Book Image

Python for Finance Cookbook

By : Eryk Lewinson
Book Image

Python for Finance Cookbook

By: Eryk Lewinson

Overview of this book

Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach.
Table of Contents (12 chapters)

Forecasting the conditional covariance matrix using DCC-GARCH

In this recipe, we cover an extension of the CCC-GARCH model: Engle's Dynamic Conditional Correlation GARCH (DCC-GARCH) model. The main difference between the two is that in the latter, the conditional correlation matrix is not constant over time—we have Rt instead of R.

There are some nuances in terms of estimation, but the outline is similar to the CCC-GARCH model:

  • Estimate the univariate GARCH models for conditional volatility
  • Estimate the DCC model for conditional correlations

In the second step of estimating the DCC model, we use a new matrix Qt, representing a proxy correlation process.

The first equation describes the relationship between the conditional correlation matrix Rt and the proxy process Qt. The second equation represents the dynamics of the proxy process. The last equation shows...