	5.5. CHAPTER’S DIGEST
	5.6. EXERCISES
CHAPTER 6: A Continuous-time Pricing Model
	6.1. CREATING SOME INTUITION
	6.2. THE BLACK-SCHOLES-MERTON FRAMEWORK
	6.3. THE BLACK-SCHOLES-MERTON EQUATION
	6.4. THE BLACK-SCHOLES-MERTON FORMULA
	6.5. THE BLACK-SCHOLES-MERTON MODEL FROM A PROBABILISTIC PERSPECTIVE
	6.6. THE BLACK-SCHOLES-MERTON PRICE AND THE BINOMIAL PRICE
	6.7. THE GREEKS IN THE BLACK-SCHOLES-MERTON MODEL
		6.7.1. Delta
		6.7.2. Theta
		6.7.3. Gamma
		6.7.4. Vega
	6.8. OTHER ASSETS
		6.8.1. Black-Scholes-Merton with Dividends
		6.8.2. Black-Scholes-Merton for Foreign-Exchange
		6.8.3. Black-scholes-Merton for Futures
	6.9. DRAWBACKS OF THE BLACK-SCHOLES-MERTON MODEL
	6.10. CHAPTER’S DIGEST
	6.11. EXERCISES
CHAPTER 7: Monte Carlo Methods
	7.1. THE NEED OF GENERAL OPTION PRICING TOOLS
	7.2. MATHEMATICAL FOUNDATIONS OF MONTE CARLO METHODS
		7.2.1. Sample Means as Estimators of Theoretical Expectations
		7.2.2. The Laws of Large Numbers
		7.2.3. The Central Limit Theorem
	7.3. OPTION PRICING WITH MONTE CARLO METHODS
		7.3.1. European Options that Depend Only on the Final Value of the Asset
		7.3.2. European Options that Depend on the Path of Asset Prices
	7.4. EUROPEAN OPTIONS THAT DEPEND ON THE FINAL PRICE OF TWO ASSETS
	7.5. CHAPTER’S DIGEST
	7.6. EXERCISES
CHAPTER 8: The Volatility
	8.1. HISTORICAL VOLATILITIES
	8.2. THE SPOT VOLATILITY
	8.3. THE IMPLIED VOLATILITY
	8.4. CHAPTER’S DIGEST
	8.5. EXERCISES
CHAPTER 9: Replicating Portfolios
	9.1. REPLICATING PORTFOLIOS FOR THE BINOMIAL MODEL
	9.2. REPLICATING PORTFOLIOS FOR THE BLACK-SCHOLES-MERTON MODE
	9.3. CHAPTER’S DIGEST
	9.4. EXERCISES
APPENDIX A: Introduction to Python
	A.1. BASIC OPERATIONS
	A.2. DATA TYPES
	A.3. VARIABLES
	A.4. PRINT
	A.5. PACKAGES
	A.6. ROCKING LIKE A DATA SCIENTIST
		A.6.1. Import Data
		A.6.2. Using Dataframes
		A.6.3. Make Plot
	A.7. CHAPTER’S DIGEST
	A.8. EXERCISES
APPENDIX B: Introduction to Coding in Python
	B.1. DEFINE YOUR OWN FUNCTIONS
	B.2. IF
	B.3. FOR
	B.4. CREATING MATRICES
	B.5. CHAPTER’S DIGEST
	B.6. EXERCISES
Bibliography
Index