Financial Risk Manager Part 1

Explanation

Employment data typically exhibits patterns of seasonality and autocorrelations that decay gradually over time. This characteristic makes both Autoregressive (AR) and Autoregressive Moving Average (ARMA) models appropriate for analyzing such data.

Key Points:

1. AR Models: Autoregressive models are suitable when current values depend on past values with gradually decaying autocorrelations.
2. ARMA Models: These combine AR and MA components and are particularly effective for time series data with both autoregressive and moving average characteristics.
3. Moving Average (MA) Models: These would be more appropriate if autocorrelations cut off abruptly after a certain lag, which is not typical for employment data.
4. Employment Data Characteristics: Employment statistics often show:
   • Seasonal patterns (quarterly, yearly cycles)
   • Gradual decay in autocorrelations
   • Trend components
   • Economic cycle dependencies

Why not the other options:

• A (Moving Average): Only appropriate if autocorrelations cut off abruptly
• B (Autoregressive): While AR models can be useful, ARMA models provide more flexibility by combining AR and MA components
• D (None of the above): Incorrect since both AR and ARMA models are appropriate

The correct answer is C because both AR and ARMA models can effectively capture the gradual decay in autocorrelations and seasonal patterns characteristic of employment data.