Financial Risk Manager Part 1
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Consider the following statements regarding the modeling of seasonal data:
I. Both the autoregressive process and the autoregressive moving average process include lagged terms and are therefore appropriate for a relationship in motion II. Both the autoregressive process and the autoregressive moving average process specialize in capturing only the random movements in data
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NNikitesh
A
I and II are both correct
B
Only II is correct
C
Only I is correct
D
I and II are both incorrect
Explanation:
Explanation
Statement I is correct: Both autoregressive (AR) and autoregressive moving average (ARMA) processes include lagged terms of the dependent variable (and in the case of ARMA, also lagged error terms). These lagged terms make these models appropriate for capturing relationships that evolve over time, or "relationships in motion."
Statement II is incorrect: While moving average (MA) processes specialize in capturing random movements or shocks in data (through lagged error terms), autoregressive (AR) processes capture persistence through lagged dependent variables. ARMA processes combine both features, capturing both persistence and random shocks. Therefore, it's not accurate to say that both AR and ARMA processes specialize in capturing only random movements.
Key points:
- AR processes: Use lagged values of the variable itself to model persistence
- MA processes: Use lagged error terms to model random shocks
- ARMA processes: Combine both AR and MA components
- The moving average representation is best suited for capturing only random movements in data
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