cotor training session ii
Download
Skip this Video
Download Presentation
COTOR Training Session II

Loading in 2 Seconds...

play fullscreen
1 / 40

COTOR Training Session II - PowerPoint PPT Presentation


  • 112 Views
  • Uploaded on

COTOR Training Session II. GL Data: Long Tails, Volatility, Data Transforms September 11, 2006. COTOR Session II Presenters. Doug Ryan MBA Actuaries, Inc. Phil Heckman Heckman Actuarial Consulting. Assumptions and Verification. Behavior of mean, variance, distribution (sometimes)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'COTOR Training Session II' - terry


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
cotor training session ii

COTOR Training Session II

GL Data: Long Tails, Volatility, Data Transforms

September 11, 2006

cotor session ii presenters
COTOR Session II Presenters

Doug Ryan

MBA Actuaries, Inc.

Phil Heckman

Heckman Actuarial Consulting

assumptions and verification
Assumptions and Verification
  • Behavior of mean, variance, distribution (sometimes)
  • Verify by examining
    • Descriptive statistics
    • Regression diagnostics
    • Scatter plots
    • Residual plots
what are they
What are they?
  • Slope standard error
  • R square: Percentage of variance explained by regression
  • Intercept standard error
  • Degrees of Freedom: # Observations - # Parameters
a key diagnostic standard residual
A Key Diagnostic: Standard Residual
  • Standardize by subtracting mean (should be zero) and divide by standard deviation
  • A z-score
    • Z = (x – mean)/sd
two factor model
Two Factor Model
  • One factor model: incremental loss =f(prior cumulative)
    • Compute separate function for each development age
    • Can use Excel regression functions
  • Two factor model: incremental loss = f(accident period, development age)
    • Bornhuetter-Ferguson is an example
    • Nonlinear function, Use solver
why use logarithms
Why use logarithms?
  • Descriptive statistics indicate data not normal
  • A-priori belief that model is mutiplicative
  • Residuals increase with value of dependent variable
iterative least squares
Iterative Least Squares
  • Start with all weights = 1
  • Estimate by minimizing weighted sum of squares
  • Calculate new weights =

1/(1+ Old Weight*Squared Error)

  • Reëstimate. Stop when weights stop changing.
ad