Financial Risk Manager Part 1

The candidate's approach is inappropriate because seasonality may account for a large part of the variation. In time series analysis, both seasonal and non-seasonal variations play a significant role. Seasonal variation refers to fluctuations in the data that occur in regular, predictable intervals, such as monthly, quarterly, or annually. These variations can be due to seasonal factors like weather, holidays, and school schedules. On the other hand, non-seasonal variation refers to fluctuations that do not follow a particular pattern. These can be due to factors like economic conditions, political events, and technological changes. When analyzing a time series, it is crucial to consider both types of variations as they provide a comprehensive understanding of the data. Ignoring seasonal variation can lead to inaccurate conclusions as it might account for a significant portion of the variation in the data. Therefore, the candidate's approach of disregarding seasonal variation is inappropriate.

Choice A is incorrect. Seasonality can have a significant impact on many phenomena, including economic ones. Therefore, disregarding it may lead to inaccurate analysis and conclusions.