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Casualty Actuarial Society Dynamic Financial Analysis
1998 Special Interest Seminar
Basic Track - Session 4A Basic Model for DFA
Stephen P. D’ArcyUniversity of Illinois at Urbana-Champaign
Charles C. EmmaMiller, Rapp, Herbers & Terry, Inc.
Overview
1 Description of Model - me
2 Demonstration of Model - Chuck
3 Use of Model - You (the audience)
Objectives of this DFA Model
Develop a financial model for a U. S. property-liability insurer that is:
Realistic enough to be useableSimple enough to be understood
Caveats
• Any model is a simplified version of reality
• This model deals with quantifiable risk only
– Examples of excluded items:• A line of business being socialized• Management fraud• Devastating meteor strike
Key Risks for U.S. Property-Liability Insurers
• Underwriting– Aging Phenomenon– Jurisdictional Risk– Loss Development
• Catastrophes
• Investment – Asset Value– Investment Income
Specifics Provisions of Model• Six separate, but interrelated modules
Investments Catastrophes
Underwriting Taxation
Interest rate generator Loss reserve development
• Ten lines of business• For each line of business
– New business
– 1st renewals
– 2nd and subsequent renewals
What Does This Model Do?
Simulates results for the next 5 years
Generates financial statements
Balance sheet
Operating statement
IRIS results
Indicates expected values and distribution of results for any value selected
What Information is Required?Underwriting data
Premiums and exposures, by line, state and ageRenewal patternsProjected growth ratesLoss development patternsLoss frequency and severityReinsurance program
Investment dataStatutory and market asset values by asset classMaturity and coupon rates for bondsBeta for equity portfolio
Primary Risks Reflected
• Pricing
• Loss reserve development
• Catastrophe
• Investment
Components of Pricing Risk
• Random variation– Loss frequency and severity
• Inflation affects severity– Correlated with short term interest rates– Line of business specific
• Jurisdictional risk
• Underwriting cycle
Jurisdictional Risk
State specificRange of rate changes established
Narrower range in more restrictive states
Time lag for implementing rate changeLonger in more restrictive statesIncreases take longer to implement than decreases
Underwriting CycleFour phases
Immature hard Mature hardImmature soft Mature soft
Each phase has different supply-demand function
Probability distribution for moving to different phase next period
Loss Development Risk
• Initial reserve levels based on actuarial analysis, not statement values
• Still subject to random variation
• Inflation also affects reserve development– Initial reserves reflect specific inflation rate– Changes in inflation rate affect development
Catastrophe Risk
• Poisson distribution for number of catastrophes• Each catastrophe assigned to a geographic focal
point• Based on focal point, size of catastrophe is
determined based on a lognormal distribution• Contagion factor is used to distribute catastrophe
to nearby states• Losses distributed based on market share by state
Investment Risk
BondsMarket values calculated based on term structure of interest ratesIncludes provision for default
Equities - 3 step approach1 Initial market return:
Short term interest rate + market risk premium of 8.5%2 Adjusted market return: Initial market return - 4 times change in short term rates3 Final return includes random component (mean = 0, standard
deviation = 15%)
Interest Rate Generator
Cox-Ingersoll-Ross one factor model
ondistributi normal standard a from sampling random
year one
in change annual
0854.process rateinterest of volatility
05.rateinterest mean run long
2339.reversion of speed
rateinterest short term
)(
t
rr
s
b
a
r
rstrbar
How to Obtain this Model
Access the Miller, Rapp, Herbers & Terry, Inc. homepage (www.mrht.com)
Click on DFA Model to obtain DynaMo
You need to have Excel to run this model
You should have @Risk in order to run full version of the model
How to Learn More about this Model
CAS Limited Attendance Seminar on DFA
October 1-2, 1998
Chicago, Illinois
• Explanation of types and history of DFA• Discussion of common DFA issues• Hands-on workshop using DynaMo• Supervised use of model on participant provided data