Two-Project Series

Finance Volatility Modeling you own this product

prerequisites
intermediate Python • basic scikit-learn and Matplotlib • basic financial concepts • intermediate machine learning
skills learned
volatility forecasting in financial modeling using ARCH and GARCH • optimizing financial model parameters using BIC • observing ML-based models using SVR-GARCH and NN-GARCH models • prediction analysis
Abdullah Karasan
2 weeks · 8-10 hours per week average · BEGINNER
includes 2 liveProjects
liveProject $39.99 $39.99 self-paced learning

Are you ready to “explode” into the financial analysis-side of data science? Volatility is a factor in determining financial risk and it even makes appearances in option pricing formulas. In fact, volatility analysis is the backbone of finance modeling. In this liveProject series, you’ll learn to predict and model volatility, optimize model parameters, and choose the best financial model by analyzing and comparing prediction results. With the projects in this series, you’ll be managing financial risk like a pro with reliable and accurate volatility forecasts.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

here's what's included

Project 1 Using Traditional Finance Models

In this liveProject, you’ll play the part of a freelance consultant who’s been hired to assess a company’s financial risk. Using the traditional volatility modeling packages ARCH and GARCH, you’ll model the volatility of S&P-500 stock prices—a good proxy for the entire financial market—and measure model performance. Then, you’ll optimize the model parameters using information criteria such as Bayesian Information Criteria (BIC). When you’ve completed the project, you’ll have a solid understanding of the logic of these traditional models and be ready to apply it to other models.

$29.99 $19.99
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Project 2 Using Machine Learning

Now that you’ve tackled volatility modeling in a traditional way, in this liveProject, your employer has challenged you to uplevel your volatility modeling by taking a more dynamic, data-dependent approach. By the end of this project, you’ll have firsthand experience modeling volatility using support vector machines with different kernels, neural networks, and deep learning models. What’s more, you’ll have the skills to determine if the machine learning-based models outperform the traditional parametric models.

$29.99 $19.99
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books resources

When you start each of the projects in this series, you'll get full access to the following books for 90 days.

project author

Abdullah Karasan
Abdullah Karasan was born in Berlin, Germany. After studying economics and business administration, he obtained his master's degree in applied economics from the University of Michigan, Ann Arbor, and his PhD in financial mathematics from the Middle East Technical University, Ankara. He is a former Treasury employee of Turkey and currently works as a principal data scientist at Magnimind and as a lecturer at the University of Maryland, Baltimore. He has also published several papers in the field of financial data science.

Prerequisites

These liveProjects are for intermediate Python programmers and data scientists who want to leverage their Python skills for finance applications. To begin these liveProjects you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Jupyter Notebook
TECHNIQUES
  • Basic financial concepts
  • Intermediate machine learning
  • sklearn for running classical time series analysis and stationary analysis
  • Matplotlib for visualization
  • ARCH for modeling
  • scipy.optimize.minimize for modeling volatility by hand

you will learn

In this liveProject series, you’ll learn skills and techniques for volatility forecasting in financial modeling using traditional models as well as machine learning models. The valuable knowledge and skills you’ll master here can help take your career to the next level:


  • Importing modules with Python
  • Declaring customized functions
  • Data extraction
  • Predicting and modeling volatility
  • Optimizing model parameters
  • Analyzing and comparing prediction results
  • Running classical time series analysis with sklearn
  • Running stationary analysis with sklearn
  • Visualizing and plotting with Matplotlib
  • Modeling with ARCH and GARCH
  • Modeling volatility by hand using scipy.optimize.minimize
  • Using machine and deep learning libraries TensorFlow and sklearn
  • Volatility modeling using using support vector machines with different kernels, neural networks, and deep learning models

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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