Brosius Flashcards

1
Q

Formula for Least Squares Method (raw formula, Excel)

A

Excel: LINEST(known y’s, known x’s) => returns b, a

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2
Q

Least Squares as a credibility weighting of the Link Ratio and Budgeted
Loss methods

A

link ratio: CL; budgeted loss: expected loss

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3
Q

Estimates of developed losses for each method in Brosius

A
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4
Q

In what special cases is the Least Squares Method equivalent to the link
ratio or budgeted loss methods?

A
  • If developed losses (y) are totally uncorrelated with undeveloped losses (x),
    => then b = 0 and L(x) = a
    o This is the budgeted loss method
  • If the regression line fits through the origin,
    => a = 0 and L(x) = bx
    o This is the link ratio method
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5
Q

How is the Least Squares method more flexible than the BF method?

A

BF Method
Ultimate losses are estimated as expected unobserved loss plus actual observed loss, L(x) = a + x → b = 1

LS Method
Allows b to vary according to the data, L(x) = a + bx
→ b isn’t constrained to 1
→ LS method allows for negative development

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6
Q

What situations result in problems for estimating parameters for the Least
Squares method?

A
  • Significant changes to the nature of the loss experience in the book of business
  • Normal sampling error will lead to variance in a and b estimates
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7
Q

For the Least Squares method, what are the problems if a < 0 or b < 0?
What are some of the possible corrections?

A

a < 0
* Estimate of developed losses (y) will be negative for small values of x
o Substitute the link ratio method instead

b < 0
* Estimate of y decreases as x increases
o Substitute the budgeted loss method instead

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8
Q

When is the least squares method appropriate to use? When is it
inappropriate?

A
  • Appropriate if we have a series of years of data where we can assume stable distributions for Y and X
    o We assume fluctuations are driven by random chance
  • Inappropriate if year-to-year changes are due to systematic changes in the book of business (e.g. mix shift).
    o Other methods such as Berquist-Sherman may be better
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9
Q

What adjustments should be made to data prior to using Least Squares
development?

A
  • If using incurred losses, correct data for inflation to put losses on a constant-dollar basis
  • If there is significant growth in the book, divide losses by an exposure basis to correct the distortion
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10
Q

Bayesian Credibility in a changing system

A
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11
Q

What is the caseload effect?

A
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12
Q

Credibility development formula allowing for the caseload effect

A
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13
Q

Formula for the best linear approximation to Q(x), the Bayesian estimate

A
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14
Q

3 advantages of the best linear approximation as a replacement for the Bayesian estimate

A
  1. simpler to compute
  2. easier to understand and explain
  3. less dependent upon the underlying distribution
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15
Q

Using the best linear approximation to Q(x), how does the relationship
between Cov(X,Y ) and Var(X ) impact the response to loss reserves for a
large reported loss

A

A large reported loss (increasing x) changes the loss reserves according to
the three different answers to Hugh White’s question. For x > E[X ]:
* Cov(X,Y ) < Var(X ) - loss reserve decreases
* Cov(X,Y ) = Var(X ) - loss reserve unaffected (ultimate loss increases
. by the increase to x)
* Cov(X,Y ) > Var(X ) - loss reserve increases

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16
Q

Based on Hugh White’s Question, if reported losses are higher than
expected, what are three different responses to estimated loss reserves and
the reasoning behind each response?

A

If x is greater than E[X ], possible responses to the loss reserve estimate are:

  • Reduce the reserve by a corresponding amount
    o You believe loss reporting is accelerating, this is the Budgeted Loss method (fixed prior case)
  • Leave the reserve at the same percent of expected losses
    o You believe there was a random fluctuation (e.g. large loss), this is the BF method
  • Increase the reserve proportionally to the increase in actual reported loss
    o You’re not very confident in E[Y ], this is the link ratio method