What are GARCH models, what are they used for, and how can you implement them in Python? After completing this first chapter you’ll be able to confidently answer all these questions.


Why do we need GARCH models

Understand volatility

Observe volatility clustering

Calculate volatility

What are ARCH and GARCH

Review GARCH model basics

Simulate ARCH and GARCH series

Observe the impact of model parameters

How to implement GARCH models in Python

Review "arch" documentation

Implement a basic GARCH model

Make forecast with GARCH models

GARCH Model Fundamentals

A normal GARCH model is not representative of the real financial data, whose distributions frequently exhibit fat tails, skewness, and asymmetric shocks. In this chapter, you’ll learn how to define better GARCH models with more realistic assumptions. You’ll also learn how to make more sophisticated volatility forecasts with rolling window approaches.

Distribution assumptions

Fat tails and skewness

Plot distribution of standardized residuals

Fit a GARCH with skewed t-distribution

Mean model specifications

Check mean model assumptions

Effect of mean model on volatility predictions

Volatility models for asymmetric shocks

Modeling of asymmetric responses of volatility

Fit GARCH models to cryptocurrency

Compare GJR-GARCH with EGARCH

GARCH rolling window forecast

Why use rolling window forecast

Fixed rolling window forecast

Compare forecast results

GARCH Model Configuration

This chapter introduces you to the KISS principle of data science modeling. You’ll learn how to use p-values and t-statistics to simplify model configuration, use ACF plot, Ljung-Box test to verify model assumptions and use likelihood and information criteria for model selection.

Significance testing of model parameters

Keep it simple stupid

Simplify the model with p-values

Simplify the model with t-statistics

Validation of GARCH model assumptions

Detect autocorrelations

ACF plot

Ljung-Box test

Goodness of fit measures

Goodness of fit basics

Pick a winner based on log-likelihood

Pick a winner based on AIC/BIC

GARCH model backtesting

Backtesting basics

Backtesting with MAE, MSE

Model Performance Evaluation

In this final chapter, you’ll learn how to apply the GARCH models you’ve previously learned to practical financial world scenarios. You’ll develop your skills as you become more familiar with VaR in risk management, dynamic covariance in asset allocation, and dynamic Beta in portfolio management.

VaR nella gestione del rischio finanziario

Concetto di VaR

Calcola il VaR parametrico

Calcola il VaR empirico

Covarianza dinamica nell’ottimizzazione di portafoglio

Concetto di covarianza

Calcolare la covarianza GARCH

Calcola la varianza dinamica del portafoglio

Beta dinamico nella gestione di portafoglio

Concetto di Beta

Calcola il Beta dinamico del titolo

Congratulazioni!

GARCH in Action

SP500

Tesla stock price

Bitcoin price

Foreign exchange data

La volatilità è un concetto fondamentale in finanza, motivo per cui i modelli GARCH in Python sono una scelta molto diffusa per prevedere le variazioni della varianza, soprattutto quando si lavora con serie temporali dipendenti dal tempo. In questo corso vedrai come e quando implementare i modelli GARCH, come definire le ipotesi del modello, come fare previsioni di volatilità e come valutarne le prestazioni. Utilizzando dati reali, tra cui i prezzi storici delle azioni Tesla, farai pratica su come quantificare meglio i rischi di portafoglio tramite il calcolo del Value-at-Risk, della covarianza e del Beta azionario. Metterai inoltre in pratica ciò che hai imparato su un’ampia gamma di asset, tra cui azioni, indici, criptovalute e valute estere, preparandoti a utilizzare i modelli GARCH.

Time Series Analysis in Python

Scopri i modelli GARCH, implementali e calibrali su dati finanziari. Applica a azioni e valute.

Modelli GARCH in Python

Scopri i modelli GARCH, come usarli e calibrarli sui dati finanziari, dalle azioni al cambio valuta.

VaR nella gestione del rischio finanziario

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