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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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Auto-Regressive Models - Random Walks and Unit Roots

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How to Select the Most Appropriate Time Series Model?

01
CFA L2: Quantitative Methods - Introduction
02
Quants: Correlation Analysis
03
Quants: Single Variable Linear Regression Analysis
04
Standard Error of the Estimate or SEE
05
Confidence Intervals (CI) for Dependent Variable Prediction
06
Coefficient of Determination (R-Squared)
07
Analysis of Variance or ANOVA
08
Multiple Regression Analysis
09
Multiple Regression and Coefficient of Determination (R-Squared)
10
Fcalc – the Global Test for Regression Significance
11
Regression Analysis and Assumption Violations
12
Qualitative and Dummy Variables in Regression Modeling
13
Time Series Analysis: Simple and Log-linear Trend Models
14
Auto-Regressive (AR) Time Series Models
15
Auto-Regressive Models - Random Walks and Unit Roots
16
ARMA Models and ARCH Testing
17
How to Select the Most Appropriate Time Series Model?

Topics

Risk Management
Statistical Methods