#### 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)
Preface
Financial Data and Preprocessing
Free Chapter
Technical Analysis in Python
Identifying Credit Default with Machine Learning
Advanced Machine Learning Models in Finance
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# Explaining stock returns' volatility with ARCH models

In this recipe, we approach the problem of explaining the conditional volatility of stock returns, with the Autoregressive Conditional Heteroskedasticity (ARCH) model.

The logic of the ARCH method can be represented by the following equations:

The first equation represents the return series as a combination of the expected return μ and the unexpected return t. The latter one is also known as the mean-corrected return, error term, or innovations. t has white noise properties—the conditional mean equal to zero and the time-varying conditional variance . Error terms are serially uncorrelated but do not need to be serially independent, as they can exhibit conditional heteroskedasticity.

A zero mean process implies that the returns are only described by the residuals, rt = t. Other popular options include constant...