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Working in a hands-on learning environment, led by our Python for Finance expert instructor, students will learn about and explore: Understand the fundamentals of Python data structures and work with time-series data. Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib. A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to finance.
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Course Access
Unlimited Duration
Last Updated
July 29, 2021
Students Enrolled
20
Total Reviews
Posted by
Certification
This course uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This course is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the this edition is truly trying to apply Python to finance. The course starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This course will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM’s market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.
Course Curriculum
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- Python Basics 00:00:00
- Python installation 00:00:00
- Variable assignment, empty space, and writing our own programs 00:00:00
- Writing a Python function 00:00:00
- Python loops 00:00:00
- Data input 00:00:00
- Data manipulation 00:00:00
- Data output 00:00:00
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- Introduction to Python Modules 00:00:00
- What is a Python module? 00:00:00
- Introduction to NumPy 00:00:00
- Introduction to SciPy 00:00:00
- Introduction to matplotlib 00:00:00
- Introduction to statsmodels 00:00:00
- Introduction to pandas 00:00:00
- Python modules related to finance 00:00:00
- Introduction to the pandas_reader module 00:00:00
- Two financial calculators 00:00:00
- How to install a Python module 00:00:00
- Module dependency 00:00:00
- Time Value of Money 00:00:00
- Introduction to time value of money 00:00:00
- Writing a financial calculator in Python 00:00:00
- Definition of NPV and NPV rule 00:00:00
- Definition of IRR and IRR rule 00:00:00
- Definition of payback period and payback period rule 00:00:00
- Writing your own financial calculator in Python 00:00:00
- Two general formulae for many functions 00:00:00
- Bond and Stock Valuation 00:00:00
- Introduction to interest rates 00:00:00
- Term structure of interest rates 00:00:00
- Bond evaluation 00:00:00
- Stock valuation 00:00:00
- A new data type – dictionary 00:00:00
- Multifactor Models and Performance Measures 00:00:00
- Introduction to the Fama-French three-factor model 00:00:00
- Fama-French three-factor model 00:00:00
- Fama-French-Carhart four-factor model and Fama-French five-factor model 00:00:00
- Implementation of Dimson (1979) adjustment for beta 00:00:00
- Performance measures 00:00:00
- How to merge different datasets 00:00:00
- Portfolio Theory 00:00:00
- Introduction to portfolio theory 00:00:00
- A 2-stock portfolio 00:00:00
- Optimization – minimization 00:00:00
- Forming an n-stock portfolio 00:00:00
- Constructing an optimal portfolio 00:00:00
- Constructing an efficient frontier with n stocks 00:00:00
- Value at Risk 00:00:00
- Introduction to VaR 00:00:00
- Normality tests 00:00:00
- Skewness and kurtosis 00:00:00
- Modified VaR 00:00:00
- VaR based on sorted historical returns 00:00:00
- Simulation and VaR 00:00:00
- VaR for portfolios 00:00:00
- Backtesting and stress testing 00:00:00
- Expected shortfall 00:00:00
- Credit Risk Analysis 00:00:00
- Introduction to credit risk analysis 00:00:00
- Credit rating 00:00:00
- Credit spread 00:00:00
- YIELD of AAA-rated bond, Altman Z-score 00:00:00
- Using the KMV model to estimate the market value of total assets and its volatility 00:00:00
- Term structure of interest rate 00:00:00
- Distance to default 00:00:00
- Credit default swap 00:00:00
- Volatility, Implied Volatility, ARCH, and GARCH 00:00:00
- Conventional volatility measure – standard deviation 00:00:00
- Tests of normality 00:00:00
- Estimating fat tails 00:00:00
- Lower partial standard deviation and Sortino ratio 00:00:00
- Test of equivalency of volatility over two periods 00:00:00
- Test of heteroskedasticity, Breusch, and Pagan 00:00:00
- Volatility smile and skewness 00:00:00
- Graphical presentation of volatility clustering 00:00:00
- The ARCH model 00:00:00
- Simulating an ARCH (1) process 00:00:00
- The GARCH model 00:00:00
- Simulating a GARCH process 00:00:00
- Simulating a GARCH (p,q) process using modified garchSim() 00:00:00
- GJR_GARCH by Glosten, Jagannanthan, and Runkle 00:00:00
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