Difference between revisions of "Friedland10.CapeCod"

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(In Plain English!)
(Example A: The Basic CC Method)
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===Example A: The Basic CC Method===
 
===Example A: The Basic CC Method===
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'''Good news''': You can cover the CC method pretty quickly because it's very similar to the BF method. The formulas for Ult<sub>r-CC</sub> and Ult<sub>r-BF</sub> look exactly the same as you can see below:
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:{| class='wikitable'
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|-
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|| Ult<sub>r-CC</sub> &nbsp; = &nbsp; (reported claims) + %<u>un</u>reported x Ult<sub>ECR</sub> &nbsp; = &nbsp; (reported claims) + (1 &ndash; 1/CDF) x Ult<sub>ECR</sub>
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|}
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The <u>difference</u> is in how Ult<sub>ECR</sub> is calculated.
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: ''BF'': You calculate Ult<sub>ECR</sub> using the ECR method. The ECR method does adjust for trend and tort reform but the final ECR selection is <u>judgmental</u>. For a refresher, you can quickly review these examples: ''[[Friedland08.ExpectedClms#Example_B:_Estimating_ECR_using_Loss_Trends | ECR Method Trend Adjustment]]'' and ''[[Friedland08.ExpectedClms#Example_C:_Estimating_ECR_under_Tort_Reform | ECR Method Tort Reform Adjustment]]''.
  
 
===Example B: A Hard CC Problem===
 
===Example B: A Hard CC Problem===

Revision as of 18:50, 15 July 2020

Reading: Friedland, J.F., Estimating Unpaid Claims Using Basic Techniques, Casualty Actuarial Society, Third Version, July 2010. The Appendices are excluded.

Chapter 10: Cape Cod Method

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Study Tips

BattleTable

Based on past exams, the main things you need to know (in rough order of importance) are:

  • fact A...
  • fact B...
reference part (a) part (b) part (c) part (d)
E (2019.Fall #19) ultimate claims:
- reported Cape Cod
E (2019.Spring #18) ultimate claims:
- reported Cape Cod
identify scenario:
- paid CC works better
E (2018.Spring #8)
E (2017.Fall #21) ultimate:
- Cape Cod
E (2017.Spring #23) ultimate:
- paid devlpt
ultimate:
- Cape Cod
E (2016.Spring #18) Cape Cod vs B-F:
- compare
Cape Cod vs B-F:
- adjustments to rptd loss
Cape Cod vs B-F:
- adjustments to EP
court decision:
- identify best method
E (2015.Spring #17) IBNR:
- Cape Code adjustments
E (2014.Spring #15) IBNR:
- B-F
IBNR:
- Cape Cod
B-F vs Cape Cod:
- rising claims, thin data
E (2013.Fall #20) IBNR:
- Cape Cod

In Plain English!

Example A: The Basic CC Method

Good news: You can cover the CC method pretty quickly because it's very similar to the BF method. The formulas for Ultr-CC and Ultr-BF look exactly the same as you can see below:

Ultr-CC   =   (reported claims) + %unreported x UltECR   =   (reported claims) + (1 – 1/CDF) x UltECR

The difference is in how UltECR is calculated.

BF: You calculate UltECR using the ECR method. The ECR method does adjust for trend and tort reform but the final ECR selection is judgmental. For a refresher, you can quickly review these examples: ECR Method Trend Adjustment and ECR Method Tort Reform Adjustment.

Example B: A Hard CC Problem

Once you understand the basic version of the CC method, here's a harder problem for you to try. It's harder for 2 reasons:

  • they don't give you the rate level adjustment factors directly – you need knowledge of the pricing material to calculate them yourself
  • they don't give the CDFs (Cumulative Development Factors) – you have to calculate them yourself from the data triangle but it's very tricky because you first have to adjust the triangle to take into account the tort reform

Give it a try before you watch the video.

CC Method Concepts

  • similar to BF - difference is in how ECR is chosen
    • BF uses results of ECR method (incorporates judgment)
    • CC uses a formula (no judgment involved)
  • often use in reinsurance (why?)
  • assumption: unreported claims will develop based on expected claims
  • ads:
    • uses reported claims in the calculation of the ECR, therefore it will respond (at least partially) to changes in claims ratios
    • (note that if the CR changes over time, increases or decreases, then this trend may not be reflected in the CC formula for ECR)
  • disads:
    • dependent on the availability and accuracy of the rate level adjustment factor (can use without adjusting for CRL but then lose accuracy)
    • thin data increases volatility (should then use BF because we can incorporate judgment)

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