Mô tả

This course is about the fundamental basics of financial engineering. First of all you will learn about stocks, bonds and other derivatives. The main reason of this course is to get a better understanding of mathematical models concerning the finance in the main.

First of all we have to consider bonds and bond pricing. Markowitz-model is the second step. Then Capital Asset Pricing Model (CAPM). One of the most elegant scientific discoveries in the 20th century is the Black-Scholes model and how to eliminate risk with hedging.

IMPORTANT: only take this course, if you are interested in statistics and mathematics !!!

Section 1 - Introduction

  • installing Python

  • why to use Python programming language

  • the problem with financial models and historical data

Section 2 - Stock Market Basics

  • present value and future value of money

  • stocks and shares

  • commodities and the FOREX

  • what are short and long positions?

Section 3 - Bond Theory and Implementation

  • what are bonds

  • yields and yield to maturity

  • Macaulay duration

  • bond pricing theory and implementation

Section 4 - Modern Portfolio Theory (Markowitz Model)

  • what is diverzification in finance?

  • mean and variance

  • efficient frontier and the Sharpe ratio

  • capital allocation line (CAL)

Section 5 - Capital Asset Pricing Model (CAPM)

  • systematic and unsystematic risks

  • beta and alpha parameters

  • linear regression and market risk

  • why market risk is the only relevant risk?

Section 6 - Derivatives Basics

  • derivatives basics

  • options (put and call options)

  • forward and future contracts

  • credit default swaps (CDS)

  • interest rate swaps

Section 7 - Random Behavior in Finance

  • random behavior

  • Wiener processes

  • stochastic calculus and Ito's lemma

  • brownian motion theory and implementation

Section 8 - Black-Scholes Model

  • Black-Scholes model theory and implementation

  • Monte-Carlo simulations for option pricing

  • the greeks

Section 9 - Value-at-Risk (VaR)

  • what is value at risk (VaR)

  • Monte-Carlo simulation to calculate risks

Section 10 - Collateralized Debt Obligation (CDO)

  • what are CDOs?

  • the financial crisis in 2008

Section 11 - Interest Rate Models

  • mean reverting stochastic processes

  • the Ornstein-Uhlenbeck process

  • the Vasicek model

  • using Monte-Carlo simulation to price bonds

Section 12 - Value Investing

  • long term investing

  • efficient market hypothesis

APPENDIX - PYTHON CRASH COURSE

  • basics - variables, strings, loops and logical operators

  • functions

  • data structures in Python (lists, arrays, tuples and dictionaries)

  • object oriented programming (OOP)

  • NumPy

Thanks for joining my course, let's get started!

Bạn sẽ học được gì

Understand stock market fundamentals

Understand bonds and bond pricing

Understand the Modern Portfolio Theory and Markowitz model

Understand the Capital Asset Pricing Model (CAPM)

Understand derivatives (futures and options)

Understand credit derivatives (credit default swaps)

Understand stochastic processes and the famous Black-Scholes model

Understand Monte-Carlo simulations

Understand Value-at-Risk (VaR)

Understand CDOs and the financial crisis

Understand interest rate models (Vasicek model)

Yêu cầu

  • You should have an interest in quantitative finance as well as in mathematics and programming!

Nội dung khoá học

26 sections

Introduction

3 lectures
Introduction
02:43
Why to use Python?
03:27
Financial models
03:50

Environment Setup

2 lectures
Installing Python
01:53
Installing PyCharm
03:38

Stock Market Basics

7 lectures
Present value and future value of money
07:45
Time value of money implementation
07:37
Stocks and shares
08:43
Commodities
06:10
Currencies and the FOREX
09:02
Short and long positions
06:52
Stock Markets Basics
6 questions

Bonds Theory

8 lectures
What are bonds?
10:28
Yields and yield to maturity
05:26
Yields and yield to maturity
05:26
Interest rates and bonds
03:53
Macaulay duration
04:55
Risks with bonds
02:48
Stocks and bonds
01:53
Bonds Quiz
4 questions

Bonds Implementation

4 lectures
Bonds pricing implementation I
06:26
Bonds pricing implementation II
07:32
Exercise - continuous model for discounting
00:15
Solution - continuous model for discounting
00:12

Modern Portfolio Theory (Markowitz-Model)

9 lectures
What are mean, variance and correlation?
07:31
The main idea - diverzification
07:04
Mathematical formulation
08:28
Expected return of the portfolio
05:39
Expected variance (risk) of the portfolio
08:05
Efficient frontier
06:21
Sharpe ratio
03:44
Capital allocation line
03:30
Markowitz Model Quiz
5 questions

Markowitz-Model Implementation

5 lectures
Markowitz model implementation I
09:48
Markowitz model implementation II
12:02
Markowitz model implementation III
08:24
Markowitz model implementation IV
11:43
Markowitz model implementation V
03:34

Capital Asset Pricing Model (CAPM) Theory

6 lectures
Systematic and unsystematic risk
03:55
Capital asset pricing model formula
05:05
The beta value
06:11
What is linear regression?
08:14
Capital asset pricing model and linear regression
04:40
Capital Asset Pricing Model Quiz
4 questions

Capital Asset Pricing Model (CAPM) Implementation

5 lectures
Capital asset pricing model implementation I
04:10
Capital asset pricing model implementation II
12:25
Capital asset pricing model implementation III
07:18
Exercise - normal distribution of returns
00:15
Solution - normal distribution of returns
04:40

Derivatives Basics

9 lectures
Introduction to derivatives
07:32
Forward and future contracts
03:18
Swaps and interest rate swaps
08:52
Credit default swap (CDS)
03:43
Options basics
04:49
Call option
06:04
Put option
03:30
American and european options
01:29
Derivatives Basics Quiz
5 questions

Random Behavior in Finance

9 lectures
Types of analysis
06:29
Random behavior of returns
05:56
Wiener-processes and random walks
10:11
Wiener-process implementation
07:18
Stochastic calculus introduction
07:30
Ito's lemma in higher dimensions
05:04
Solving the geometric random walk equation
06:07
Geometric brownian motion implementation
06:19
Random Behaviour Quiz
3 questions

Black-Scholes Model

8 lectures
Black-Scholes model introduction - the portfolio
06:44
Black-Scholes model introduction - dynamic delta hedge
06:09
Black-Scholes model introduction - no arbitrage principle
04:36
Solution to Black-Scholes equation
04:07
The greeks
04:35
How to make money with Black-Scholes model?
01:56
Long Term Capital Management (LTCM)
06:03
Black-Scholes Model Quiz
4 questions

Black-Scholes Model Implementation

6 lectures
Black-Scholes model implementation
08:19
What is Monte-Carlo simulation?
06:18
Predicting stock prices with Monte-Carlo simulation
10:41
Black-Scholes model implementation with Monte-Carlo simulation I
04:42
Black-Scholes model implementation with Monte-Carlo simulation II
08:24
Black-Scholes model implementation with Monte-Carlo simulation III
02:42

Value at Risk (VaR)

6 lectures
What is Value-at-Risk?
05:25
Value-at-Risk introduction
07:38
Value at risk implementation
11:07
Value at risk implementation with Monte-Carlo simulation I
12:28
Value at risk implementation with Monte-Carlo simulation II
03:00
Value at Risk Quiz
3 questions

Collateralized Debt Obligations (CDOs) and the Financial Crisis

5 lectures
What are CDOs?
04:22
CDOs and diverzification
05:11
CDO tranches
02:34
The financial crisis of 2007-2008
05:48
CDOs Quiz
3 questions

Interest Rate Modeling (Vasicek Model)

6 lectures
Why to use interest rate models?
03:00
The Ornstein-Uhlenbeck process introduction
04:58
The Ornstein-Uhlenbeck process implementation
05:42
Vasicek model introduction
04:16
Vasicek model implementation
05:52
Interest Rate Modeling Quiz
3 questions

Pricing Bonds with Vasicek Model

3 lectures
Bond pricing with the Vasicek model I
06:34
Bond pricing with the Vasicek model II
06:56
Bond pricing with the Vasicek model III
05:07

Long-Term Investing

2 lectures
Value investing
04:47
Efficient market hypothesis
04:42

NEXT STEPS

1 lectures
Next steps
01:20

APPENDIX - PYTHON PROGRAMMING CRASH COURSE

1 lectures
Python crash course introduction
02:06

Appendix #1 - Python Basics

23 lectures
First steps in Python
05:49
What are the basic data types?
04:45
Booleans
02:08
Strings
07:44
String slicing
06:47
Type casting
04:20
Operators
05:23
Conditional statements
04:41
How to use multiple conditions?
08:07
Exercise: conditional statements
00:25
Solution: conditional statements
00:14
Logical operators
04:04
Loops - for loop
06:00
Loops - while loop
04:13
Exercise: calculating the average
00:08
Solution: calculating the average
00:06
What are nested loops?
02:55
Enumerate
03:51
Break and continue
05:32
Calculating Fibonacci-numbers
02:30
Exercise: Fibonacci-numbers
00:07
Solution: Fibonacci-numbers
00:20
Python Basics Quiz
5 questions

Appendix #2 - Functions

16 lectures
What are functions?
04:07
Defining functions
05:24
Positional arguments and keyword arguments
10:30
Returning values
02:26
Returning multiple values
03:14
Exercise: functions
00:09
Solution: functions
00:06
Yield operator
05:02
Local and global variables
02:12
What are the most relevant built-in functions?
04:26
What is recursion?
09:29
Exercise: recursion
00:10
Solution: recursion
00:14
Local vs global variables
04:16
The __main__ function
03:25
Functions Quiz
4 questions

Appendix #3 - Data Structures in Python

22 lectures
How to measure the running time of algorithms?
10:00
Data structures introduction
03:17
What are array data structures I
06:55
What are array data structures II
06:56
Lists in Python
05:43
Lists in Python - advanced operations
08:27
Lists in Python - list comprehension
05:56
(!!!) Python lists and arrays
00:22
Exercise: list comprehension
00:37
Solution: list comprehension
00:19
Measuring running time of lists
00:45
What are tuples?
03:58
Mutability and immutability
04:30
What are linked list data structures?
08:13
Doubly linked list implementation in Python
05:32
Hashing and O(1) running time complexity
08:03
Dictionaries in Python
09:41
Sets in Python
09:49
Exercise: constructing dictionaries
00:14
Solution: constructing dictionaries
00:09
Sorting
10:44
Data Structures Quiz
5 questions

Appendix #4 - Object Oriented Programming (OOP)

18 lectures
What is object oriented programming (OOP)?
02:18
Class and objects basics
03:00
Using the constructor
06:00
Class variables and instance variables
04:46
Exercise: constructing classes
00:11
Solution: constructing classes
00:08
Private variables and name mangling
04:31
What is inheritance in OOP?
03:49
The super keyword
04:24
Function (method) override
02:34
What is polymorphism?
04:25
Polymorphism and abstraction example
06:10
Exercise: abstraction
00:16
Solution: abstraction
00:12
Modules
06:00
The __str__ function
03:16
Comparing objects - overriding functions
08:00
Object Oriented Programming (OOP) Quiz
4 questions

Appendix #5 - NumPy

12 lectures
What is the key advantage of NumPy?
04:12
Creating and updating arrays
07:36
Dimension of arrays
09:12
Indexes and slicing
07:59
Types
04:43
Reshape
07:53
Exercise: reshape problem
00:12
Solution: reshape problem
00:05
Stacking and merging arrays
06:17
Filter
03:39
Running time comparison: arrays and lists
00:44
NumPy Quiz
4 questions

Course Materials (DOWNLOADS)

1 lectures
Course materials
00:02

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