ARMA Models and ARCH Testing

  • Autoregressive Moving Average Model (ARMA) = calculates an average value over a period of time to smooth fluctuations in a time series.
  • ARMA models are very sensitive to minor changes and may rarely forecast well.
  • Auto Regressive Conditional Heteroskedasticity (ARCH) testing = can be used to determine if an AR, MA, or ARMA model suffers from conditional heteroskedasticity.
  • The ARCH test models the error terms and if its slope is statistically significant, then the predictive AR, MA, or ARMA model under scrutiny is not valid.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.