risk management, november 2004, issue no. 3 · long-anticipated round of rate increases, insur-ers...

Chairperson’s Cornerby David Ingram __________________________2

PRMIA Provides Credit to SOA Members __3

Rising Interest Rates; How Big a Threat?by Frank Sabatini __________________________4

Risk Management: The Total ReturnApproach and Beyondby Thomas S.Y. Ho _________________________8

Risk Management Seminar Visits Six Asian Citiesby David Ingram _________________________18

Book Reviewby Fred Tavan ___________________________21

Guaranteed Annuity Options or a Fine Messby Mary R. Hardy ________________________ 22

2005 Enterprise Risk ManagementSymposium ___________________________27

2004 Bowles Symposium: Extreme ValueSeminarby Steve Craighead _______________________28

Risk Management Sessions atSOA Annual Meeting __________________30

November 2004, Issue No. 3

Newsletter of the Risk Management Section

Published in Schaumburg, ILby the Society of ActuariesManagement

Risk

Table of Contents

Chairperson’s Corner

I n the end, to have an effect on the world, themathematical risk models need to betranslated into stories.

However, that is troublesome because manyrisk managers and many of those who listenhave never heard such stories before. We are allin new territory.

The risk model is an attempt to see the world ina much more realistic way than ever before. Weare trying to look not just at a most likely future,slightly conservative future or even a worst-casefuture, but to look at the shape of all possible fu-tures. Those my age or older may remember thefirst time that they saw color TV. The color pic-ture looked strange at first and these new storiesof risk models will seem strange at first, but itdidn’t take long for color TVs to look natural,and it will not take that long for stories from riskmodels to seem natural either.

There are at least three main ways that storiesabout risk and risk management need to be dif-ferent from the old black and white financial in-formation. First, risk and risk managementcannot be easily described in absolute terms.They will usually be relative to other risks, usu-ally to other similar risks within the company.When you build your first risk model, the riskand return results will be lonely with no basis forcomparison within the company. However, the“efficient frontier” model provides a simple,well-known basis for comparison. Markowitzplotted the returns of stocks and risk-free investments, but you will doubtless find it moreuseful to compare a new product risk returnagainst a stock bond continuum. Once a secondproduct risk return can also be plotted, the riskmanager can be off to the races developing thecompany’s own product-efficient frontier.Thereafter, each additional product can be seento be either “on the frontier” or under it. The

most profitable product is often not even on thefrontier. When that is seen, the real process ofunderstanding the company risk profits begins.

Second, risk, especially as it is found in long-duration, non-traded instruments such as insur-ance contracts, is multidimensional. Above Ispoke as if the risk return graph was two dimensional. Unfortunately, it is not just two dimensional. To communicate the risk of a long- duration, non-traded instrument, a mini-mum of at least six values are needed. I will callthese six values the “Actuarial Risk Profile”(ARP). A sample ARP looks like this:

Short Term Long TermExpected Return A BVolatility C DTail Risk E F

The short-term measures can be earnings-at-risk type statistics and the long-term measuresare often taken at several points in time, when itis recognized that risk changes over time. It isalso key to remember that discounting may leadto incorrect evaluations of risk for long-term products, which are not easily traded if they arenot in excellent condition. These six numbersshould be prepared for each major risk type(credit, market, hazard and operations) beforeand after adjustment for correlation. Thevolatility measure can simply be the standarddeviation or it can be some other measure thatcaptures the middle of the loss distribution. Thetail risk measure can be a VaR or CTE typemeasure.

This multi-dimensional aspect of risk is prob-lematic when the value of risk management isquestioned. That question tries to make you fitrisk and risk management into a one-dimen-sional framework. The value of risk manage-ment will only be clear if there is a reduction of

Risk Management ◗ November 2004

Risky Storiesby David Ingram

◗

Chairperson David Ingram,

FSA, MAAA, FRM, is a

consulting actuary at Milliman

USA in New York. He can be

reached at david.ingram

@milliman.com.

risk for the same return or an increase of returnfor the same level of risk. More commonly, riskmanagement will mean fine-tuning the risk re-ward profile, changing both risk and reward tobring a product closer to the efficient frontier. Ifan organization does not know what they wanttheir risk return profile to look like, risk man-agement would have no more value than a mapwould have to someone who does not knowwhere they are going.

Third, in communicating risk and risk manage-ment, there needs to be an acknowledgementthat some unknowns are less certain than oth-ers. As Donald Rumsfeld said, “there areknown unknowns and unknown unknowns.”When the market puts a value on a security,there is usually a charge for expected volatility.This is the charge for the known unknowns.However, if the market perceives that there areany significant “unknown unknowns” then therisk charge can increase dramatically.

When risk profiles are being compared betweenproducts, the risk manager should be able to in-dicate the degree to which the various productrisk models underlying the profiles depend onunknown unknowns. Otherwise, there will usu-ally be an unfair comparison between a well-tested existing product and a new product with

new benefits operating in a new market wheresome or all of the assumptions are those un-known unknowns. Ultimately, therisk manager needs to make that dis-tinction to maintain credibility.

So these three things make buildingthe risk and risk management storiesmore of a challenge. The temptationwill be there to simplify by ignoringone or more of these three complica-tions when communicating risk andrisk management. However, you willdoubtless find that re-introducingone of these elements will be even more difficultlater. Better to keep the story a little longer fromthe start.

In fact, these three ideas could be the basis for afull risk report—Part 1: How does the risk return compare among products? Part 2:—What is the Actuarial Risk Profile of theProduct? Lastly, Part 3:—How reliable are theassumptions?

Someday, the risk management profession willneed to develop a minimum standard for riskmanagement reports. To be both effective andaccurate, these are three basic elements thatwill need to be included. ✦

November 2004 ◗ Risk Management

◗

PRMIA Provides Credit to SOA Members!

Did you know that PRMIA (Professional Risk Managers’ International Association) offers actuaries cross-over exemptions against the Professional Risk Manager (PRM) certification program?

Actuarial fellows and associates are exempted from up to half of the exam modules of the PRM program.

– FSAs are exempt from PRM Exams I and II – ASAs are exempt from PRM Exam II

The normal requirements are that four exams be passed. PRM exams can be taken on any business day of the year.

For more details about the PRM and to register for the exam, please visit the PRM candidate information Web page at http://www.prmia.org/certification/candidate_info.html or contactPRMIA directly at [email protected].

For writers of fixed annuities, the impactof rising interest rates largely dependson the speed with which rate hikes are

implemented.

Now that the life insurance industry has sur-vived one of the worst bear markets in history,along with the lowest interest rates in decadesand another round in the credit cycle, will risinginterest rates be the next risk management chal-lenge for life insurers to overcome? Possibly.That depends on a number of factors, some ofwhich are environmental and others that arecompany-specific.

The recent rise in interest rates—a nearly 2 per-cent increase from the lowest point as measuredusing the five-year treasury note—has given theindustry some relief from its exposure to an ex-tended period of very low interest rates. But nowthat the Federal Reserve has embarked on along-anticipated round of rate increases, insur-ers are pondering the implications this will havefor their business and profitability.

Impact on Annuity WritersRising interest rates undoubtedly present thegreatest threat to writers of fixed deferred annu-ities. Deferred annuity crediting rates havebeen lingering at or slightly above contractualminimums for the past few years. Portfolio rateshave declined to the point where some carriershave not been able to achieve their targetspreads, resulting in depressed earnings.

To compound the problem, policyholders havecome to the realization that the 3 percent mini-mum guarantee on fixed annuities is a fabulousdeal in the current environment, and havetherefore, held on to their policies. Some com-panies have stopped writing new business toavoid selling products with current credited

rates that cannot be supported by new invest-ments. Almost all writers have lowered mini-mum guarantees to 1.5 percent or 2 percent.

Sustained levels of very low interest rates pro-vide sufficient time for portfolio returns to de-cline further as higher-yielding investments oninsurers’ books mature and are replaced by in-vestments with significantly lower yields. Withthe continuation of low rates, the gap betweeninvestment returns and minimum guaranteesnarrows even more. The rate at which this oc-curs for a particular company depends on itsparticular circumstances, but the most impor-tant factor is the extent to which there is a mis-match between the company’s asset and liabilitydurations.

Companies historically have purchased invest-ments that have a longer life than the liabilitiesthe company has acquired. Since longer-terminvestments often have higher yields, this prac-tice has provided the annuity writer with eithera greater profit expectation and/or a more com-petitive credited rate. However, as interest rateshave fallen, the liability duration has becomelonger as the minimum guarantees have beenreached, and policyholders have shown agreater tendency to keep their policies in force.

The historically low levels of interest rates haveplaced pressures on realized spreads that havebeen reflected in earnings results in recentyears. Low interest rates work toward an insur-er’s detriment because they cause investmentsto mature more quickly and portfolio yields todecline more rapidly than liability rate changes,accelerating the spread compression caused bythe minimum guarantees. To dampen the im-pact on spreads, many companies have main-tained an intentional mismatch of assets andliabilities through the normal management ofthe asset portfolio.


Rising Interest Rates: How Big a Threat? by Frank Sabatini

◗

Frank Sabatini, FSA,

MAAA, is a partner in

Ernst & Young’s Insurance

and Actuarial Advisory

Services practice. He

can be reached at

(860) 524-3511 or

[email protected].

From this perspective, the recent rise in interestrates is actually good news for fixed annuitywriters. The rise has produced higher invest-ment yields on new investments without a corre-sponding increase in credited rates, resulting inimproved spreads. The rates that annuity writ-ers are crediting today still represent an attrac-tive alternative to consumers, especiallyrelative to certificates of deposit. Therefore, therise in rates has benefited annuity writers.

It’s Not How High, but How FastBut what happens when interest rates rise sud-denly? This situation last occurred in the late1970s and early ’80s, when interest rates rosequickly and dramatically. What occurred thencould happen again, with a similar impact onannuity writers. At that time, the rise in ratesoutpaced the ability of existing investment port-folios to keep pace with credited rates seekingnew deposits and supported by the current high-er-yielding investments.

Companies had two choices. They could eithersubsidize existing credited rates to keep cus-tomers from leaving to get the higher returns, orthey could suffer the financial consequences oflosing the customer, including a loss of spreadincome and capital losses from having to liqui-date existing assets at a loss to fund the depar-tures. Often, the departing customer was fundedby the arrival of a new customer, who got thehigher crediting rate but was backed by thelower-yielding asset, thus constructively realiz-ing the financial loss.

Back then, of course, we had double-digit inter-est rates. But it’s not the level of rates that mat-ters—it’s the relationship between new moneyand in-force credited rates. Right now, new-money credited rates are close to 4 percent, andin-force policy credited rates are at the 3 per-cent minimums. This differential provides littleincentive for existing policyholders to switch toa new contract, especially when many of themwould have to accept a new surrender charge pe-riod to make the change. But a rapid rise in new-money rates will quickly change this dynamic.

The fact that significant portions of an insurer’sannuity business are outside the surrender-charge period or carry a relatively small penaltyfor the policyholder wouldlikely create even greater fi-nancial pressures for the in-surer. Many existingpolicyholders would have toaccept a new surrender-charge period to move to ahigher-rate contract, but incases where existing policy-holders incur either nopenalty or only a smallpenalty for withdrawingfunds from their contract,there will still be a signifi-cant incentive to accept the new contract, un-less access to the funds is a consideration.

For policies that were issued during the past fewyears and have significant withdrawal penaltiesstill in effect, the issue is different. When thepolicyholder has to incur a penalty to receive thehigher rate, the rate improvement has to com-pensate for the penalty fairly quickly to justifythe contract change. Bonus-rate products aredesigned to address this issue for the policy-holder, and the sheer existence of these prod-ucts makes the insurer’s exposure to anexchange even greater.

It’s difficult to forecast how policyholders anddistribution systems will respond as rates rise.However, as the gap between new-money cred-ited rates and existing credited rates widens, thepropensity for policyholders to exchange the ex-isting policy will increase. Conventional wis-dom, which is not supported by actual historicalexperience, has been that policyholders willbegin to accelerate their movement to higherrates when the gap reaches 2 percent or more.As a result, it is believed that a further rise in in-terest rates of 1 percent to 2 percent would like-ly trigger increased lapsation unless creditedrates were increased on in-force policies. Aneven more dramatic rise would accelerate thepotential for disintermediation.


continued on page 6 ◗

◗

“

”

How companies have

positioned their assets

and liabilities prior to

an interest rate hike,

and the nature of the

hedging strategies that

are in place, clearly will

have an impact on the

financial effect they

experience once rates

begin to rise.


Unfortunately, insurers’ ability to credit higherrates on in-force policies—and remain prof-itable—is limited by their returns on existinginvestments. A rapid rise in rates, e.g., over a12-month or shorter period, would not allowportfolios to respond. Insurers then would befaced with the choice of subsidizing creditedrates or allowing policyholders to leave. In con-trast, a more gradual rise over several yearscould be manageable.

Different Results for DifferentCompaniesThe financial impact of a rapid rise in interestrates will be different from company to compa-ny, based on a number of company-specific fac-tors, including the insurer’s distribution system,balance sheet diversification, product line mixand asset/liability matching approach.

Balance sheet diversification.The financialimpact will show up on the balance sheet as re-duced earnings from either reduced spreads, asliability credited rates rise faster than portfolioearnings, or as spreads earned on a shrinkingbase as policyholders leave the company in pur-suit of higher credited rates. In reality, the im-pact will be a combination of both effects.Insurers realize that policyholders will toleratesome gap and will manage in-force policy cred-its to mitigate departures without having to pro-vide market rates to all existing policyholders.

Distribution system. An insurer’s ability toretain existing policyholders is also tied to thedistribution system that produced the businessfor the company. If the producers are independ-ent, the insurer faces increased pressure tomaintain in-force credited rates at competitivelevels; otherwise, the business is likely to moverapidly. Distribution systems with greater loyal-

ty to the manufacturer are likely to tolerate ahigher gap between market rates and in-forcecredited rates. Therefore, how a company re-sponds to the higher-rate environment will beinfluenced by the distribution system’s likelybehavior.

Product line mix. The mix of a company’sbusiness will also play a role in determining theseverity of the financial impact. Clearly, compa-nies with diversified balance sheets will see asmaller effect. Many other products besides de-ferred annuities have interest rate sensitivity.Universal life and similar products would bemost affected; however, the level of disinterme-diation with these products could be signifi-cantly lower. Other product lines, such asimmediate annuities, traditional life insurance,long-term care and disability income, will actu-ally benefit from the rise in rates. Finally, somedeferred annuity writers have written market-value-adjusted (MVA) business with fixed-rateguarantees. This product design protects thecompany from disintermediation, although anyALM mismatch could negatively impact prof-itability.

Asset/liability matching. As noted above, thelevel of surrender charge protection also willhave an impact on the financial implications ofrising rates. In general, companies that haveasset/liability mismatches of one year or greaterwill be more at risk than companies that haveaccepted a lower spread over the past few yearsto stay more closely matched.

In addition, some companies have implementedrisk management programs designed to respondto the rapid-rise scenario by implementing de-rivatives-based and/or carefully designedstrategies to mitigate the effects of a rise in inter-est rates. How companies have positioned theirassets and liabilities prior to an interest ratehike, and the nature of the hedging strategiesthat are in place, clearly will have an impact onthe financial effect they experience once ratesbegin to rise.

Rising Interest Rates◗ continued from page 5

◗

◗


Hoping for the Best … Fearing theWorstWhat should we hope for? A slip back to lowerrates or a rapid increase in rates will present theindustry with serious challenges. Marginswould shrink in either scenario. The best scenario is that interest rates will stay wherethey are or rise gradually over time.

For those not wanting to risk an acceleration in upward rate movements, now would be a good time to evaluate investment and product management strategies that are designed to mitigate this exposure or respond as it emerges.The companies that are better prepared will produce better earnings in a rising-interest-rate environment. ✦

Get your copy of the Society of Actuaries’ newest publication and first-ever book on the topic, Life Insurance and Modified Endowments Under Internal Revenue Code Sections 7702 and 7702A later this month. This innovative work provides a practical look atthe issues surrounding federal income tax treatment of life insurance products, including the in-depth information on the statutory definition of life insurance found in sections 101 (f)and 7702, and the modified endowment rules in 7702A. An essential resource for product designers and those dealing with compliance issues on a daily basis, the book also delivers background and historical information to help readers appreciate the context in which these sections were developed.

Leading experts in the field, actuaries Chris DesRochers, Doug Hertz and Brian King teamed up with attorney John Adney to write a well-balanced book. The result is a text that reflects the actuarial theory, tax policy and political compromises underlying the statutory limitations. Formulas and calculations are provided, along with extensive legalanalysis and citations.

To purchase a copy, visit the Bookstore on the SOA Web site—www.soa.org. Just click on Research and Publications.

7702 Announcement


◗

Thomas S. Y. Ho, PhD, is

president of the Thomas Ho

Company, executive vice

president at BARRA, and

founder/CEO of Global

Advanced Technology

Corp. He can be reached

at [email protected]

Risk management for insurers is quitedistinct from that for the trading floors.The main differences arise from the

insurer’s liabilities. They are in general longdated and illiquid with no secondary marketsand some of their risks cannot be replicated orhedged. As a result, the management of the lia-bilities tends to be based on book value. Themanagement performance metrics are not basedin on marking to market value, but on perform-ance over a much longer time horizon. For thesereasons, “enhancing the equity” based onmarking to market or over a short-term horizoncan no longer be used as the performance met-ric. VaR approach has to be extended to themanagement of insurance liability before it canbe useful; and, to date, managing the VaR risk ofthe “equity” of an insurer’s balance sheet isoften not considered relevant in practice.1

This paper discusses the risk management ap-proaches of insurers. I will describe some of thecurrent practices of the total return approach.Then I will describe how the total return ap-proach is used in managing risks as a process.Finally, I propose a model that takes future salesinto consideration and determines the appropri-ate fair valuation method of an ongoing busi-ness. The methodology enables us to determinethe goal of managing the risk on an enterpriselevel, taking other performance metrics likeearnings at risk, into account.

A. Risk Management Practice for Life Companies: the Total Return Approach

There is no one standard approach to risk man-agement for life companies in practice.Different insurers have their methodologies andprocedures in managing risk. On the one hand,

there is regulation in place to ensure that insur-ers comply with the adequacy of their assets insupporting their liabilities. This regulation iscalled cash-flow testing. Such a risk manage-ment approach is confined to managing the solvency risk. How should we manage the eco-nomic value of the insurer’s assets and liabilities?

The total return approach is a risk managementprocess that can be used to measure, monitor,report and manage the assets and liabilities onan economic basis. The total return approachhas been described elsewhere (see Ho,Scheitlin and Tam 1995). I will provide a briefsummary here. The total return approach can beused as an extension of the cash-flow testingmethods.

The approach can use the liability models de-veloped in the cash-flow testing to determinethe cash flow of each product under differentscenarios. The main difference between the twoanalyses, cash-flow testing and total return approach, is the use of present value measuresin the total return approach versus the use ofcash-flow projections in cash-flow testing. Byusing the present value concept, the analyticalresults do not depend on future reinvestmentstrategies. This is because when assets are fair-ly priced, future investment strategies (buyingor selling the assets) would not affect the portfo-lio value today. And the present value measurefor the assets is the same as the market value ofthe assets. Therefore, the total return approachcan analyze assets and liabilities in one consis-tent framework. The total return approach hasfour steps: (a) fair valuation of liabilities, (b) de-termination of the liability benchmark, (c) de-termination of the asset benchmarks and (d)

1 Portions of this article are taken from “The Risk Management of Insurers” by Thomas S. Y. Ho in the Journal of Investment Management, forthcoming.

Risk Management: The Total Return Approach and Beyondby Thomas S.Y. Ho

establish the return attribution process. We nowdescribe them in turn.

a. Fair valuation of liabilitiesFair valuation of liabilities begins with the de-termination of a pricing curve. The pricingcurve is the time value of money curve that isused to discount the liability cash flows. Thecurve can be the Treasury curve or the swapcurve. The cash flows of the liabilities are dis-counted by this curve to determine the presentvalue of the cash flows. In the cases where the li-abilities have embedded options, we use an ar-bitrage-free interest rate model to determine theinterest rate scenarios and we determine thepresent value of the cash flows. In essence, themethod uses the arbitrage-free valuation ap-proach to determine the fair value of the liabili-ties. As a result, the liability cash flows arevalued relative to those of the capital markets.Assets and liabilities are evaluated in one con-sistent framework. This method has been dis-cussed extensively in other papers. (Ho 2000;Ho, Scheitlin, Tam 1995; Ho and Lee 2003).

As I mentioned in the previous section, the liabilities have characteristics that are difficultto treat like capital market assets. For example,some liabilities have a time to termination ofover 30 years, beyond most of the capital marketbonds. In these cases, one approach may be toassume that the yield curve is flat beyond a cer-tain maturity to determine the fair value of theseliabilities. Therefore the assumptions of themodeling of liability have to be specified, ingeneral.

b. Liability BenchmarkWhen the liability is first sold to the policyhold-er, a constant spread is added to the pricingcurve such that the present value of the liabilityis assured to equal the value of the premium ofthe liability sold. This spread is the option-adjusted spread of the liability and this spreadis called the required option-adjusted spread(see Ho, Scheitlin, and Tam 1995.) The finan-cial model of the liability becomes a representa-tion of the actual liability. In particular, the

liability model captures the simulated project-ed cash flow of the liability under different market scenarios. And the market scenarios areconsistent with the observed interest rate levels,the interest rate volatilities and other market parameters.

Using the liability model, we then decomposethe liability to basic building blocks. For exam-ple, we can represent the liability as a portfolioof cash flows with options. These options can becaps and floors. Or they can be swaptions. Sucha decomposition may allow management tomanage the derivatives separately from the cashflows. This decomposition has been explainedin Ho and Chen (1996). For example, Wallace(2000) describes the construction of the liabili-ty benchmark in the management of a block ofbusiness, which can be decomposed into a port-folio of cash flows and a portfolio of interest ratederivatives.

The liability benchmark captures the salientfeatures of the liabilities in terms of their capitalmarket risks. As a result, the method provides asystematic way to separate the market risks andthe product risks, like mortality risk. The sepa-ration of these two types of risks enables us touse the capital market instruments to managethe capital market risks embedded in the liabil-ities and to use actuarial methods to manage theproduct risks. In sum, the liability benchmarkmay be a liability financial model or a set of fi-nancial models represented by specific cashflows and market derivatives like caps andfloors. This liability benchmark replicates theliability in their projected cash flows under abroad range of scenarios. The effectiveness of the liability benchmark depends of on itsability to capture the liability cash flows under stochastic scenarios.

An insurance company may have multiple products and product segments. Therefore, theinsurers may have multiple liability bench-marks. These benchmarks have to be revisedperiodically since the actual liabilities’ charac-teristics may change over time and the bench-

“

”

In sum, the liability

benchmark may be a

liability financial model

or a set of financial

models represented by

specific cash flows and

market derivatives like

caps and floors.



◗

marks may become less accurate in replicatingthe behavior of the liabilities. This revisionshould be conducted when the liabilities under-go significant changes.

c. Asset BenchmarksThe asset benchmarks are derived from the lia-bility benchmark. There are two types of assetbenchmarks: an asset portfolio benchmark anda sector benchmark. The procedure to deter-mine the asset benchmarks for a particular lia-bility benchmark may follow three steps: (1)specify the investment guidelines, (2) constructthe asset benchmark, (3) and construct the sec-tor benchmarks.

1. Investment Guidelines

The procedure begins with the senior manage-ment laying out some specific guidelines aboutthe appropriate risk that the company is willingto take. These guidelines may reflect the prefer-ences of management and the constraints im-posed on the company from outsideconstituents. A typical guideline may addressthe four characteristics of an asset portfolio.

Interest rate risk exposure limits can be set bystating the maximum allowable duration mis-match, or key rate duration mismatch, between

the liability benchmark and the portfolio bench-mark. Further, there may be a maximum expo-sure of negatively convex assets that may beallowed in the benchmark.

Credit risk exposure limits may be set by themaximum allowable percentage of assets thatare categorized as high-yield assets. There canalso be a minimum percentage of assets that arerated as “A” and above.

Liquidity in the asset portfolio is assured by themaximum allowable percentage of assets thatare considered less liquid (or one could statethem as illiquid assets). Assets that fall in thiscategory, for example, are private placementbonds and commercial mortgages.

The senior management of some companies mayalso place overall broad guidelines on asset allocation—in the form of maximum or mini-mum allocation to certain specified classes ofasset sectors.

Several other factors also affect the overallguidelines. For example, the insurance compa-nies may incorporate the rating agencies’ meas-ures of risk, mimic the asset allocation of peergroup companies, and take the desired level ofcapital of the company into account.

2. The Asset Benchmark

The asset benchmark consists of several sectorbenchmarks (which are described as follows)with appropriate weights to each asset class(which is often referred to as the asset alloca-tion). It represents the mix of asset classes andtheir weights that will meet the desired needs ofthe liabilities while catering to the restrictionsimposed by the investment guidelines.

The design takes into account the liquidityneeds, the duration (or key rate durations) andconvexity profile, the interest crediting strategy,minimum guarantees, required spread over thecrediting rates and other product features. All ofthese attributes are not always identifiable


RM: The Total Return Approachand Beyond

◗ continued from page 9

◗

through the liability benchmarks. Therefore, itis important that the design incorporates seniormanagement’s perspective on the allowable riskthat the company is willing to take. The risk isdefined to include the model risks as well as themarket, credit and product risks.

The portfolio managers then add specificity to the benchmark by reviewing the require-ment/behavior of the liabilities, the desiredminimum spread and the guidelines specifiedby the senior management.

The process of refining the benchmark balancesthe asset allocation and the duration distribu-tion of the assets within each asset class. Thelatter defines the duration of the benchmark andconsequentially the targeted duration mis-match between the assets and the liabilities.

Therefore, the asset benchmark is an asset port-folio that satisfies all the constraints deter-mined from the analysis of the liabilitybenchmark, the investment guideline and theasset portfolio management preferences.

3. The Sector Benchmark

The sector benchmark is specific to an assetsector or class of an asset (like investment-grade domestic corporate bonds, collateralizedmortgage-backed securities, high-yield securi-ties and asset-backed securities). The portfoliomanager of each market sector manages theportfolio using the sector benchmark to measurethe relative risks and returns of the portfolio.The manager’s performances are then analyzedbased on the sector benchmarks.

Thus far, we have described an asset benchmarkthat replicates the characteristics of the liabili-ty benchmark. However, if the asset and liabili-ty management process does not requireimmunizing the market risks, then the assetbenchmark can be constructed with mismatch-ing asset and liability market risks. For exam-ple, some life insurers use a mean varianceframework to determine their strategic assetportfolio positions. Other insurers use the

distribution of the present value of the cashflows of assets net of liabilities to determinetheir optimal asset portfolio.

d. Return attributionReturn attribution is concerned with calculat-ing the total returns of the assets and the liabili-ties and determining the components of thereturns. The purpose of breaking down the returns into its components is to detect thesources of the risks and attribute the returns to decisions made in the asset and liability management process. In identifying the impactof the decisions on the insurer’s asset and liabil-ity combined total return, we have developed a procedure with a feedback effect to the management process.

The return attributions can be calculated as follows. Over a certain time horizon, say onemonth, we can determine the portfolio total re-turn and the liability total return. The total re-turn of an asset follows the conventionaldefinition, and that is the change in the unreal-ized profit and loss plus the cash flow (divi-dends, coupons and actual gain/loss from thedisposition of the assets) to the insurer’s portfo-lio over that period. The liability total return isdefined analogously. It is defined as the changein the fair value of the liability plus the cash out-flows of the liability over the holding period.

Both the total returns of the assets or the liabili-ties can be decomposed into the basic compo-nents. These components are the risk-freereturns, the option-adjusted spreads, the keyrate duration returns, transactions andcheap/rich changes. Specifically, the total return of the asset portfolio is given by:

And the liability portfolio total return is given by



In identifying the impact

of the decisions on the

insurer’s asset and lia-

bility combined total

return, we have devel-

oped a procedure with a

feedback effect for the

management process.

“

”

◗

where r is the risk-free rate. OAS is the option-adjusted spread of the asset portfolio. ROAS is the required returns of the liability portfolio.krdA(i) and krdL(i) are the key rate durations of the assets and the liabilities respectively.

is the shift of the ith key rate relative tothe forward yield curve. Finally, eA and eL arethe residuals of the asset total returns and the li-ability total returns equations respectively.There may be other basic components depend-ing on the asset and liability types. For example,there may be factors explaining the convexityeffect and the product risks. For clarity of expo-sition, I only describe some of the components

here. Details are provided in Ho, Scheitlin andTam (1995).

Product risks are priced by the margins, whichare the spreads that are part of the required op-tion-adjusted spreads. And each product riskwill be measured from historical experience.Therefore, while the asset benchmark has notincorporated the product risks explicitly, it hastaken the margins for the product risks into ac-count. The margins can then be compared withthe experience of the product risks to determinethe risk and return tradeoff in the pricing of theproducts.

B. Beyond the Total ReturnApproach: Risk Management as a Process

The returns attribution process is becomingmore important in asset management. Theprocess relates separate departments requiringthe departments to coordinate. Stabbert (1995)describes how such a coordination can be organized. Typically, return attribution, basedon total return approach, is not commonly foundin liability management. The lack of use of re-turn attribution method in liability managementmay be explained by the slow adoption of fairvalue approach to analyze the liabilities. Withthe recent emphasis on fair value accounting toinsurance companies, the return attribution ap-proach may be adopted in the risk managementpractice in the future.

Risk management considers asset and liability management as a process. In thisprocess, we then can measure the risks and the performance of each phase, and a risk/return trade-off analysis is conducted for each phase of the process. A more detailed description of an investment cycle can be foundin Ho (1995) where the management of the organization is discussed.

We can construct asset and liability manage-ment as a cycle. It should be clearly organized in

◗




An investment cycle describes the process for making investments. There are four “phases of the investmentcycle” that will oversee the milestones: the requirement phase, design phase, test phase and implementa-tion phase. Each phase will provide the checks and balances for the following phase. The boxes for invest-ment objective, market outlook, investment strategies and performance evaluation are indicating thatthey are actions to take one phase to another phase.

Figure 1: An Investment Process

∆r i( )

order to monitor each business unit’s responsibilities to determine the: asset and liability management objective, market out-look, investment strategies, product manage-ment and performance evaluation. There arefour “phases of the asset and managementcycle” that oversee the process: the require-ment phase, design phase, test phase and im-plementation phase (see Figure 1). Each phaseprovides the direction for the following phase.The requirement phase establishes the goals tomeet the client’s needs. This in turn dictates the objective. The design phase sets strategies forportfolio managers, which formulates the mar-ket outlook from the investment objective. Thetest phase uses the market outlook to formulateinvestment strategies. The implementationphase executes the investment strategies thatresult in trades and portfolio performances,which completes the investment cycle.

Each phase can be managed separately to as-sure that each phase’s performance ties back tothe asset and liability management objectives, aprocess similar to quality assurance manage-ment. For example, we can decompose the riskof the process into the risks of the phases of thecycle so that each risk can be measured sepa-rately. In measuring the risk of an investmentcycle, risk managers can manage all the phasesof the asset and liability process.

Risk managers can implement a more complexinvestment cycle that will include the designphase of all the proposed business strategies tomonitor and adjust the business cycle. Thebusiness cycle would enable risk managers toprovide risk exposures, risk sources, risk limitsand policies, etc. Implementing risk manage-ment within a business control cycle would ben-efit the senior management when it comes tomaking decisions to optimize the shareholders’value, using all the measures that impact thebalance sheet’s risk and profitability.

The risk management of investment using aprocess described above illustrates how enter-

prise management can be implemented by mod-eling the business processes of the enterprise.Now we can relate this asset and liability man-agement process to the firm’s organization.

The insurer has five departments, which aresenior management, ALM Committee, portfoliomanagement, line business and risk manage-ment. The responsibilities of each departmentare given as follows:

(1) Senior management is responsible for theoperations of the insurer and setting the insurer’s performance targets; senior management includes the managementcommittee that represents the stakehold-ers’ interests.

(2) ALM is responsible for determining theasset and liability structure. For the pur-pose of this paper, asset and liability man-agement also coordinates with riskmanagement.

(3) The Portfolio management is responsiblefor investments. Investments include assetallocation, sector rotation and securitiesevaluation and trading. For most insurers,portfolio management is separated intotrading and other functions. The proposedmethodology can be used for drilling downto such disaggregated levels.

(4) Line business is responsible for the sale ofproducts.



◗

(5) Risk management, as mentioned above, isresponsible for the management of theprocess.

The model can be used in a multi-period con-text. To simplify the explanation, we will presentthe model as a one-period model. The periodrefers to the reporting period, which may be onemonth or three months in length. The model willbe used on a prospective basis when it is used forrisk management. At the same time, we will alsouse the model on a retrospective basis when it is used for performance measures.

The model can be used on a retrospective basisfor measuring the performance of each depart-ment in the process of the commercial bankingbusiness. This is accomplished by setting upasset benchmark returns (r*) and liabilitybenchmark returns (r* ). The benchmark re-turns are the returns of portfolios (loans or de-posits) based on the average performancedetermined by senior management. For the as-sets, this is often accomplished by using some

broad-based market index, tilted to reflect thedesired risk exposure of that asset and liabilitymanagement view. Similarly, the liabilitybenchmarks are determined by the liabilitymodeling without assuming significant superi-ority in knowledge and information of the line ofbusiness. The performance of the ALM depart-ment depends on the views that the ALM de-partment takes and how their views are reflectedby the benchmarks that they establish for eachreporting period. Therefore, their performanceis measured by the difference of the returns ofthe asset and liability benchmarks.Specifically, we have:

y(ALM)=r* A–r* L

where A is the asset value, L is the liability value and y(ALM) is the performance measure.r* and r* are the returns of the liability and asset benchmarks respectively.

The performance of the portfolio management,y(PM), is measured by the expected return of the asset portfolio net the expected returns ofthe benchmark on a prospective basis. For return attribution on a retrospective basis, theperformance would be the realized returns of theassets rA net the realized returns of the asset benchmark rA. Specifically, we have:

y(PM)=r* A–r* A

The performance of the line business (y(LB)) ismeasured by the profits they generate from thenew sales and their management of the liabili-ties in their performance against the bench-marks:

y(LB)=pv+r* L–r L

where p is the profit margin and v is the sales volume.


◗



A L

A A

L A

L L

L

A

We can now specify the corporate performancemeasure by noting that:

y=y(ALM) +y(PM) +y(LB)–FC

where FC is the overhead costs of the manage-ment of the business. The senior management’srole is to ensure that the income y will enhancethe shareholders’ value by managing theprocess and ensuring that the net income ymeets the shareholders’ expectations.

It is important to note that this paper proposes aset of performance measures. It does not sug-gest that management compensations should bedirectly related to these measures, even thoughthese measures can be part of the inputs. It alsodoes not propose a management system to dealwith human resource issues. It focuses on theprocess-engineering aspect of the risk transfor-mation and control of an insurance company.

While performances in general are additive,risks are not. Indeed, not only are risks diversi-fiable so that they are not additive, but risks areoften cross-hedged across different businesslines. Therefore, risk attribution must takethese issues into account to assure coherence inthe analysis.

C. Beyond the Total ReturnApproach: The Corporate Model Approach

The total return approach focuses on managingthe risks of the economic value of the in-forcebusiness. Using benchmarks in our risk man-agement processes can assist us in managingour business, but these approaches have theirlimitations.

First, to manage the risk of our shareholders’value, we need to relate our models to the valuesof the businesses, identifying the sources ofrisks to our shareholders, and not only to the in-force business. There is no direct relation-

ship between managing the total returns of theassets and liabilities to the shareholders’ value.

Second, many products do not fall into the usualgenre of a spread product where the total returnapproach is effective. These products may havesignificant product risk with lapse or renewalrisks, more akin to a going concern business.For example, long-term health care insurancein life insurance is more like the general insur-ance where the potential product liability is significant and difficult to estimate.

The model that brings the two approaches to-gether in one consistent framework is called thefair value corporate model. The corporatemodel is described in more detail in Ho and Lee(2004). In the corporate model approach, we de-termine all the assets and liabilities by arbi-trage-free relative valuation models. Wecalibrate all the assets and liabilities to the ob-served securities prices.

The extension of the approach is based on incor-porating the following features of modeling:

1. We specify the models of new sales. Fromthese models, we can determine the freecash flow generated by the product sales and the asset and liability management. Thepresent value of the free cash flow is then related to the market capitalization via relative valuation approaches.

2. We relate the economic value to the GAAPfinancial statements. Therefore, earnings atrisk can be calculated.

3. We determine the appropriate discount rateof the business in such a way the valuation isconsistent with the total return approach.

4. We determine optimal risk management tomaximize the market capitalization of the insurer subject to market constraints,like the rating agencies’ measure of credit risks and the stock analysts’ demand on performance metrics.

◗



While performances in

general are additive,

risks are not.

“

”

◗


D. Conclusions

While all insurance companies are engaged inselling insurance products, they differ signifi-cantly in their approaches to managing their as-sets and liabilities and in managing their risks.Indeed, asset liability management and riskmanagement in practice is quite fragmentedwithin the industry. The methods used dependon the product within the company or depend on the business units. The approach is clearlydifferent between one company and another.

We have shown that the life insurance compa-ny’s risk management practice focuses on in-force business. They seek to manage the assets and the liabilities on their balancesheets. The fragmentation confines us in theusefulness of the asset/liability and the riskmanagement processes. As a result, an

insurer’s risk management practice may be lim-ited to determine whether a product’s risk can be appropriately managed or a business unit satisfies a solvency test, but we cannot deter-mine how each business unit should be optimal-ly managed. Methodologies have been proposedto answer these questions.

I propose a general approach, which is to incor-porate the future projected sales and the valua-tion of the firm in the financial modeling of theinsurance company. Under a more integratedframework, we could deal with risk manage-ment in a broader context. Specifically, I pro-pose using a corporate model that uses thearbitrage-free approach in valuing the assetsand liabilities and incorporates the future salesof the products to develop a going concern approach to determine the free cash flows of theinsurer. We then relate the risk management impact on the assets and liabilities to the marketcapitalization of the insurer and the impact ofthe firm’s financial statements. In doing so, wecan relate risk management to many perform-ance metrics of the firm, like earnings at risk.Given this relationship, we can then develop aconsistent methodology to determine the opti-mal risk management.

References

Ho, Thomas (1992) “ Key rate durations: meas-ure of interest rate risk” Journal of FixedIncome, volume 2 number 2 September 1992.

Ho, Thomas (1995) “Quality-Based InvestmentCycle” Journal of Portfolio Management.

Ho, Thomas (2000) “Market valuation ofLiabilities: Transfer Pricing, profit release, andcredit spread” The Fair Value of InsuranceBusiness, edited by Irwin Vanderhoof andEdward Altman. Kluwer Academic Publishers.



◗


Risk Management Working Groups (leaders)

RMTF/RM Section Groups

ERM (Jenny Bowen)Extreme Value Models (Tom Edwalds)Health (John Stark)Health RM Specialty Guide —Rajiv DuttHealth Solvency—Trevor Pollitt

CRO (Juan Kelly)Policyholder Behavior in the Tails (Jim Reiskytl)RBC Covariance (Jim Reiskytl)Risk Metrics (Fred Tavan)Pricing for Risk (Novian Junus)Risk Management Survey (Charles Gilbert)Integration of Risk Models (Ken Seng Tan)

Joint with CAS

Standard Risk & Risk Management Terms(Open)Risk Tolerance (Larry Johnson)Operational Risk Management (Mark Verheyen)

Joint with Investment Section

Credit Risk (Dave Ingram)

All groups are open to additional volunteers.No experience necessary.

The author would like to thank Mark Abbott, Marlys Appleton, Edwin Betz, Ashok Chawla, Robert Lally, AlexScheitlin, Gerd Stabbert, Kin Tam and Marsha Wallace for their helpful comments and suggestions. Any errorsare the responsibility of the author.

Ho, Thomas and Michael Chen (1996) “Arbitrage-free bond canonical decomposition”Fixed income solutions edited by Thomas Ho.Irwin Professional Publishing pp. 283-298.

Ho, Thomas and Sang Bin Lee (2004) TheOxford Guide to Financial Modeling OxfordUniversity Press.

Ho, Thomas, Alex Scheitlin, and Kin Tam(1995) “Total Return Approach to PerformanceMeasurements”, The Financial Dynamics of theInsurance Industry edited by Edward Altmanand Irwin Vanderhoof. Irwin ProfessionalPublishing pp. 341-377.

Lally, Robert (1993) “Managing fixed-incomeassets within a multi-asset class insurance port-folio” Fixed-income portfolio management edited by Thomas Ho, Business One Irwin, pp.87 – 100.

Stabbert, Gerd (1995) “An analytical approachto asset/liability performance measurement”Fixed income investment edited by Thomas HoIrwin Professional Publishing pp. 205-229.

Wallace, Marsha (2000) “ Fair-value account-ing for financial liabilities” The Fair Value ofInsurance Business, edited by Irwin Vanderhoofand Edward Altman. Kluwer AcademicPublishers. ✦


◗

David Ingram, FRM, FSA,

MAAA, PRM, is a consulting

actuary at Milliman USA in

New York. He can be

reached at david.ingram

@milliman.com.

You are always at the mercy of yourdumbest competitor,” George Tangwarned the crowd at the Joint Regional

seminar on Risk Management in Kuala Lumpur.In fact, George actually said that six times to fiveadditional audiences in Taipei, Hong Kong,Shanghai, Seoul and Tokyo. George is an actu-ary at the MassMutual Mercuries Life InsuranceCompany in Taipei. He addressed many of thepractical issues for risk managers practicing inAsia, pointing out that risk management goesbeyond ALM to encompass operations, prod-ucts, financials, investments, legal and market(sales) conduct.

The most popular part of George’s presentationin each and every city was when he addressedAsian cultural issues that impede risk manage-ment effectiveness. Common behaviors such asreticence to say a direct “no” and the propensi-ty to avoid controversial topics are often in di-rect conflict with effective risk management.George suggested strategies to overcome theseproblems such as presenting solutions alongwith problems and seeking personal opinions inprivate meetings.

Kuala Lumpur was the starting point for thetraveling seminar. The seminar was jointlysponsored by the Faculty and Institute ofActuaries, the Society of Actuaries ChinaRegional Committee and the Institute ofActuaries of Australia as well as local actuarialbodies in each city. The audience topped 100 inKL as it did in every city and the seminar washeld in the shadow of the Petronas towers.Interest in KL was high since insurance compa-nies in both Malaysia and Singapore are beingchallenged by their regulators and sometimesalso by bank owners or partners to implementbank-style risk management.

“You are not alone. Insurers in many countriesare suffering,” said Steve Miles, a Tillinghast

consultant from Singapore. Steve talked aboutstrategies for a low interest rate environment.He described how regulators were taking com-pletely different approaches to the problemscaused by low interest rates in Japan, China andSingapore.

In Japan, regulators played for time, hoping thatrates would rise, by bending solvency rules, ma-nipulating interest rates and providing subsi-dies or preferred loans. Since interest ratesfailed to rise, those actions encouraged un-healthy competition and ultimately increasedthe cost to the taxpayer of bailing out the mosttroubled companies.

In China, regulators have permitted relativelyweak reserving levels, allowing companies tocover shortfalls from unprofitable in-force business with future margins from profitablenew business. The Singapore regulators havegone the route of developing a risk-based capi-tal requirement. This requirement will encour-age companies to look more at reducing the riskinherent in product structures.

Taiwan was the second stop on the tour. Therethe need for good risk management practices isbecoming evident with the emergence of signif-icant negative interest spread in the in-forcebusiness. ALM practices are being developedin many companies with the usual concern ofhow to match when the available assets are tooshort and/or too low earning. Regulators haverecently changed the regulations on par busi-ness, in hopes that more companies will sellthose products with significant margins that cancushion against future risks, but true par busi-ness has yet to become popular in Taiwan, and itremains to be seen whether it will be widely ac-cepted as an alternative to non-participatingand unit-linked/variable products. We leftTaiwan as a typhoon was approaching. It fol-

“

Risk Management Seminar Visits Six Asian Citiesby David Ingram

Editor’s note: Reprinted with permission from Asia Insurance Review

◗


lowed us for most of the rest of the tour, but wekept one city ahead at all times.

Neil Parmenter, or Pong Ming Dur as he liked tointroduce himself, the current president of theSociety of Actuaries kicked off the seminar ineach city with a talk about the importance ofAsia to the SOA. Asian actuaries account for alarge portion of the growth of the SOA over thepast 10 years and the membership of the SOA inAsia continues to have strong growth. Neil alsotold of the importance of risk management to thefuture of the actuarial profession and the workthat the SOA and the Casualty Actuarial Societyare doing to help to position actuaries to be thefirst called for chief risk officer positions.

In Hong Kong, we saw the nightly laser lightshow and spoke to a very diverse crowd of aspir-ing and current risk managers. People from allover the world are working in the insurance in-dustry in Hong Kong. Hong Kong has a largecontingent of international companies, many ofwhom have brought risk management practicesfrom their parent companies to Hong Kong.Risk management practice and regulation inHong Kong are among the strongest in Asia butthere is room for improvement and seminar at-tendees were looking for help to identify theirnext risk management development step.

“Market consistent embedded value is not a re-placement for embedded value, but an en-hancement” said Mark Saunders, managingprincipal and head of Tillinghast in Asia. AsMark explained, MCEV helps you obtain a ro-bust measure of risk, bringing the concepts ofmodern finance theory into the valuation andrisk assessment of insurance companies. Thoseconcepts include: (1) risks that can be diversi-fied away do not command a risk premium; (2)systemic risks can be replicated with a portfolioof traded assets; (3) two portfolios with the samecash flows have the same value; and (4) thevalue of a company is more (or less) than the sumof its discrete parts. With MCEV an explicit al-lowance is made for the cost of policy guaranteesand policyholder options in assessing the valueof the insurance company and/or line of busi-ness. In the MCEV calculation, the appropriate

risk discount rate should bebased on the real risks. Eventhough MCEV is not yetwidely accepted, Mark rec-ommended that it should beused to give additional in-sights into the business sothat alternative perspectivescan be understood and thatsensitivity testing is essen-tial. Mark concluded thatMCEV helps you be better informed. The betterinformed you are, the better placed you are tosucceed.

Following Mark’s session we had the liveliestround of questions and answers yet. The HongKong attendees were clearly interested in these enhanced techniques and were up-to-speed on the issues as EV determination is commonpractice for Hong Kong insurers.

We landed in Shanghai and the entire group ofspeakers and our invaluable coordinator, PatKum of the Actuaries Office in Hong Kongbrought us directly to Zhou Zhuang, a tradition-al Chinese water village with canals runningthroughout the town and picturesque foot-bridges. A packed hall heard our seminar thenext day and a presentation the following dayfrom a locally based actuary, TerrenceCummings of AIA, on the topic of annuitylongevity concerns. Many students and recentgraduates were in the audience who are all hop-ing to put their recent risk management learn-ings to good use. Insurance industry focus inShanghai has been on growth, not risk manage-ment however. The regulations and underde-veloped capital markets currently restrictinvestment options significantly. This willchange in time and more sophisticated riskmanagement should become possible in future.

“Many insurance companies are struggling toclose the gap between the design and planningstages and the actual execution and integrationof their ERM programs,” said Robert Fok, PWCDirector of Actuarial Services in Hong Kong.Robert spoke about a recent survey of globalrisk management practices. The survey report-


ed the responses of 44 companies in Asia,Europe, North America, Australia and Africa.Most companies have several key componentsof ERM in place such as enterprise-wide riskidentification, risk assessment, risk responseand controls and monitoring. However the sur-vey found that ERM is not yet aligned withstrategic planning. ERM is not fully integratedacross companies and there is often an absenceof clear standards for risk taking. In total, only 8percent of the companies polled felt that theirERM system was fully in place and operating ef-fectively.

City number five was Seoul. By this time wewere waking up not knowing where we were anddreaming that we were being asked to do eachother’s presentations. Risk management is a hottopic in Korea. Events following the “Asian cur-rency” crisis of 1998 seem to have driven homethe importance of ALM to Korean insurancecompanies. Most would like to develop sophis-ticated ALM programs but are stymied by thelack of long-term assets in the Korean financialmarkets. The Korean regulator has been work-ing on a requirement that companies adopt afull-scale risk management program, phased inover several years. Companies are beginning toimplement ERM and find themselves withquestions about how it is being done in otherareas of the world.

“Many life insurance products have the charac-teristics of highly complex financial deriva-tives,” Paul Headey stated. Paul is the head ofthe Milliman Hong Kong office. His presenta-tion discussed ways of hedging insurance prod-uct options and the impact such strategies couldhave on product development. Since there arefew if any traded financial instruments that canbe used to replicate insurance product options,the delta hedging process is often used. Withdelta hedging, the sensitivity of the insurancecontract to changes in the marketplace ismatched by securities with the same sensitivity.Frequent rebalancing is usually necessary to

keep a delta-hedging program effective. Insome cases insurance companies can purchasea static hedge contract customized by a bank fortheir risks. Rolling hedges are used for curren-cy exposures that last beyond the horizon of anyFX contracts. Insurance risks usually have fat-ter tails or extreme situation loss characteristicsthan most market traded instruments. Riskmanagers were urged by Paul to be careful whenmodeling risks, and that they should start sim-ple and refine their models as they learn.

Tokyo provided a very warm—well, actually a very hot—welcome to our final stop on ourseminar. Japanese companies have been practicing many of the aspects of risk manage-ment for several years, but are still developingtheir capabilities to fully quantify their risks.The audience presented many challengingquestions to the speakers as participants wereclearly hoping to go away with knowledge that they could immediately use and they wanted to make sure that they fully understoodthe presentations.

“The savvy corporate leader uses risk manage-ment as both a sword and a shield,” was a quotefrom John Hunkin that Dave Ingram used toopen his presentation. Dave is a Milliman con-sultant in New York City. As head of the SOARisk Management Section, Dave has seen therisk management operations of many NorthAmerican companies. Key to the success of anERM program is the development of a risk man-agement culture. He presented a set of 12 riskmanagement best practices as a sample of oneway that a risk management culture can be de-fined in a company.

1. Board and senior management are responsible for risk management.

2. Senior management understands all firmactivities and understands the basis ofthe risk management system.

3. Authority and responsibility are clearlydefined and risk measurement and man-agement are independent from risk tak-ing functions.

4. All material risks are identified andmeasured; exposures are aggregated and

◗


RM Seminar Visits Asian Cities◗ continued from page 19

“

”

The savvy corporate

leader uses risk man-

agement as both a

sword and a shield ...

◗


management attends to the largest expo-sures.

5. There are risk limits for all material risksand a system for enforcing the limits thatis part of an internal control system.

6. The firm has staff with sufficient expert-ise to perform the risk management func-tions and adequate systems support.

7. Risk surplus is allocated to businessunits and products and used for capitalbudgeting purposes.

8. Stress testing is a part of the risk manage-ment process.

9. New products and ventures trigger con-sideration of potential risks and new riskmanagement procedures.

10. Financial reporting allows managementto view the risk-adjusted returns of busi-ness units, products and activities.

11. Product pricing and rate setting reflectsthe risk adjusted return.

12. The firm has a process for quickly resolv-ing identified risk management weak-nesses.

Dave urged each audience member to developtheir own list of risk management best practices,taking into account their company culture, capabilities and goals.

Suddenly, the sixth seminar was over and weswore never to give those presentations again.No sooner was I back to New York than I wasasked to give my best practices presentation inthe fall in Nebraska. Well, it was a hit all overAsia, why not Nebraska? These seminars reallydid permanently enlarge my world. Hopefullywe did the same for our audiences. ✦

Risk Management by Michel Crouhy, Dan Galai, Robert Markby Fred Tavan

This book is a ‘must-read’ for any serious student of risk management. It provides a comprehensive introduction to the subject. Presented within the framework of a financialinstitution, it covers the design and operation of a risk management system, the technicalmodeling within the system, and the interaction between internal oversight and externalregulatory requirements. The mathematical models and methodology of risk managementare presented rigorously, and they are integrated with the empirical evidence on their application.

This book covers the entire field of risk management from policies to methodologies, as well as data and technological infrastructure.

The reader can learn about using different VaR approaches to measure and manage marketand credit risk. Approaches other than VaR are also detailed. Pros and cons of each ap-proach are summarized for quick reference. RAROC concepts are described and providea good starting point for a student’s education in this area. Operational risk management iscovered in a practical way and a framework is described for operational risk assessment.

Book Review

Fred Tavan, FSA, FCIA, is

AVP, reinsurance pricing

at Canada Life Reinsurance

in Toronto, Ontario. He

can be reached at

fred_tavan@

canadalife.com

◗


Guaranteed Annuity Options or a Fine Messby Mary R. Hardy

1. INTRODUCTION

T he actuarial profession in the UK isunder unprecedented external scruti-ny currently. The serious financial

difficulties faced by Equitable Life (UK), theoldest mutual insurer in England, led to the government commissioning an investigationby a senior law lord. The result is the recentlypublished Penrose Report. As a consequence ofcriticisms of the profession in the PenroseReport, the government then asked a senioreconomist, Sir Derek Morris, recently retiredchairman of the competition commission to re-view the way the UK profession sets standardsand monitors performance. Although the word‘crisis’ is not being publicly bandied about,there is a lot of discomfort around Staple Inn, theheadquarters of the Institute of Actuaries.

The solvency problems, which broughtEquitable Life (UK) to close its doors to newbusiness, and which nearly broke several othercompanies, arose from an obscure rider to some insured defined contribution pension contracts issued in the 1970s and ‘80s. Therider was an annuitization guarantee, called theguaranteed annuity option (GAO), and the riskmanagement challenges that this option createdare the topic of this article.

The most significant contributions to the dis-cussion of risk management of these options areWilkie et al (2003), Ballotta and Haberman(2003) and Boyle and Hardy (2003), where moredetails about the results in the next couple ofsections can be found.

2. THE GAOThe GAO was attached to with-profit and unit-linked, single-premium and annual-premium contracts. Although most of the con-tracts affected were with-profit, we will look at asingle premium unit-linked version here, as it is

more transparent and therefore easier to describe and model than the with-profit version.A unit-linked contract is very similar to a vari-able annuity contract in the United States, or asegregated fund contract in Canada; premiums(after deduction for expenses) are invested in a fund similar to a mutual fund, with certain guarantees on death and possibly maturity.

Suppose the policyholder’s fund at maturity isdenoted F(n). The GAO rider guaranteed an annuity rate g such that that the pension after annuitization would be no less than F(n)/g.Typically, for a 65-year-old male, g = 9. Now, without this guarantee, the pension woulddepend on the annuity value a65 at maturity, which would obviously vary with interest rates,as well as being updated from time-to-time toallow for improvements in mortality. For a cost-neutral annuitization, the amount of pension would be F(n) / a65.

So, if a65 (t) is the market value of the unit annuity at time t, then the payoff of the option atmaturity at time n, say, is

max (F(n) – F(n) , 0) a65 (n)g a65 (n)

Now, F(n) is the accumulated fund; if the original premium is P, and letting St denote themarket value of the investment fund at t, then

F(n) = P SnS0

So that the payoff formula can be rearranged to:

P Sn max ( a65 (n) – g , 0)S0 g

This is a quanto interest rate option. A quantooption is one that is measured in units different

Mary Hardy, FSA, PhD, is

an associate professor at

the University of Waterloo

in Ontario, Canada. She

can be reached at

[email protected].

¨ (12)

(12)

(12)

(12)

(12)

(12)

◗


from standard cash units; in this case the payoff is in units of the final fund value. The option itself depends on mortality and interest.We will focus on the equity and interest rate risk, though the cost of mortality improvementhas also proved a significant nondiversifiablerisk factor.

We can see the experience of the option cost over the last 25 years in Figure 1. This gives thecost of the option per $100 maturity proceeds at retirement for a male age 65 using an up todate mortality table (PMA92(C20)). In the mid-‘90s, actuaries began to be aware of the po-tential liability, and in the late ‘90s, the true costof falling interest rates became evident. The fig-ure does not show the cost of the spectacular equity returns in the 1990s.

3. VALUING THE OPTION

3.1 Using Jamshidian’s formulafor options on coupon bondsGiven that several companies have substantialGAO liability risk, there has been some discus-sion of how to manage the risk now that it is bet-ter understood. Many companies have usedreinsurance through banks. The modern actuar-ial approach to risk management might be toproject the liabilities under P-measure, and usea discounted tail measure as a capital require-ment. This approach is explored in Wilkie,Waters and Yang (2003). Pelsser (2003) dis-cusses the use of swaptions, though this onlymanages the interest rate risk, not the equity ormortality parts of the liability.

Figure 1: Emerging GAO Cost Per $100 Maturity Proceeds


◗


From an option pricing viewpoint, the GAO iseasier to price than to hedge. We will demonstrate an approach to pricing here.

Assume annual pension payments in arrear, andletting D (t, T) be the price at t of a pure dis-count bond maturing at T, then we have

a65 (n) = Σ jp65 D(n, n + j)

Using D(t, n) as numeraire means that for anyoption payoff at n, say, the value at t < n is

V (t) = D(t, n)EQ [V (n)|Ft ]

Q here represents the forward measure.

The payoff of the GAO at maturity, assuming survival, is

V (n) = P Sn max ( a65 (n) – g , 0)S0 g

Assume P = S0, for simplicity. The guaranteeapplies only to lives who survive to annuitize.The value at t of the payoff for a life age x < 65,x+(n – t) = 65, allowing for survival is then

G(t) =65-xpx D(t, n) EQ Sn (a65(n) – g)+

g

If we assume further that St is independent ofD(t, T)—that is, that interest rates and stocksare independent, then we can simplify further.Recall that under the Q-measure, the discounted value at t < T of ST must be St, so:

G(t) = 65-xpx D(t, n) EQ [Sn] EQ (a65(n) – g)+

g

= 65-xpx St EQ [ (a65(n) – g)+ |Ft]g

So, we have effectively eliminated the quantoproblem, and we are left with an (undiscounted)interest rate option. In Boyle and Hardy(2003) the annuity is treated as a coupon bond,and we use Jamshidian’s formula for valuing options on coupon bonds in terms of options on pure discount bonds (Jamshidian 1989). In order to apply this, we use the Hull-White (or

Guaranteed Annuity Options◗ continued from page 23

Figure 2: GAO Option Value, 10-years to Maturity, % of Fund

ω−65

j=1

(12)

[ |Ft]

[ |Ft]

extended Vasicek) single-factor interest ratemodel. The interest rate model is fitted to theterm structure at the valuation date. In Figure 2we show the resulting option prices for a contract valued at the dates given on the x-axis,assuming a life age 55, that is, assuming 10years to maturity. At the more recent dates thevalues are very similar to Figure 1 as theyshould be. The option is deep in the money, andbecause the graph is shown in units of the fundSt, the cost is unaffected by discounting1.Notice though, that the option price gives someslightly earlier warning that the option mightcost money.

Although we have an option price, we don’t real-ly have a hedge. It is well known in the area of interest rate options that single-factor modelsdon’t give very good hedges. While they mightadequately model the Q-measure distributionof losses at maturity, they do not adequatelymodel the Q-measure process over the term ofthe contract. An accurate representation of theprocess is required for the dynamic hedge. Forthe dynamics of the interest rate process to besufficiently accurate a model with at least twostochastic factors is required, but modellingsuch a complex option with a two-factor modelwould be very difficult.

However, we can indicate roughly what thehedge looks like by using a much simpler modelfor interest rates.

3.2 Using a lognormal assumption for a65(t)There is substantial autocorrelation in the val-ues for a65(t). Nevertheless, in order to give anindication of what the hedge might look like, weassume that a65(t) follows a lognormal process;we also continue to assume that the annuity isindependent of equity performance. Note that Iam not advocating this approach! I am just usingit to illustrate what this hedge might look like.With different assumptions the hedge wouldlook broadly similar.

With these assumptions we can express the option value as

G(t) = 65-xpx D(t, n) EQ [Sn] EQ (a65(n) – g)+

g

= St n - tpx EQ D(t, n) (a65(t) – g)+

g D(t, n)

and now the expectation term is a simple optionon the risky asset a65(t). The resulting optioncost is

G(t) = 65-xpx St {a65(t) Φ (d1) – Φ(d2)}g

where

d1 = log (a65(t)) – log g + σ a (n – t)/2 ,

σa n – t

d2 = d1 – σa n – t

and σa is the volatility of a65(t).

We can hedge this in three parts: an annuity partinvested in a65(t), Ht , say, a bond part in a purediscount bond maturing when the policyholderreaches age 65, Ht , and an equity part invested in the same assets as the premium, Ht.Each part is determined, as usual, by differ-entiating the bond price with respect to the different assets.

The result is

Ht = 65-xpx St a65(t) Φ(d1)g

Ht = –Ht

Ht = 65-xpx St {a65(t) Φ(d1) – Φ(d2)}g

◗



[ |Ft][ |Ft]

2

b

a

s

a

b

S

1We know that the undiscounted value of a deep in-the-money option is more or less the current moneyness.

a

◗


Which just says that we take the entire optionvalue and put it in the equity fund, and in addition short sell some bonds maturing at theretirement age, and use the proceeds to buy theannuity asset. Even when the option value isrelatively small, the positions taken in the bondand annuity might be substantial. For example,assume σa = .025, g = 9, and consider a life age 45 with 20 years to retirement. If the long-term rate of interest is around 10 percent(as it was in the early ‘80s), then the option priceis low, at around 0.2 percent of the initial premi-um. However, the annuity hedge amounts toaround 5 percent of the premium, much moresubstantial, and the sensitivity to changes in theannuity value is more apparent.

4. SOME THOUGHTSEven though with some simplifying assump-tions, the ongoing challenge of hedging is verydifficult. Perhaps the first lesson from the GAOstory is that insurers need to be very carefulabout all financial guarantees, and that (almost)no guarantee is really cost-free. Some insurerswere so casual about these guarantees that theydid not even record which policies carried theoption and which did not. They may have be-lieved that they could mitigate the cost of theguarantees by adjusting the with-profit bonus(dividend) when the policyholder exercised theoption—giving with the right hand and takingaway with the left. The high court found that approach was not an acceptable interpretation

of the concept of a guarantee.

The second lesson might be toemphasize the importance of fi-nancial mathematics—themathematics of financial guaran-tees—in actuarial education.Under the current plans for the2005 SOA education redesign,

only risk management and investment special-ists will learn financial mathematics. But theoptimal risk management for GAOs would havebeen not to offer them in the first place (howeverfascinating they might be to financial engi-neers). A deep understanding of the nature of financial guarantees is critical at all stages—product development, marketing, valuation andrisk management. Every life insurance actuaryneeds to be comfortable with the characteristicsof financial guarantees and how these are man-aged. Therefore, every life insurance actuaryneeds to have a good grasp of modern financialmathematics. We must ensure that actuarial education provides what is necessary.

ReferencesBallotta, L. and Haberman S. (2003) Valuationof guaranteed annuity conversion options.Insurance: Mathematics and Economics 33 (1)87-108.

Boyle, P.P. and Hardy, M.R. (2003) Guaranteedannuity options. ASTIN Bulletin. 33(2) 125-152.

Jamshidian, F. (1989): “ An Exact Bond OptionFormula,” Journal of Finance, 44 pp. 205-209.

Pelsser, A. (2003) Pricing and HedgingGuaranteed Annuity Options via Static OptionReplication. Insurance: Mathematics andEconomics 33 (2) pp. 293-296.

Penrose (2004) Report Of The Equitable LifeInquiry www.hm-treasury.gov.uk/independentreviews/penrose report/indrev pen index.cfm

Wilkie, A.D., Waters, H.R. and Yang, S. (2004)Reserving, Pricing and Hedging for Policieswith Guaranteed Annuity Options. BritishActuarial Journal 10 (forthcoming). Availablefrom www.actuaries.org.uk/files/pdf/sessional/fac sm030120.pdf ✦

Guaranteed Annuity Options◗ continued from page 25

“

”

Perhaps the first les-

son from the GAO

story is that insurers

need to be very care-

ful about all financial

guarantees, and that

(almost) no guaran-

tee is really cost-free.

Building on the tremendous success ofthe last two years, the Society ofActuaries, Casualty Actuarial Society,

Georgia State University’s risk management de-partment and Professional Risk Managers’International Association are partnering againto announce the 2005 Enterprise RiskManagement Symposium. This world-class pro-fessional education event will focus on riskmanagement issues applicable to the entirespectrum of the risk profession, making thisevent appeal to any professional practicing orseeking to practice in this emerging discipline.

In spite of its tender age, this symposium imme-diately received great attention from the riskmanagement community after its launch in2003. It has consistently brought together someof the best and the brightest minds in ERM, who,over the course of event, have been able to suc-cessfully network and deliberate on a variety ofcritical issues in enterprise-wide risk manage-ment.

The following are expected general themes forthe 2005 ERM sessions. The intent is to addressvarious areas of practice and various industries—from financial services to energy and corporate, and beyond, allowing for cross-pollination of the best risk management prac-tices across various economic sectors. Bothpractical and conceptual presentations aregoing to be encouraged.

l Correlation and integration of risks across an organization

l Creation of value through ERMl ERM risk reporting formatsl ERM—theoretical foundationl Translating risk monitoring and measure-

ment into decision-makingl ERM frameworksl Risk capital and managementl Operational risk measurement

Please mark your calendars!

The 2005 ERM Symposium is scheduled forMay 2-3 and will be held at the Sheraton indowntown Chicago.

There will also be a separately bookable, limit-ed-attendance ERM Essentials Workshop onMay 1, targeted at senior management. Seniormanagers interested in establishing effectiveERM frameworks within their companies willbenefit from attending the workshop, taking astep-by-step look into the art and science behind ERM and utilizing the opportunity forpersonalized interaction with the ERM expertpanelists.

A separate stand-alone dedicated Web site isbeing created for the symposium. It is expectedto be launched in October-November. In the meantime, you can view the 2004 ERM Symposium program at http://www.casact.org/coneduc/erm/2004/ . ✦

2005 Enterprise Risk Management Symposium

◗


◗


2004 Bowles Symposium: Extreme Value Seminarby Steve Craighead

Our seminar at the Bowles Symposiumwas so packed with intense mathemati-cal formulae and discussions of compli-

cated material that you might feel as if you justtook a 10-week graduate course in three hours!

However, in those three hours we covered thefundamentals of the use of extreme value theoryin risk management. Here we discussed do-mains of attraction (this describes whether a sta-tistical distribution has a light, medium orheavy tail). We examined various distributionsthat one would fit to model these domains of at-traction such as Fréchet, Weibull, Gumbel andgeneralized extreme value distributions. We ex-amined ideas of conditional tail expectation(CTE) (just think of this as an average of claims over a threshold). This discussion alsoincluded methods to estimate the required num-ber of scenarios for different levels of CTEs. Wecovered many other topics as well, that are related to understanding and modeling risks.

Dr. H. N. Nagaraja, a world expert in extremevalue statistics who is from Ohio StateUniversity, was the primary speaker. His pres-entation was excellent (notwithstanding thecomplexity). His paper is one of my favorite ref-erences as I create new applications using ex-treme value statistical techniques at mycompany.

I made two contributions. First, I punctuatedDr. Nagaraja’s presentation with brief exposésof insurance applications of the current topicunder discussion. Second, I demonstrated aspreadsheet that I have used over the past 12years to implement some of the extreme valuestatistical techniques that Dr. Nagaraja pre-sented.

This sheet is fairly simple. First the user inputsthe data he or she wishes to examine (starting incell C22 of the sheet). When the user processesthe sheet, it provides two results. The first is thatit gives the domain of attraction (remember thisis how much area is in one tail of a distribution)as reported in cell C171. The second result isthat it allows the user to input a list of various ex-treme percentiles (such as 0.01 percent) start-ing in cell H22 and it will extrapolate the user’sdata to estimate these percentiles. This extrap-olation is done either by a kernel method2

or resampling3. A kernel method is where onefits a specific statistical distribution (the kernel) locally over a small subset of the data.This process creates something very similar to amoving weighted average of the data. The size of the local subset used is determined by a distance called a bandwidth. For the actuariesthat have used graduation, this is very similar tothe ideas used in the Whittaker-Hendersongraduation method. Resampling is where oneuses their data over and over (resampling) to determine specific statistical values, in thiscase the extreme percentiles.

The user chooses which kernel by entering a “1”in either cells C4, C5 or C6, which are normal,lognormal or Pareto. Note: Lognormal is mostfrequently the best kernel.

If the user wants to use resampling, enter a “1”in the resampling cell C7.

Note: The options of fitting the Fréchet, Weibulland Gumbel distributions are not currently activein the spreadsheet.

Next the user should specify how many elements are in the data in cell C12. The sheetdoes not set an express limit on the number of

Steve Craighead, ASA,

MAAA, is an assistant

actuary at Nationwide

Financial in Columbus,

Ohio. He can be

reached at craighs@

nationwide.com.

1 The algorithm is described in the article “The Selection of the Domain of Attraction of an Extreme Value Distribution from a Set of Data,” by

Castillo, E., Galambos, J., and Sarabia, J. M. This is a chapter in Lecture Notes in Statistics Vol. 51: Extreme Value Theory edited by J. Husler and

R. Reiss and published by Springer-Verlag. 2 Dan Heyer, FCAS, introduced me to the use of kernel methods in extreme value estimations in reinsurance.3 This method was introduced by D. Zetterman, in the article “A Semi-parametric Bootstrap Technique for Simulating Extreme Order Statistics.”

This is an article in the June 1993 Journal of the American Statistical Association (Vol. 88, No. 422, pp. 477-485).

◗


elements that you caninput, however, youmany need to revise thediagnostic graph to reflect more than 999 elements.

In cell C13, the user in-puts where they believethat the tail of the distri-bution begins. I havestolen a reinsuranceterm here and call thisthe attachment point.This is entered as a per-cent. Frequently this isa lower percent, which iscontained within thedata provided. Most fre-quently reinsurancecompanies use 1 percentor 2 percent. If you setthis value too high, yourextrapolations may betoo conservative.

In cell C14, enter the number of extreme per-centiles that you wish to extrapolate. Recallthese were input starting in cell H22.

If you are using resampling, cell C15 is whereyou specify how many scenarios that you wishprocessed.

When ready, you execute by pressing the esti-mate extremes button. Three things then occur.

The first result is that the estimated tail type is updated. If the value is greater than 2, the tail is light. If the value is between 0.5 and 2, the tail has medium weight. If it’s below 0.5, the tail is heavy.

The second result is that the extrapolated esti-mates are placed in cell K22 and below.

The third result is that the diagnostic graph isupdated. This is a graph of the 10 percent lefttail (in black dots) with the extreme percentiles(purple dots with a blue line) superimposed.The x-axis is the log of the percent.

By playing with your choice of the kernel or resampling, you can examine your own data.

I have used extreme value theory in determiningthe riskiness of a product. This is indicated ifthe Estimated Tail Type is less than 0.5.

Other areas where the extrapolation was criticalwere insolvency analysis and capital needs.Most of our computer financial models are high-ly accurate, but extremely slow to process. HereI have used extrapolation to obtain extreme per-centiles that otherwise may take days or monthsto obtain from an existing model. The modelcould also be used to estimate extreme claims, ifone would subtract the claim amounts from avery large number. This converts a right tailproblem into a left tail problem. By then map-ping the threshold by the same method and esti-mating many extreme percentiles, one can thenaverage these and determine a mean claims overa threshold.

Both Dr. Nagaraja’s excellent reference paperand my spreadsheet are available on the CAS2004 Bowles Symposium Web page (look forTrack E) at http://www.casact.org/coneduc/erm/2004/handouts/ . My spreadsheet is alsoavailable on the Risk Management Task ForceExtreme Value Subcommittee Web page:http://rmtf.soa.org/rmtf_evm.html. Any futureupdates of my worksheet will be placed on thelatter Web site. ✦

◗


Risk Management Sessions at SOA Annual Meeting

The Risk Management Section sponsored thefollowing sessions at the 2004 Annual Meetingin New York:

RM 14 PD—Integrating andAggregating RisksModerator: Frank Sabatini (Ernst & Young)Panel: Frank Sabatini and Ugur Koyluoglu(Mercer Oliver Wyman)Summary: Implementing risk integration andaggregation provides a powerful view of enter-prise risk and the benefits of diversification onthe total company risk exposure. The panelistsdiscussed approaches for measuring risk acrossrisk elements on a consistent basis and dis-cussed the methods for aggregating results andmeasuring diversification.

RM 27 PD—Quantitative MethodsUsed in Managing Credit RiskModerator: Tony Dardis (Tillinghast)Panel: Peter Davis (Ernst & Young), UgurKoyluoglu (Mercer Oliver Wyman) and RishiKapur (Swiss Re Financial Services)Summary: Panel members shared their experi-ences in credit risk management and describedsome of the main quantitative methods used.Topics included procedures for evaluating anindividual credit risk and techniques for portfo-lio credit risk management.

RM TS 60—Enterprise RiskManagement (ERM) Tools andAnalyticsModerator: Hubert Mueller (Tillinghast)Instructors: Samir Shah (Tillinghast) and Fred Tavan (Canada Life)Summary: A teaching session was held to discuss ERM tools and analytics. Instructorsdiscussed industry approaches and best

practices for measuring, monitoring and managing financial and operational risks. ERM tools and analytics discussed includedquantitative risk metrics, heat maps, risk scorecards and economic surplus charges.

RM 91 OF—Chief Risk Officer(CRO) ForumModerator: Helene Pouliot (Tillinghast)Panel: Don Mango (ERC) and Grant Hardy, executive vice president and CRO for RBCInsurance.Summary: One of the elements of an effectiverisk management process is building an effec-tive strategy for managing risk and creating arisk management culture. A panel of CROs dis-cussed the challenges of gaining and maintain-ing management support, creating a riskmanagement culture and building an effectiverisk management process.

RM 106 SM—Risk ManagementSection Hot BreakfastModerator: David Ingram (Milliman)Summary: The meeting focused on the actuary’srole in leading risk management activities, andsection members were encouraged to expresstheir views on section priorities and activities.

RM 121 PD—Catastrophic Risk ina Post 9/11 EnvironmentModerator: Joseph KolodneySummary: Panelists offered insight into specif-ic catastrophic risks, methods used to analyzeand quantify the risks, available reinsurancesolutions to manage the risk and pricing as-sumptions that have led to significant cost in-creases in post-9/11 reinsurance. ✦

◗


Risk ManagementIssue Number 3 • November 2004

Published by the Society of Actuaries475 N. Martingale Road, Suite 600Schaumburg, IL 60173-2226phone: (847) 706-3500 fax: (847) 706-3599www.soa.org

This newsletter is free to section members. A subscription is $15.00 for nonmembers.Current-year issues are available from theCommunications Department. Back issues of section newsletters have been placed in theSOA library and on the SOA Web site:(www.soa.org). Photocopies of back issues may be requested for a nominal fee.

2004-2005 SECTION LEADERSHIP

EditorKen Seng Tan, ASAUniversity of WaterlooWaterloo, OntarioCanada N2L3G1phone: (519) 888-4567 xt. 6688fax: (519) 746-1875e-mail: [email protected]

Council MembersDouglas W. Brooks, FSACharles L. Gilbert, FSADavid Ingram, FSA John Kollar, FCASBeverly Margolian, FSAS. Michael McLaughlin, FSA Hubert B. Mueller, FSA Frank P. Sabatini, FSAKen Seng Tan, ASAJohn Stark, FSAFred Tavan, FSAShaun Wang, ASA

Society Staff ContactsClay Baznik, Publications [email protected]

Lois Chinnock, Sections Manager [email protected]

Staff ActuaryValentina Isakina, ASA

Newsletter DesignMary Pienkowski, Graphic Designer

Facts and opinions contained herein are thesole responsibility of the persons expressing them and should not be attributed to the Society of Actuaries, its com-mittees, the Risk Management Section or theemployers of the authors. We will promptly cor-rect errors brought to our attention.

Copyright © 2004 Society of Actuaries.All rights reserved.Printed in the United States of America.

Articles Needed for Risk Management

Your help and participation is needed and welcomed. All articles will include a byline to give you full credit for your effort. If you would like to submit an article, please contact Ken Seng Tan, editor, [email protected].

The next issue of Risk Management will be published:

Publication Date Submission DeadlineMarch 2005 January 3, 2005July 2005 May 2, 2005

Preferred FormatIn order to efficiently handle articles, please use the following format when submitting articles:

Please e-mail your articles as attachments in either MS Word (.doc) or Simple Text (.txt) files. Weare able to convert most PC-compatible software packages. Headlines are typed upper andlower case. Please use a 10-point Times New Roman font for the body text. Carriage returns areput in only at the end of paragraphs. The right-hand margin is not justified.

If you must submit articles in another manner, please call Joe Adduci, (847) 706-3548, at the Society of Actuaries for help.

Please send an electronic copy of the article to:

Dr. Ken Seng Tan, ASAUniversity of WaterlooWaterloo, Ontario Canada N2L 3G1phone: (519) 888-4567 ext. 6688fax: (519) 746-1875e-mail: [email protected]

Thank you for your help.

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