1. Lernen
    2. /
  2. Kurse
    3. /
  3. GARCH Models in Python
  • 1

    GARCH Model Fundamentals

    Kostenlos

    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.

  • 2

    GARCH Model Configuration

    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.

  • 3

    Model Performance Evaluation

    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.

  • 4

    GARCH in Action

    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.

Initializing

Ubung

Plot distribution of standardized residuals

GARCH models make distribution assumptions of the standardized residuals. Recall residuals are the differences between predicted returns and the mean returns. And standardized residuals are the residuals divided by the model estimated volatility.

In this exercise, you will practice computing the standardized residuals from a fitted GARCH model, and then plot its histogram together with a standard normal distribution normal_resid.

A GARCH model has been defined and fitted with S&P 500 price return data. The fitted result can be accessed as gm_result. In addition matplotlib has been preloaded as plt.

Anweisungen

100 XP
  • Obtain model estimated residuals and save it in gm_resid.
  • Obtain model estimated volatility and save it in gm_std.
  • Calculate the standardized residuals gm_std_resid.
  • Plot a histogram of gm_std_resid.