B.4. Continuous Changes Flashcards
Reasons for adjusting continuous changes
To ensure historical data reflects mix of business and levels of social and economic inflation expected in the future period
Possible data sources for trends
Historical insurer data
Industry data
Economic data
Data adjustments before trending
Adjust for large events, one-time changes, seasonality
Advantages for using each type of premium to determine premium trends
Earned - We are trending earned premium anyway
Written - more recent data, as changes in average written premium will ultimately show up as changes in average earned premium.
Advantages between AY and CY data to determine loss trends
AY - better match between losses and exposures
CY - no need to estimate ultimate losses
Why frequency and severity are typically analyzed separately
They can change for different reasons
Two other alternative methods for loss trends
- Using econometric or GL methods
2. Using incremental CY data by AY
Advantages between using paid and reported loss data to determine loss trends
Paid – not subject to changes in case reserving practices
Reported – incorporates more recent information (case could ultimately become paid)
Two reasons why excess severity trends are greater than basic or total limits trends
- For losses above basic limits, trend is entirely in excess layer
- Losses just under basic limit are pushed into excess layer by the trend, creating new excess losses
Three ways to calculate trend from historical data
- Take an average of the percent changes
- Fit a line to the data (assumes constant change for each time period)
- Fit exponential curve (assumes constant percent change between time periods)
Information needed to determine average earned date of a future policy period
- Future rate change effective date
- Length of time rates are expected to be in effect
- Policy term late
Assumptions commonly made in determining trend periods
- Policies are written uniformly over time
2. Premiums earned uniformly and losses occur uniformly
Two ways to perform first step of a two-step trend
- Adjust historical period level to be equal to latest level:
Current trend factor = latest average WP at current / historical average EP at current - Trend historical period level to the latest time period (more common for loss trends)
Overlap fallacy between development and trending
Development brings data from each historical period to its ultimate level, while trending reflects the difference in ultimate levels from one period to the next.
Loss development makes sure future policy is priced to cover ultimate losses. Loss trend makes sure ultimate losses are at cost levels corresponding to future policy period.