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KEETON 14 Comparison slides.pdf

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  • 8/12/2019 KEETON 14 Comparison slides.pdf

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    R i s k A d v i s o r y S e r v i c e sSa l i en t

    2014 Model

    Comparison

    1

    Severe Convective

    Storms

    Tornado Alley Ohio Valley

    South East

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    R i s k A d v i s o r y S e r v i c e sSa l i en t

    2014 Model

    Comparison

    2

    o Thanks!

    Vendor Participation

    Model: RQE v14Aug. 2013

    Sims: 300 k years

    Model: v6.1 - June 2008

    Platform: Touchstone v 1.5.2

    Sims: 10 k years

    Model: US SCS Jan. 2014Platform: RiskLink 13.1

    Events: 58 k + high frequency

    background

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    R i s k A d v i s o r y S e r v i c e sSa l i en t

    2014 Model

    Comparison

    3

    Comparison Focus &

    Exposure Portfolios

    Map courtesy of Munich Re

    o 2014 Focus

    FrequencyAnnual Freq. of $1 loss

    Severity - Expected Loss

    Location variabilityCoef. Var.

    Portfolio Risk - 100 yr. TVar

    o 3 Grid Portfolios

    ~250k grid locations

    Residential - $ 200 k Bldg.

    o 3 Hypothetical Portfolios

    Commercial & Residential

    Varied replacement values Bldg., Contents & BI

    300 k locations / $ 60 B TIV - total all 3 portfolios

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    Comparison

    4

    US Severe Convective Storm

    o Modeling Challenges What defines an Event?

    How are models calibrated?

    What sub-perils are included?

    o Risk Management Given the challenges noted above:

    How do the models accumulate losses in:

    A Single Occurrence?

    The Annual Aggregate?

    o Model Usage Best practices?

    From single location UW to Port. Mgmt.

    Whats next?

    Any Black Swans?

    Initial Background

    Questions

    Image courtesy of USGS

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    Comparison

    5

    o Same

    Includes allmodeledsub-perils

    oDifferent!

    Expectations

    Basis

    Granularity Approach

    SCALES!

    Not possibleto map onsame scale!

    Frequency

    Comparisono Annual Frequencies

    Of at least $1 loss

    o Relative Scale Left to right - Low to high

    *RMS basis differences Combined probabilities & different grid sizes

    Probability of loss causing event in grid ANDprobability of risk existing in grid

    AIR

    EQE

    RMS*

    Maps by Munich Re

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    R i s k A d v i s o r y S e r v i c e sSa l i en t

    2014 Model

    Comparison

    9

    Frequency Comparison

    Southeast & Tornado Alley

    AIR EQE RMS*

    Kansas

    Oklahoma

    TN

    GAAL

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    Comparison

    10

    o Same SCALE!

    Includes allmodeled sub-perils

    o Different! Granularity

    Distribution ofExpectedLosses

    AIR Centralized

    EQE Oklahoma

    RMS Vertical

    bands westto east

    Ground Up AAL

    Comparison

    o Relative Scale Left to right - Low to high

    AIR

    EQE

    RMS

    Maps by Munich Re

    Kansas

    Oklahoma

    Population density

    driven vs. even grid?

    96.3

    82.0

    43.4

    Ground Up Annual Expected Loss ($ M)

    EQE

    AIR

    RMS

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    R i s k A d v i s o r y S e r v i c e sSa l i en t

    2014 Model

    Comparison

    11

    GU AAL ($ m) Comparison

    South East & Ohio Valley

    AIR

    23.3EQE

    23.3

    RMS

    28.6

    TN

    GAAL

    KY

    INOH

    AIR

    22.7EQE

    24.1

    RMS

    25.2

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    2014 Model

    Comparison

    12

    Average Annual Loss

    General Observations

    o General

    Totals more similar than distributions of individual location E[L]

    Greatest $ difference in Tornado Alley

    o Frequency vs. AAL

    Slightly different distributions / concentrations

    Implies impact of different peril severities and exposure vulnerabilities

    105 4

    11

    4 3 5

    49

    33

    11

    5 5

    11

    4 3 5

    23 21

    10 94

    15

    4 37

    58

    38

    0

    10

    20

    30

    4050

    60

    70

    IN KY OH AL GA MS TN KS OK

    GU AAL by State ($ m)AIR

    EQERMS

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    R i s k A d v i s o r y S e r v i c e sSa l i en t

    2014 Model

    Comparison

    Hail Tornado SL wind

    Tornado Alley

    EQE

    RMS

    Hail Tornado SL wind

    South East

    EQE

    RMS

    13

    Severe Convective

    Storms - Sub-Perils

    o SCSSub-perils by Grid

    Each model includes components for a variety of sub-perils

    This years comparison requested location results by sub-perils

    Tornado, Hail & Straight Line Wind

    o Vendor Submissions

    Comparisons difficult

    More discussions

    in tomorrows

    presentations

    AIRAll sub-perils

    EQEAll sub-perils

    Tornado

    Hail

    RMS

    All sub-perils

    Tornado

    Hail

    SL wind

    Low Freq. Events

    Hail Tornado SL wind

    Ohio Valley

    EQE

    RMS

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    Comparison

    o General

    High correlation between location values ($) E[L] and Std. Dev.

    Selected example using Tornado Alley

    Same scale used for each vendor (i.e. 1 for E[L], 1 for Coef. Var.)

    Relative Scale

    Left to RightLow to High

    14

    Variability of Location E[L]

    Maps by Munich Re

    AIR EQE RMS

    Kansas

    Oklahoma

    Cv Cv Cv

    E[L] E[L] E[L]

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    Comparison

    0

    50

    100

    150

    200

    250

    300

    2 20 200

    Estimated

    Losses($m)

    Return Period (yrs)

    Ohio Valley Grid All SCS, GU

    AIR - AEP EQE - AEP RMS - AEP

    AIR - OEP EQE - OEP RMS - OEP

    15

    Loss Accumulation

    Grid EP curves

    o General

    SCS peril much more likely to have multiple loss occurrences in a year

    Risk Metric100 year Tail Value at Risk (TVaR) (estimated by dots on graph)

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    Comparison

    0

    50

    100

    150

    200

    250

    300

    2 20 200

    Estimated

    Losses($m)

    Return Period (yrs)

    Southeast Grid All SCS, GU

    AIR - AEP EQE - AEP RMS - AEP

    AIR - OEP EQE - OEP RMS - OEP

    16

    Loss Accumulation

    Grid EP curves

    o General

    SCS peril much more likely to have multiple loss occurrences in a year

    Risk Metric100 year Tail Value at Risk (TVaR) (estimated by dots on graph)

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    2014 Model

    Comparison

    0

    50

    100

    150

    200

    250

    300

    2 20 200

    Estimated

    Losses($m)

    Return Period (yrs)

    Tornado Alley Grid All SCS, GU

    AIR - AEP EQE - AEP RMS - AEP

    AIR - OEP EQE - OEP RMS - OEP

    17

    Loss Accumulation

    Grid EP curves

    o General

    SCS peril much more likely to have multiple loss occurrences in a year

    Risk Metric100 year Tail Value at Risk (TVaR) (estimated by dots on graph)

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    Comparison

    o General

    TVaR belongs to a robust and efficient class of risk metrics.

    TVaR = Average Loss in the tail of the EP curve beyond a return period

    Mapping each grids contribution to the portfolio TVaR is one way to helpvisualize how locations contributed to tail losses.

    Contribution TVaR & E[L] can be distributed very differently within any portfolio.

    Relative Scale

    Left to RightLow to High

    18

    Allocating Portfolio Risk

    to Individual Locations

    Maps by Munich Re

    AIR EQE RMS

    KYIN

    OH

    KYIN

    OH

    KYIN

    OH

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    Comparison

    19

    Next upImpact on

    Hypothetical Portfolios

    Map courtesy of Munich Re

    o Recap

    FrequencyDifferent distributions and granularities

    E[L] & Variability - Challenges smoothing small footprints, population

    based grids, event set sizes and sub-peril assumptions

    Portfolio RiskMultiple occurrences more likely in an give year and each

    model accumulates loss potential differently

    o 3 Hypothetical Portfolios

    Commercial & Residential Varied replacement values Bldg., Contents & BI

    300 k locations / $ 60 B TIV - total all 3 portfolios

    Nominal deductibles - $ 1k & $2k options

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    2014 Model

    Comparison

    6 6 5

    18 17 17

    2419

    17

    GU GR_1k GR_2k

    Upper Mid West

    AIR EQE RMS

    8 7 7

    19 17 16

    33

    2624

    GU GR_1k GR_2k

    South East

    11 10 9

    17 16 15

    45

    3733

    GU GR_1k GR_2k

    Lower Mid West

    20

    Hypothetical Portfolio

    ALs ($ m)

    o Lower Mid West

    ~73 k locations, $ 8.8 BBldg. values, $ 11.2 B total replacement costo Upper Mid West

    ~89 k locations, $ 17.8 BBldg. values, $ 23.5 B total replacement cost

    o South East

    ~155 k locations, $ 19.4 BBldg. values, $ 26.3 B total replacement cost

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    Comparison

    45

    17

    11

    Ground Up Annual Expected Loss ($ M)

    AIR

    EQE

    RMS

    21

    Look Similar?

    GeographicDifferences MO

    OK & N. TX

    So. TX

    Sub-Perils Different North to

    South?

    Terrain Impact?

    Other Analysis Granularity

    / smoothing

    No. of events /simulated years

    Lower

    MWo GU AAL

    Same scale

    o Relative Scale Left to right - Low to high

    EQE

    Maps by Munich Re

    AIR

    RMS

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    2014 Model

    Comparison

    0.3

    1.0

    0.2 0.1

    1.0 1.00.5

    1.5

    0.6 0.7

    1.5 1.71.7

    4.7

    1.41.0

    4.0 4.1

    0.0

    1.0

    2.0

    3.0

    4.0

    5.0

    6.0

    Masonry Mobile Concrete Steel Unknown Wd Frm

    GU Loss Cost (per 1k TIV) by ConstructionAIR

    EQE

    RMS

    0.6 1.0 0.8 0.7

    3.7 4.1

    1.1 1.2 1.3 0.9

    6.4 6.4

    2.0

    4.53.5

    2.7

    17.5

    15.3

    0.0

    5.0

    10.0

    15.0

    20.0

    IA KS MO NE OK TX

    GU AAL by State ($ m)AIR

    EQE

    RMS

    22

    Lower Mid West

    AAL Breakdown

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    Comparison

    23

    Look Similar?

    GeographicDifferences Upper WI

    Upper MI

    Kentucky

    SE Ohio

    Sub-Perils Different

    North toSouth?

    TerrainImpact?

    Other Analysis

    Granularity /smoothing

    No. of events/ simulatedyears

    Upper

    MWo GU AAL

    Same scale

    o Relative Scale

    Left to right - Low to high

    EQE

    Maps by Munich Re

    AIR

    RMS

    24

    18

    6

    Ground Up Annual Expected Loss ($ M)

    AIR

    EQE

    RMS

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    Comparison

    24

    Upper Mid West

    AAL Breakdown

    2.1

    1.00.5

    1.2 1.10.6

    5.7

    4.1

    1.2

    3.12.6

    1.5

    7.0

    5.0

    3.6

    2.5

    3.8

    1.8

    0.0

    1.0

    2.0

    3.0

    4.0

    5.06.0

    7.0

    8.0

    IL IN KY MI OH WI

    GU AAL by State ($ m) AIR

    EQE

    RMS

    0.1

    0.5

    0.1 0.0

    0.4 0.40.4

    1.1

    0.40.5

    0.9 1.0

    0.5

    2.0

    0.4 0.4

    1.3 1.2

    0.0

    0.5

    1.0

    1.5

    2.0

    2.5

    3.0

    Masonry Mobile Concrete Steel Unknown Wd Frm

    GU Loss Cost (per 1k TIV) by ConstructionAIREQE

    RMS

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    Comparison

    33

    19

    8

    Ground Up Annual Expected Loss ($ M)

    AIR

    EQE

    RMS

    25

    Geographic Differences West of the Appalachians

    TN, MS & AL

    Virginia

    Sub-Perils Different East to West?

    Terrain Impact?

    Other Analysis Granularity / smoothing

    No. of events / simulated years

    South

    Easto GU AAL

    Same scale

    o Relative Scale

    Left to right - Low to high

    EQE

    Maps by Munich Re

    AIR

    RMS

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    Comparison

    0.1

    0.5

    0.0 0.1

    0.3 0.30.3

    0.8

    0.20.4

    0.8 0.80.8

    1.6

    0.50.6

    1.41.3

    0.0

    0.5

    1.0

    1.5

    2.0

    Masonry Mobile Concrete Steel Unknown Wd Frm

    GU Loss Cost (per 1k TIV) by ConstructionAIR

    EQE

    RMS

    1.1

    2.4

    0.6

    2.11.3

    0.6

    3.5

    6.1

    1.1

    3.42.6

    1.8

    6.8

    9.0

    2.3

    5.0 4.8 4.9

    0.0

    2.0

    4.0

    6.0

    8.0

    10.0

    AL GA MS NC SC TN

    GU AAL by State ($ m) AIR

    EQE

    RMS

    26

    Lower Mid West

    AAL Breakdown

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    Comparison

    0

    20

    40

    60

    80

    100

    2 20 200

    EstimatedLosses($m)

    Return Per. (yrs)

    Southeast Hypoth. Port All SCS, GU,

    AEP

    AIR EQE RMS

    0

    20

    40

    60

    80

    100

    2 20 200

    EstimatedLosses($m)

    Return Per. (yrs)

    Tornado Alley Hypoth. Port All SCS,

    GU, AEP

    AIR EQE RMS

    0

    20

    40

    60

    80

    100

    2 20 200

    EstimatedLosses($m

    )

    Return Per. (yrs)

    Ohio Valley Hypoth. Port All SCS, GU,

    AEP

    AIR EQE RMS

    27

    Hypothetical Portfolio

    EP curves

    o General

    The EP curves and AALs tell the

    same story. The maps show the different

    distributions of AAL

    But the underlying assumptions aredriving the results:

    Smoothing, granularity, numberof events, etc.

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    Comparison

    28

    A big Thank You to

    Participating Model Vendors

    Modeling Teams and Presenters

    As well as

    The Steering Committee & QC Team

    Special Recognition

    Mark BoveMapping

    Andrew MooreTechnical


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