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Working in a handson 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 timeseries data. Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib. A stepbystep tutorial packed with many Python programs that will help you learn how to apply Python to finance.
Unlimited Duration
January 6, 2021
EXPIRED
Course Description
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, multifactor 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 FamaFrench 3factor, 5factor, or FamaFrenchCarhart 4 factor model, estimating the VaR of a 5stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20stock portfolio with realworld stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous BlackScholesMerton option model and how to price exotic options such as the average price call option.
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Course Curriculum

 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

 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

 Sources of Data 00:00:00
 Diving into deeper concepts 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

 Capital Asset Pricing Model 00:00:00
 Introduction to CAPM 00:00:00
 Moving beta 00:00:00
 Adjusted beta 00:00:00
 Extracting output data 00:00:00
 Simple string manipulation 00:00:00
 Python via Canopy 00:00:00

 Multifactor Models and Performance Measures 00:00:00
 Introduction to the FamaFrench threefactor model 00:00:00
 FamaFrench threefactor model 00:00:00
 FamaFrenchCarhart fourfactor model and FamaFrench fivefactor 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

 TimeSeries Analysis 00:00:00
 Introduction to timeseries analysis 00:00:00
 Merging datasets based on a date variable 00:00:00
 Understanding the interpolation technique 00:00:00
 Tests of normality 00:00:00
 52week high and low trading strategy 00:00:00
 Estimating Roll’s spread 00:00:00
 Estimating Amihud’s illiquidity 00:00:00
 Estimating Pastor and Stambaugh (2003) liquidity measure 00:00:00
 FamaMacBeth regression 00:00:00
 DurbinWatson 00:00:00
 Python for highfrequency data 00:00:00
 Spread estimated based on highfrequency data 00:00:00
 Introduction to CRSP 00:00:00

 Portfolio Theory 00:00:00
 Introduction to portfolio theory 00:00:00
 A 2stock portfolio 00:00:00
 Optimization – minimization 00:00:00
 Forming an nstock portfolio 00:00:00
 Constructing an optimal portfolio 00:00:00
 Constructing an efficient frontier with n stocks 00:00:00

 Options and Futures 00:00:00
 Introducing futures 00:00:00
 Payoff and profit/loss functions for call and put options 00:00:00
 European versus American options 00:00:00
 BlackScholesMerton option model on nondividend paying stocks 00:00:00
 Generating our own module p4f 00:00:00
 European options with known dividends 00:00:00
 Various trading strategies 00:00:00
 Putcall parity and its graphic presentation 00:00:00
 Binomial tree and its graphic presentation 00:00:00
 Hedging strategies 00:00:00
 Implied volatility 00:00:00
 Binarysearch 00:00:00
 Retrieving option data from Yahoo! Finance 00:00:00
 Volatility smile and skewness 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

 Monte Carlo Simulation 00:00:00
 Importance of Monte Carlo Simulation 00:00:00
 Generating random numbers from a standard normal distribution 00:00:00
 Generating random numbers with a seed 00:00:00
 Generating random numbers from a uniform distribution 00:00:00
 Using simulation to estimate the pi value 00:00:00
 Generating random numbers from a Poisson distribution 00:00:00
 Selecting m stocks randomly from n given stocks 00:00:00
 With/without replacements 00:00:00
 Distribution of annual returns 00:00:00
 Simulation of stock price movements 00:00:00
 Graphical presentation of stock prices at options’ maturity dates 00:00:00
 Replicating a BlackScholesMerton call using simulation 00:00:00
 Liking two methods for VaR using simulation 00:00:00
 Capital budgeting with Monte Carlo Simulation 00:00:00
 Python SimPy module 00:00:00
 Comparison between two social policies – basic income and basic job 00:00:00
 Finding an efficient frontier based on two stocks by using simulation 00:00:00
 Constructing an efficient frontier with n stocks 00:00:00
 Longterm return forecasting 00:00:00
 Efficiency, QuasiMonte Carlo, and Sobol sequences 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 AAArated bond, Altman Zscore 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

 Exotic Options 00:00:00
 European, American, and Bermuda options 00:00:00
 Chooser options 00:00:00
 Shout options 00:00:00
 Binary options 00:00:00
 Rainbow options 00:00:00
 Pricing average options 00:00:00
 Pricing barrier options 00:00:00
 Barrier inandout parity 00:00:00
 Graph of upandout and upandin parity 00:00:00
 Pricing lookback options with floating strikes 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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