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Report on CAS Research Working Parties. Moderator: Donald Mango, FCAS, MAAA CAS Vice President of Research and Development Director of Research and Development, GE ERC 2004 ERM Symposium April 27, 2004. Agenda. Working Parties Overview Correlations and Dependencies WP

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Report on cas research working parties l.jpg

Report on CAS Research Working Parties

Moderator: Donald Mango, FCAS, MAAA

CAS Vice President of Research and Development

Director of Research and Development, GE ERC

2004 ERM Symposium

April 27, 2004


Agenda l.jpg
Agenda

  • Working Parties Overview

  • Correlations and Dependencies WP

  • Executive Decision Making Using DFA WP

  • Elicitation and Elucidation of Risk Preferences WP

  • Q&A throughout


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What is a “working party”?

  • Essentially a task force focused on research of a specific topic or solution of a specific problem

  • Group effort with a single work product

  • Modeled on GIRO platform where it has been used successfully


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What can WP’s deliver?

  • Original Research

  • Survey papers

    • Overview of research published to date on a particular topic

    • Could be used as syllabus material

  • Compendiums of papers by different authors

    • Correlation of Risk working party will use this format

    • Similar approach taken by Reserve Variability WP

  • Studies of industry experience based on special data calls


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What are the current working parties?

  • Correlations and Dependencies Among All Risk Sources

  • Executive Level Decision Making Using Dynamic Financial Analysis

  • Elicitation and Elucidation of Risk Preferences

  • Quantifying Variability in Reserves Estimates

  • Implication of Fair Value on Asset Allocation (on hold)


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Report of the Correlation Working Party

Glenn Meyers

Insurance Services Office, Inc.


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Charge of the Working Party

  • ERM requires the quantification of the total risk of an enterprise. One must consider correlation to properly combine the individual risk components.

  • Considerations

    • Theoretical

    • Empirical

    • Computational


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Theoretical Considerations

  • Conclusion – No overriding “theory of correlation.”

  • We will provide examples of multivariate models that exhibit correlation.

  • Experts prefer the term “dependencies” rather than correlation.

    • I find myself reverting the common usage so nonexperts will know what I am talking about.


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Empirical Considerations

  • Historical problem – lack of data

    • One observation per year

  • If correlation matters, we should be able to find data that exhibits that correlation.

  • One approach

    • Create a model that depends on a “driver” for correlation.

    • Use data from several insurers to parameterize the driver.

    • Example to follow


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Computational Considerations

  • ERM demands the aggregation of segments.

  • Simulation

    • Iman Conover and Copulas

  • Fourier transforms

    • Faster than simulations, but less flexible and require more setup time.


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Chapters Written by Individual Authors

  • Common Shock Models – Glenn Meyers

  • The Iman-Conover Method – Stephen Mildenhall

  • Correlation over time – Hans Waszink

  • Aggregating Bivariate Distributions – David Homer

  • Dependency in Market Risk – Younju Lee

  • Modeling Time Series with Non-Constant Correlations – Dan Heyer

  • Correlations in a General Stochastic Setting – Lijia Guo

  • 4 CAS Members and 3 non members


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From Meyers ChapterThe Negative Binomial Distribution

  • Select a at random from a gamma distribution with mean 1 and variance c.

  • Select the claim count K at random from a Poisson distribution with mean al

  • K has a negative binomial distribution with:


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Multiple Line Parameter Uncertainty

  • Select b from a distribution with E[b] = 1 and Var[b] = b.

  • For each line h, multiply each loss by b.

  • Can calculate r if desired.


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Multiple Line Parameter UncertaintyA simple, but nontrivial example

E[b] = 1 and Var[b] = b


Low volatility b 0 01 r 0 50 l.jpg
Low Volatilityb = 0.01 r= 0.50


Low volatility b 0 03 r 0 75 l.jpg
Low Volatilityb = 0.03 r= 0.75


High volatility b 0 01 r 0 25 l.jpg
High Volatilityb = 0.01 r= 0.25


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High Volatilityb = 0.03 r= 0.45


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About Correlation

  • There is no direct connection between r and b.

  • For the same value of b:

    • Small insurers have large process risk and hence smaller correlation

    • Large insurers have smaller process risk and hence larger correlations.

  • Pay attention to the process that generates correlations.


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Estimating b From Data

=0


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Reliable estimates of b are possible with lots of data.

  • Real estimates provided by Meyers, Klinker and Lalonde

    http://www.casact.org/pubs/forum/03sforum/03sf015.pdf


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Sample CalculationsCommon Shocks to Frequency and Severity

Multiply expected claim count by a random shock.

Negative binomial count distributions

Var[Shock] called covariance generator

Multiply scale of claim severity by a random shock.

Lognormal severity distributions

Var[Shock] called the mixing parameter

Look at spreadsheet




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Parting Message

  • Build models of underlying processes.

    • Common shock model illustrated here

    • Other chapters build other models

  • Quantify parameters of models

    • Use data! (If data will never exist, why worry?)

    • Express parameters in a form that has intuitive meaning.

  • Correlation is a consequence of the models.


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Working Party:Presenting DFA Results to Decision Makers

Nathan Babcock

Conning Asset Management


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Statement of Purpose

  • “Executive-Level Decision Making Using Dynamic Financial Analysis”

  • Facilitate effective DFA presentations to senior management

  • Survey existing presentations

  • Create tools and documentation


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Summary of Events

  • Enroll volunteers

  • Survey past presentations

  • Develop graphical template and manual

  • Create sample dynamic financial analyses and presentations

  • Write summary report

  • Review presentations, report, template


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End Products

  • Summary report

  • PowerPoint template for graphs

  • Paper describing concepts behind template

  • Three sample presentations (applying template graphs)

  • Document of presentation guidelines


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Lessons of the WorkingParty Process

  • Chairpersons - management responsibilities

  • Communication with oversight committee

  • Ability to direct a group of volunteers

  • Multiple rewrites of the project timeline


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Working Party Discoveries

  • WebEx - online presentations- paired with simultaneous teleconference

  • Hyperlinks- useful in documents, not just Internet- one reading electronically can page within set of {manual, template, report}

  • Graphics



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Sample Graph: Liability cash flow

Practically

no chance of paying this much


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Sample Graph: Hedging

Hedge is inexpensive,

but useful


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Working Party:Elicitation and Elucidation of Risk Preferences

David Ruhm

The Hartford


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Risk PreferencesWorking Party

  • Motivation:

    • Most DFA and Risk Management exercises assume management has a clear, consistent, well-understood, transparent set of risk preferences

    • Such a set is a prerequisite for many risk analysis efforts, including asset portfolio composition and reinsurance purchasing.

    • The WP members and others do not believe this to be the case in general


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Risk PreferencesWorking Party

  • Goals:

    • Describe or develop techniques and exercises that can be used to assist company senior management in developing (elicitation) and refining (elucidation) a consistent set of risk preferences.

    • The Working Party may draw from research in decision theory, prospect theory, and behavioral finance.


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Risk PreferencesWorking Party

  • Framework:

    • Company level assessment (i.e., not riskiness/leverage of individual products sold) supporting planning decisions (product lines).

    • Provide decision support for the selection of the most desirable prospective portfolio mix.

      • But moving beyond Markowitz by considering many measures and timeframes.

    • Consider one-year, two-year, and three-year time horizons

    • Net Income, both absolute and scaled on PHS

    • Several likelihood thresholds (1 in 5, 10, 25, 50, 100, 250 years)


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Risk PreferencesWorking Party

  • Two Sub-Teams:

    • Senior Management Silent Auction

    • Survey Research of Psychology Studies of Preferences

  • Silent Auction

    • Attempts to elicit consistent preferences while correcting for framing effects

    • Give to Sr. Mgmt members, then plot their answers on a single chart


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Risk PreferencesWorking Party

  • Behavioral Finance

    • Non-Bayesian Forecasting: must have a prior before you can apply Bayes' Theorem. Also tend to overweight recent experience.

    • Herding: need content providers, expressing actual opinions, rather than relativistic participants (what do my peers do?). If all the peers are asking about the other peers, who is putting content in?

    • Over-reaction: beware of this tendency, which is particularly troubling in this buy-and-hold, non-exchange market. What should the threshold for reaction be? How many bad quarters or years in a row?


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Risk PreferencesWorking Party

  • Behavioral Finance

    • Myopic Loss Aversion: risk premium is a function of time horizon. If you look at returns and results more frequently (shorter time horizon), you will demand a higher risk premium.

    • Earnings Management: owner's often want unrealistic earnings stability, often at odds with the desired return.

    • Growth/Decline/Reorganization: Expansion occurs more readily than redeployment and destruction. Function in part of myopic focus on sunk costs and difficulty of exit.


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Risk PreferencesWorking Party

  • The Road Ahead

  • We need a chairperson!

  • Finalize research summary

  • Finish Senior Mgmt Silent Auction

  • Report out later this year



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