Chapter Outline 3.1THE PERVASIVENESS OF RISK Risks Faced by an Automobile Manufacturer Risks Faced by Students 3.2BASIC CONCEPTS FROM PROBABILITY AND STATISTICS Random Variables and Probability Distributions Characteristics of Probability Distributions Expected Value Variance and Standard Deviation Sample Mean and Sample Standard Deviation Skewness Correlation 3.3RISK REDUCTION THROUGH POOLING INDEPENDENT LOSSES 3.4POOLING ARRANGEMENTS WITH CORRELATED LOSSES Other Examples of Diversification 3.5SUMMARY

Appendix Outline APPENDIX: MORE ON RISK MEASUREMENT AND RISK REDUCTION The Concept of Covariance and More about Correlation Expected Value and Standard Deviation of Combinations of Random Variables Expected Value of a Constant times a Random Variable Standard Deviation and Variance of a Constant times a Random Variable Expected Value of a Sum of Random Variables Variance and Standard Deviation of the Average of Homogeneous Random Variables

Probability Distributions Probability distributions – Listing of all possible outcomes and their associated probabilities – Sum of the probabilities must ________ – Two types of distributions: discrete continuous

Presenting Probability Distributions Two ways of presenting discrete distributions: – Numerical listing of outcomes and probabilities – Graphically Two ways of presenting continuous distributions: – Density function (not used in this course) – Graphically

Example of a Discrete Probability Distribution – Random variable = damage from auto accidents Possible Outcomes for Damages Probability $00.50 $200____ $_____0.10 $5,000____ $10,

Example of a Discrete Probability Distribution

Example of a Continuous Probability Distribution

Continuous Distributions Important characteristic – Area under the entire curve equals ____ – Area under the curve between ___ points gives the probability of outcomes falling within that given range

Probabilities with Continuous Distributions Find the probability that the loss > $______ Find the probability that the loss < $______ Find the probability that $2,000 < loss < $5,000 Possible Losses Probability $5,000 $2,000

Expected Value – Formula for a discrete distribution: Expected Value = x 1 p 1 + x 2 p 2 + … + x M p M. –Example: Possible Outcomes for DamagesProbabilityProduct $ $ $1, $5, $10, $860 Expected Value =

Expected Value

Standard Deviation and Variance – Standard deviation indicates the expected magnitude of the error from using the expected value as a predictor of the outcome – Variance = (standard deviation) 2 – Standard deviation (variance) is higher when when the outcomes have a ______deviation from the expected value probabilities of the ______ outcomes increase

Standard Deviation and Variance – Comparing standard deviation for three discrete distributions Distribution 1Distribution 2Distribution 3 Outcome ProbOutcome ProbOutcome Prob $ $00.33$00.4 __________________________ $ $ $

Standard Deviation and Variance

Sample Mean and Standard Deviation – Sample mean and standard deviation can and usually will differ from population expected value and standard deviation – Coin flipping example $1 if heads X = -$1 if tails Expected average gain from game = $0 Actual average gain from playing the game ___ times =

Skewness Skewness measures the symmetry of the distribution – No skewness ==> symmetric – Most loss distributions exhibit ________

Loss Forecasting: Component Approach Estimating the Annual Claim Distribution Historical Claims Frequency Historical Claims Severity  Loss Development Adjustment Inflation Adjustment  Exposure Unit Adjustment  Frequency Probability Distribution Severity Probability Distribution  Claim Distribution

Annual Claims are shared: Firm Retains a PortionTransfers the Rest  Firm’s Loss ForecastPremium for Losses Transferred  Loss Payment Pattern Premium Payment Pattern  Mean and Variance impact on e.p.s.

Slip and Fall Claims at Well- Known Food Chain

Unadjusted Frequency Distribution Number ofProbabilityCumulative Claims of ClaimProbability _____ _____

Unadjusted Severity Distribution Interval Relative Cumulative in DollarsFrequency Probability ___-___ _____

Annual Claim Distribution Combine the _______ and ______ distributions to obtain the annual claim distribution Sometimes this can be done mathematically Usually it must be done using “brute force” statistical procedures. An example of this follows.

Frequency Distribution Number Probability of Claimsof Claim

Severity Distribution Prob.Cum. Amount of Loss Midpointof LossProb. $0to$2,000 $1, ,001to 8,000 5,000 ___ ____ 8,001to12,000 10,000 ___ ____ 12,001to88,000 50, ,001to312, , GT 312, ,

Annual Claim Distribution Cumulative Claim Amount Probability $ to2, ,001 to8,000 _____ _____ 8,001 to12, ,001 to70, ,001 to450, ,001 to511,000 _______ GT 511,

________ ________ Loss when applied to: – severity distribution – annual claim distribution

Loss Forecasting Aggregate Approach Estimating the Annual Claim Distribution Annual Claims: Raw Figures  Loss Development Adjustment  Inflation Adjustment  Exposure Unit Adjustment  Annual Claim Distribution

Loss Forecasting Aggregate Approach Annual Claims are shared: Firm Retains a PortionTransfers the Rest   Firm’s Loss ForecastPremium for Losses Transferred  Loss Payment Pattern Premium Payment Pattern  Mean and Variance impact on e.p.s.