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exam 7 > Brehm ch.3 > Flashcards

Brehm ch.3 Flashcards

1
Q

what does implementing an IRM require beyond data collection and model running?

(Brehm ch.3)

A
  • planning
  • organizational change
  • leadership
  • communication
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2
Q

what are six organizational details of implementing IRMs that need to be addressed early on?

(Brehm ch.3)

A
  • organization chart
  • functions represented
  • resource commitment
  • critical roles and responsibilities
  • purpose
  • scope
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3
Q

what org chart considerations should be assessed when implementing IRMs?

(Brehm ch.3)

A
  • modeling team reporting line

- solid line vs. dotted line reporting

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4
Q

what considerations about functions represented should be assessed when implementing IRMs?

(Brehm ch.3)

A
  • reserving
  • pricing
  • finance
  • planning
  • underwriting
  • risk
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5
Q

what resource commitment considerations should be assessed when implementing IRMs?

(Brehm ch.3)

A
  • mix of skill set (UW, actuarial, communication, etc.)

- full time vs. part time

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6
Q

what critical roles and responsibilities should be assessed when implementing IRMs?

(Brehm ch.3)

A
  • control of input parameters
  • control of output data
  • analyses and uses of output
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7
Q

what purpose consideration should be assessed when implementing IRMs?

(Brehm ch.3)

A

is the goal of the model to quantify variation around the plan?

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8
Q

what scope considerations should be assessed when implementing IRMs?

(Brehm ch.3)

A
  • prospective UW year only or including reserves, assets, operational risks?
  • low detail (whole company) or high detail (on specific segment)?
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9
Q

what do Brehm et. al recommend re: reporting relationship when implementing IRMs?

(Brehm ch.3)

A

reporting line for IRM team is less important than ensuring they report to a leader who is fair

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10
Q

what do Brehm et. al recommend re: resource commitment when implementing IRMs?

(Brehm ch.3)

A

best to transfer internal employees or hire external employees for full-time positions

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11
Q

what do Brehm et. al recommend re: inputs and outputs when implementing IRMs?

(Brehm ch.3)

A

controlled in a manner similar to that used for general ledger or reserving systems

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12
Q

what do Brehm et. al recommend re: initial scope when implementing IRMs?

(Brehm ch.3)

A
  • prospective UW period

- variation around plan

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13
Q

what four parameter development details need to be addressed when implementing IRMs?

(Brehm ch.3)

A
  • modeling software
  • developing input parameters
  • correlations
  • validation and testing
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14
Q

what are modeling software considerations when implementing IRMs?

(Brehm ch.3)

A
  • capabilities
  • scalability
  • learning curve
  • integration with other systems
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15
Q

what are three considerations when developing input parameters to implement IRMs?

(Brehm ch.3)

A
  • process heavily data driven
  • requires expert opinion (especially with low data quality)
  • many functional areas should be involved
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16
Q

what needs to be addressed about correlations when implementing IRMs?

(Brehm ch.3)

A

-LOB reps cannot set cross-line parameters -> corporate-level ownership is required

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17
Q

what are validation and testing considerations when implementing an IRM?

(Brehm ch.3)

A
  • no existing IRM with which to compare

- multi-metric testing

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18
Q

what do the authors recommend for modeling software re: parameter development for IRMs?

(Brehm ch.3)

A
  • compare existing vendor software with user-built options

- ensure final software choice aligns with capabilities of the IRM team

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19
Q

what do the authors recommend when developing input parameters for IRMs?

(Brehm ch.3)

A
  • include product expertise from UW, claims, planning, and actuarial
  • develop systematic way to capture expert opinion
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20
Q

what do the authors recommend when development correlation parameter details for IRMs?

(Brehm ch.3)

A
  • have IRM team recommend correlation assumptions

- ultimately own those at the corporate level

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21
Q

what do the authors recommend for validating and testing IRMs?

(Brehm ch.3)

A
  • validate and test over extended period

- provide basic education to interested parties on probabilities and statistics

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22
Q

what four IRM implementation details need to be addressed?

Brehm ch.3

A
  • priority setting
  • interest and impact
  • pilot test
  • education process
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23
Q

what priority setting details need to be addressed for IRM implementation?

(Brehm ch.3)

A
  • importance of priority (company may not immediately make necessary improvements to support implementation)
  • approach and style (ask vs. mandate)
  • priority and timeline must be driven from the top
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24
Q

what interest and impact details need to be addressed for IRM implementation?

(Brehm ch.3)

A

-implement communication and education plans across the enterprise

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25
Q

what pilot testing details need to be addressed for IRM implementation?

(Brehm ch.3)

A
  • assign multidisciplinary team (actuarial, UW, finance, etc.) to provide real data and real analysis on the company as a whole or one one specific segment
  • piloting means model indications receive NO WEIGHT
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26
Q

what education process details need to be addressed for IRM implementation?

(Brehm ch.3)

A
  • run in education process in parallel with pilot test

- bring leadership to same point of understanding regarding probability and statistics

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27
Q

what do the authors recommend for priority setting for IRM implementation details?

(Brehm ch.3)

A

have top management set the priority for implementation

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28
Q

what do the authors recommend for interest and impact details for IRM implementation?

(Brehm ch.3)

A

plan for regular communication to broad audiences

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29
Q

what do the authors recommend for pilot testing for IRM implementation?

(Brehm ch.3)

A

prepare the company for the magnitude of change resulting from using an IRM

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30
Q

what do the authors recommend for education process for IRM implementaiton?

(Brehm ch.3)

A

-target training to bring leadership to a similar BASE level of understanding

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31
Q

what three IRM integration and maintenance details need to be addressed?

(Brehm ch.3)

A
  • cycle
  • updating
  • controls
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32
Q

what details re: IRM cycle need to be addressed?

Brehm ch.3

A
  • integrate model runs into major corporate calendar (planning, reinsurance purchasing, capacity allocation)
  • ensure that IRM output supports major company decisions
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33
Q

what details re: updating IRMs need to be addressed?

Brehm ch.3

A

determine frequency and magnitude of updates

34
Q

what control details for IRMs need to be addressed?

Brehm ch.3

A
  • ensure there is centralized storage and control of input sets and output sets (date sampling vital)
  • ensure there is an endorsed set of analytical templates used to manipulate IRM outputs for various purposes (such as decision making and reporting)
35
Q

what do the authors recommend for IRM integration and maintenance cycle?

(Brehm ch.3)

A

integrate into planning calendar at a minimum

36
Q

what do the authors recommend for IRM updating?

Brehm ch.3

A
  • perform major input review no more frequently than twice a year
  • minor updates can be handled by modifying the scale of the impacted portfolio segments
37
Q

what do the authors recommend for IRM controls?

Brehm ch.3

A

-maintain centralized control of inputs, outputs, and application templates

38
Q

in what three ways does the risk of assuming incorrect distributions or parameters for estimating F and S distributions for each LOB manifest itself?

(Brehm ch.3)

A
  • estimation risk
  • projection risk
  • model risk
39
Q

what is estimation risk?

Brehm ch.3

A

arises from using only a sample of the universe of the possible claims to estimate the parameters of the distribution

40
Q

what is projection risk?

Brehm ch.3

A

arises from projecting past trends into the future

41
Q

what is model risk?

Brehm ch.3

A

arises from having the wrong models to begin with

42
Q

if severity distributions are identical, but one company has a larger frequency distribution, how would their CV for aggregate losses compare?

(Brehm ch.3)

A

-much higher for company with smaller frequency mean

43
Q

if severity distributions are identical, but one company has a larger poisson F mean, and losses are multiplied by random factor (1+J), how do the impacts to CV for each company compare?

[1+J is constant for all claims for a year and has a mean of 1]
(Brehm ch.3)

A
  • as CV(1+J) rises, the risk for larger companies rises as well
  • much less pronounced for smaller companies since they’re already volatile
44
Q

how can projection risk be quantified for a simple trend model (trend line fit to loss cost history)?

(Brehm ch.3)

A

-prediction intervals can be placed around the projection

45
Q

in the projection period, what is the projection uncertainty a combination of?

(Brehm ch.3)

A
  • uncertainty in each historical point
  • uncertainty in fitted trend line
  • -> spread in prediction intervals increases in projection period
46
Q

how does claim severity trend tend to differ from general inflation?

(Brehm ch.3)

A
  • claim severity trend is often greater

- excess trend is often described as social inflation or superimposed inflation

47
Q

what is an approach to modeling severity that considers general inflation?

(Brehm ch.3)

A
  • correct payment data using general inflation indices
  • model residual superimposed inflation
  • *any subsequent projection is of superimposed inflation ONLY, need separate general inflation projection
48
Q

what is an advantage of projecting superimposed inflation and general inflation separately?

(Brehm ch.3)

A
  • reflects the dependency between claim severity trend and general inflation
  • ->inflation uncertainty is incorporated into projection risk
49
Q

what does the simple trend model assume?

Brehm ch.3

A
  • assumes there is a single underlying trend rate that has been constant throughout the historical period and will remain constant in the future
  • only potential error is misestimation of rate
50
Q

what is the general structure of a first order autocorrelated time series (AR-1)?

(Brehm ch.3)

A
  • mean-reverting time series
  • true mean unknown and estimated from data
  • includes autocorrelation coefficient and annual disturbance distribution
51
Q

how do prediction intervals change over time for the simple model vs. the AR-1 model?

(Brehm ch.3)

A
  • for both models, prediction intervals widen with time due to the uncertainty in the estimated trend rate
  • more pronounced to AR-1 & intervals are wider due to additional uncertainty
52
Q

in long-tailed lines of business with a long projection period, which trend model is preferred and why?

(Brehm ch.3)

A
  • prefer AR-1

- simple trend likely understates projection risk

53
Q

what is a challenge of parameterizing time series?

Brehm ch.3

A

if time period of data is too limited to exhibit a range of behaviors, resulting model will be limited too, and will understate projection risk

54
Q

what is the preferred method for estimating parameters of frequency and severity distributions from historical data?

(Brehm ch.3)

A

maximum likelihood estimation (MLE)

55
Q

what is the likelihood of a distribution for a set of data?

Brehm ch.3

A

probability of observing that data from a samle of that size from the (F/S) distribution

56
Q

when might the best-fitting parameters be difficult to determine by MLE?

(Brehm ch.3)

A

-if likelihood “surface” is very flat near the maximum -> wide range of parameter sets have almost the same likelihood –> set that maximizes likelihood might not be any better than one that has slightly smaller likelihood

57
Q

what is the information matrix?

Brehm ch.3

A

-matrix of all second partial derivatives of the negative of the log-likelihood

58
Q

what is the inverse of the information matrix?

Brehm ch.3

A

covariance matrix for the parameters of the distribution

59
Q

for large data sets, what are the parameter distributions in the MLE procedure?

(Brehm ch.3)

A

multivariate normal

60
Q

why does the normality assumption for parameter distributions in the MLE procedure create problems?

(Brehm ch.3)

A
  • SD of parameters can be high enough to produce negative param values with significant probability
  • distribution of parameters may be heavy-tailed (bi-variate normal is not heavy-tailed)
  • simulation tests on small samples have found that log-normal distributions provide a good fit to the parameter distributions
61
Q

how do we assess estimation risk?

Brehm ch.3

A
  • use covariance matrix from standard MLE procedure

- assume params follow a joint logN distribution

62
Q

how do AIC and BIC consider parameters when evaluating models?

(Brehm ch.3)

A

penalize models that use a large number of params

63
Q

what type of AIC/BIC is desirable, and what does this indicate?

(Brehm ch.3)

A
  • low is desirable

- indicates model that fits well AND has less complexity

64
Q

what is another statistic similar to AIC/BIC?

Brehm ch.3

A

Hannan-Quinn Information Criteria (HQIC)

65
Q

what are five recommendations to overcome the possibility of selecting the wrong distribution?

(Brehm ch.3)

A
  • assign probabilities of being right to all of the better-fitting distributions (based on HQIC or Bayesian analysis)
  • use simulation model to select distribution
  • select params from joint logN distr. of params
  • simulate loss scenario
  • start process over again
66
Q

what is parsimony?

Brehm ch.3

A

less model complexity

67
Q

what can too much parsimony produce?

Brehm ch.3

A

unrealistically stable results

68
Q

why is some model complexity required?

Brehm ch.3

A

to ensure that the model is capturing the true uncertainty of the underlying process

69
Q

how does the correlation between auto and property insurance (or any two correlated lines) need to be reflected in IRMs?

(Brehm ch.3)

A

by incorporating more weight into the right tail of the multivariate distribution describing the SUM of the lines

70
Q

what is a copula?

Brehm ch.3

A

a function that combines individual marginal distributions (ex: auto and property lines) into a multivariate distribution

71
Q

what does C(0.3, 0.2) represent?

Brehm ch.3

A
  • the probability that u < 0.3 and v < 0.2

- probability that F_X(x) < 0.3 and F_Y(y) < 0.2

72
Q

what kind of correlation in the tails does the Frank copula produce?

(Brehm ch.3)

A

weak tail correlation

73
Q

how does tail concentration in the Gumbel copula compare to Frank?

(Brehm ch.3)

A

-more tail concentration

74
Q

what is the general shape of the Gumbel copula?

Brehm ch.3

A

asymmetric with more weight in the right tail

75
Q

what does the heavy right tail Copula look?

Brehm ch.3

A

-less correlation in left tail and more correlation in right tail

76
Q

when does a HRT copula produce a joint Burr distribution?

Brehm ch.3

A
  • if X and Y are Burr distributions AND

- a parameter of both Burr distributions is the same as that of HRT copula

77
Q

what are two advantages of the normal copula?

Brehm ch.3

A
  • easy simulation method

- generalizes to multi-dimensions (i.e. greated than two dimensions)

78
Q

how does the normal copula right tail compare to the other copulas?

(Brehm ch.3)

A
  • lighter than Gumbel and HRT copulas

- heavier than Frank copula

79
Q

order the tails of the Gumbel, Frank, HRT, and normal copulas, from lightest to heaviest

A

Frank < Normal < Gumbel < HRT

80
Q

what are three ways to fit copulas to data?

Brehm ch.3

A
  • MLE
  • minimize SSE between empirical and fitted copulas
  • look at descriptive functions (ex: tail concentration functions) to judge goodness of fit

exam 7 (21 decks)

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