Financial Risk Manager Part 1

A seasonal time series component reflects variability during a single year. This means that the data exhibits a pattern of change that repeats annually. For example, retail sales may increase during the holiday season each year, or energy consumption may rise during the summer months due to increased use of air conditioning. This predictable and recurring pattern within each year is what defines a seasonal time series component. It's important to note that the pattern must repeat with a fixed and known frequency within each year for it to be considered a seasonal component. This component is crucial in time series analysis as it helps in forecasting and understanding the underlying patterns in the data.

Choice A is incorrect. The variability due to natural disasters is not a seasonal component of a time series. Natural disasters are unpredictable and do not follow a regular pattern, hence they cannot be considered as seasonal components which are expected to occur at specific intervals.

Choice C is incorrect. A regular, multi-year pattern of being above and below the trend line does not describe the characteristics of a seasonal time series component. Seasonal components reflect variability within a single year, whereas this choice describes cyclical patterns that can span multiple years.

Choice D is incorrect. Gradual variability over a long time period refers to the trend component of a time series, not the seasonal component. The trend represents long-term progression of the series while seasonality refers to short-term, predictable and regular patterns within specific periods (usually within an annual cycle).