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Advanced Python for Finance

Implementing advanced financial modeling techniques

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Topic: Software Development
Abdullah Karasan

Time series analysis is at the heart of financial modeling. Most financial data, such as stock prices, has a time dimension, which needs to be modeled accordingly. Likewise, volatility prediction is an ingredient in many financial models, including portfolio optimization and value at risk (VaR)—one of the main financial risk management tools.

Join expert Abdullah Karasan to explore time series analysis, volatility prediction, and value at risk (VaR). You’ll get hands-on with the autoregressive (AR) model, the moving average (MA) model, the autoregressive moving average (ARMA) model, the autoregressive integrated moving average (ARIMA) model, and the seasonal autoregressive integrated moving average (SARIMA) model for time series analysis. You’ll then learn how to perform volatility modeling using the autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH), and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models. You’ll conclude by discovering how to conduct VaR and credit VaR (CVaR) analysis with Python.

What you'll learn-and how you can apply it

By the end of this live online course, you’ll understand:

  • How to effectively use Python in finance
  • How and why to perform time series analysis
  • How to predict volatility, which is the proxy of risk
  • How to conduct risk management

And you’ll be able to:

  • Conduct time series and volatility analyses
  • Pursue financial risk management using VaR and CVaR

This training course is for you because...

  • You’re a financial analyst who wants to improve your financial modeling skills.
  • You want to improve your finance knowledge.
  • You want to learn how to adapt Python to finance while building your Python skills.

Prerequisites

  • A basic understanding of statistics (central tendency and dispersion measures, distributions, etc.)
  • A general knowledge of finance concepts (risk, return, correlation, covariance, etc.)
  • Familiarity with Python (for loops and if clauses, pandas, NumPy, etc.)

Recommended preparation:

Recommended follow-up:

About your instructor

  • Abdullah Karasan was born in Berlin, Germany. After he studied Economics and Business Administration, he obtained his master’s degree from the University of Michigan-Ann Arbor and his PhD in Financial Mathematics from Middle East Technical University (METU)-Ankara. He worked as a Treasury Controller at the Undersecretariat of the Treasury in Turkey. More recently, he has been working as Principal Data Science consultant and instructor at Thinkful and Magnimind.

    His research fields are financial modeling, stochastic analysis, risk modeling machine learning, deep learning. Along with his researches, he is writing a book titled "Machine Learning for Financial Risk Management with Python.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Time series analysis (55 minutes)

  • Presentation: The autoregressive model, the moving average model, the autoregressive moving average model, the autoregressive integrated moving average model, and the seasonal autoregressive integrated moving average model—and their application in Python
  • Jupyter notebook exercise: Apply the above models to time series data using Python
  • Q&A

Break (5 minutes)

Volatility analysis (55 minutes)

  • Presentation: The importance of volatility modeling; autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH), and exponential generalized autoregressive conditional heteroskedasticity (EGARCH)—and their application in Python
  • Jupyter notebook exercise: Apply the above models to time series data using Python
  • Q&A

Break (5 minutes)

Financial risk management (50 minutes)

  • Presentation: Financial risk; the motivation for using the value at risk and credit value at risk models
  • Jupyter notebook exercise: Compute value at risk for a return series via Python

Wrap-up and Q&A (10 minutes)