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1、Research Working Party on the Public - Access DFA ModelPhase I Work Product:Documentation and Evaluation of Componentsof Existing ModelMorgan H. Bugbee, Co-ChairmanPatrick J. Crowe, Co-ChairmanJoel E. Atkins, MemberRobert J. Azari, MemberThomas P. Conway, MemberWilliam D. Hansen, MemberAbstractThe R
2、esearch Working Party on the Public Access DFA Model has published the attached report to document and evaluate the components of an existing DFA model which has been available over the internet. Under the supervision of the CAS-Dynamic Risk Modeling Committee (DRMC), the working group was formed to
3、 evaluate, update and correct the DFA model which was out of date and required thorough documentation. The underlying goal of the working group is to create an accepted, documented and widely available DFA model template. The revised DFA model and its documentation are the basis for future call pape
4、rs and research projects involving DFA modeling.The attached paper is the documentation of the revised DFA model. The first section provides an overview of model functionality. In the subsequent sections, additional documentation is provided for each MS Excel model worksheet which describes the func
5、tionality, assumptions and errata. After reading the paper a user should have an understanding of the inner workings of the model, how to input starting assumptions, trigger model simulation runs and generate output. The documentation should enable the user to research and develop enhancements to th
6、e model.Table of ContentsSECTIONPAGEINTRODUCTION2ORGANIZATION OF DOCUMENTATION9UNDERWRITING INPUT SHEETS: XYZ COMPANY HMP-I; XYZ COMPANY WC-I10UNDERWRITING OUTPUT SHEETS: XYZ COMPANY HMP-O; XYZ COMPANY WC-O15UNDERWRITING OUTPUT SHEET: LINE SUMMARY18REINSURANCE INPUT19CAT LOSS GENERATOR21INVESTMENT I
7、NPUT WORKSHEET24BOND 1 THRU BOND 5 WORKSHEETS27BOND SUMMARY WORKSHEET30STOCKS WORKSHEET31GENERAL INPUT WORKSHEET33TAX CALCULATOR WORKSHEET35SIMULATION DATA WORKSHEET38RANDOM NUMBERS WORKSHEET40INVESTMENT DISTRIBUTION WORKSHEET41OUTPUT SHEET43STATUTORY SUMMARY47GAAP SUMMARY49APPENDIX A 51Introduction
8、The Dynamic Risk Modeling committee has established goals for Dynamic Financial modeling, which are outlined in the four phases below. The CAS Working Party on the Public Access DFA Model has been formed to document and evaluate the public access dynamic financial modeling tool that is available for
9、 download from the internet. This modeling tool was built in the mid nineties by the actuarial firm of Miller, Rapp, Herbers and Terry along with a team from the University of Illinois. The working group has agreed to update and enhance the model through several phases of work described as follows:P
10、hase 1: Documentation and evaluation of the components of the existing modelThe current components being evaluated and documented include:1. Interest rate and inflation generator2. Investment module3. Financial statement development4. Loss development and payment patterns5. New business6. Jurisdicti
11、onal risk7. Catastrophe module8. Underwriting cycle9. Taxation10. OutputPhase 2: Identification of selected enhancements to the modelPhase 3: Implementation of selected enhancementsPhase 4: Ultimately, consider an “open source” framework for the public-access modelThe following document is the first
12、 step in addressing the working groups first phase. This paper describes the cumulative work done to review and document the current public access DFA model.General Description of Model FunctionalityThe Public Access DFA model is a spreadsheet based stochastic simulation model which simulates and pr
13、operty casualty insurance companies financial conditions over a five year time horizon. The model generates financial statements including balance sheets, income statements and IRIS ratios. The model can also capture and display expected values and distributions of any variable included in the model
14、.The model relies on input assumptions for a series of financial and underwriting variables listed below:Investment Assumptions1. Short-term interest rate2. Term structure3. Default potential4. Equity performance5. Inflation6. Mortgage pre-payment patternsUnderwriting Assumptions1. Loss frequency an
15、d severity2. Rates and exposures3. Expenses4. Underwriting cycle5. Loss reserve development6. Jurisdictional risk7. Policyholder aging phenomenon8. Payment patterns9. Catastrophes10. Reinsurance Terms11. TaxesModel outputs can be produced in a variety of forms but standard output includes:1. 5-year
16、projections2. Balance sheets3. Income statements4. Loss ratio reports5. IRIS testsThe flow of information through the public access model is illustrated through the flow chart below:Public Access Model Approach to Modeling Risk The following section is an excerpt from a paper presented at the 1997 C
17、AS DFA Seminar by DArcy, Gorvett, Herbers, Hettinger, Lehmann and MillerThe risks facing insurers can be classified into two major categories: one for items listed on the balance sheet, and the other based on continuing operations (which would appear in the operating statement). Furthermore, each of
18、 these categories can be subdivided into two further categories. Balance sheet risk consists of asset risk and liability risk. Operating risk consists of underwriting risk and investment risk.Asset risk involves the change in value of an existing asset. For a bond, this could result from a change in
19、 interest rates, a change in the debt rating, or default on interest or principal. For an equity, asset risk involves a change in the market price, which could be caused by some of the same factors affecting bond values, or by other changes affecting company profitability or operations. Other assets
20、, such as agents balances, are exposed to default risk.Liability risk is primarily related to the adequacy of the loss reserves. As statutory valuation requires loss reserves to be carried as the nominal value of all future payments, this risk involves the possibility that total payments will ultima
21、tely differ from the indicated estimate. Based on market valuation of loss reserves, however, the risk also includes timing and discount rate components as well as the total payment amount. In addition, liability risk includes the adequacy of the unearned premium reserve to cover losses that will em
22、erge on existing policies.Underwriting risk is the risk associated with business that the insurer will write in the future, either as new business or renewals of existing policies. This risk includes pricing risk - the ability to obtain adequate premium levels on this business - as well as the risk
23、associated with stochastic losses and expenses.Investment risk relates to investment income and capital gains to be earned on existing assets and new assets resulting from continuing operations. This is dependent on interest rates and other economic conditions.The four risk components are complexly
24、interrelated. An increase in interest rates, for example, would lead to a decline in the value of existing assets (especially bonds), but higher investment income on new investments. Adverse development on loss reserves would generate the need for premium increases, and impact future underwriting ex
25、perience. The advantage of a DFA model is that it can allow for this type of interaction. However, a drawback is that these relationships are difficult to quantify. This leads to the need to develop answers to some basic modeling questions before proceeding.Pricing RiskProperty-liability insurers ha
26、ve the opportunity to change the premium level prior to writing new or renewal business. Thus, as expenses or expected losses change, insurers can reflect these changes in the new rate levels. However, two problems can affect the ability of insurers to charge the correct price. First, since most ins
27、urance premiums are set prior to the policy being written, the insurer may incorrectly estimate future experience, causing the price to be either inadequate or excessive. Second, the freedom of insurers to set premium levels varies by state, with some states allowing relatively unrestricted pricing
28、and other states having extensive restrictions. Thus, there are two components to pricing risk. The first component is handled in this model by having the loss ratio (exclusive of catastrophes - see next subsection) be a random variable with the mean value and standard deviation based on company exp
29、erience. Loss ratios are simulated by line, with appropriate consideration given in the simulations to correlations of contemporaneous loss experience between lines. The second component of pricing risk is handled by a factor imposing a restriction on the ability of a company to make rate changes wh
30、ich are indicated by changes in loss frequency or severity. In our model, a factor of 1 would represent complete freedom to adjust rates in accordance with indications, while lower values are used when companies write in states with restrictive jurisdictional forces.Catastrophe RiskIn addition to no
31、rmal pricing risk and the inherently stochastic nature of the loss process, property-liability insurers face the risk of a catastrophic loss. Hurricanes, earthquakes, winter storms, and fires all have the potential to significantly affect the financial condition of an insurer. This risk is separated
32、 out from the normal pricing risk described above. In this model, catastrophes are handled as follows, for each simulated year:1. The number of catastrophes (by our definition, events of any type causing industry-wide losses in excess of $25 million) during the year is determined based on a Poisson
33、distribution, with the parameter based on historical experience.2. Each catastrophe is assigned to a specific geographical area, or focal point, again based on historical tendencies.3. Once assigned to a focal point, the aggregate-industry size of each catastrophe is determined, based on a lognormal
34、 distribution. The size of the event is affected by the location, as both the type of loss and the amount of insured property exposed to a loss is a function of where the catastrophe occurred. The parameters of the lognormal distribution are based on historical industry experience, appropriately adj
35、usted to future cost levels.4. The geographical distribution of the event by state is determined, based on a state-by-state frequency correlation matrix determined from historical patterns.5. The loss is allocated to the company based on market share in the lines exposed to catastrophic risk.Loss Re
36、serving and Adverse Development RiskThis is the major component of liability risk, and one that distinguishes, and complicates, dynamic financial analysis for property-liability insurers. The starting value used for the loss reserve in this model should be the value indicated by an analysis of the c
37、ompanys historical experience, not just the loss reserve stated in the latest financial report. However, even though the loss reserve is based on an actuarial analysis, it cannot be assumed to be exact - there is likely to be some random deficiency or redundancy. In addition to the stochastic nature
38、 of the loss reserve and payout processes, a complication is the correlation between loss reserve development and interest rates, since both are correlated with inflation. However, whereas the relationship between inflation and interest rates is well recognized and has been extensively documented, t
39、he relationship between inflation and loss development is much harder to quantify. Loss reserving techniques traditionally assume that past inflation rates will continue. If inflation increases over historical (or other forecasted) levels, then future loss payments are likely to exceed the amount re
40、served. The relationship between inflation and loss development is one area that needs additional research.As mentioned, loss development is subject to further variability unrelated to inflation. This variability is factored into the model by a normal random variable that allows for either favorable
41、 or adverse development. The volatility parameter is selected based on the companys size and past development patterns, as well as industry considerations (however, any tendency on the part of management - or the industry - to consistently over- or under-reserve is considered separately, i.e., in th
42、e analysis of the appropriate beginning loss reserve level). In years in which the uncertainty regarding court decisions affecting loss payments is higher than usual or when other economic conditions generate greater volatility, this additional uncertainty would be reflected by an increase in the lo
43、ss development parameters. Loss reserve development may also affect rate adequacy. Significant under-reserving, in addition to impacting surplus directly, generates the need for additional rate increases that may, depending on the jurisdictional environment (as discussed below), be difficult to obta
44、in. Also, rate increases can affect the renewal rates on business, causing an additional effect on a companys operations.Jurisdictional RiskIn addition to having the potential to affect the responsiveness of rates to changes in economic conditions, the jurisdictions in which a company operates impos
45、e additional risks on insurers. Residual market subsidies, retroactive premium rebates, and benefit changes on workers compensation policies already written, are all examples of jurisdictional burdens on insurers that increase the financial risk of the company. Thus, an additional, jurisdictional, r
46、isk component, dependent upon the geographical distribution of writings, is added to the model. This risk is assumed to only have the potential for a negative impact on an insurer (an insurer is not likely to be the beneficiary of a retroactive premium surcharge on former policyholders). The number
47、of jurisdictional events is simulated by a Poisson distribution, with the parameter based on the characteristics of the jurisdictional environment in which the insurer operates. The size of each simulated event is determined based on a lognormal distribution.Interest Rate RiskInterest rate volatilit
48、y has led to a major focus on modeling interest rates by many financial institutions, including life insurers. Extremely complex models, using multifactor stochastic variables and time series relationships, have been developed. Despite the complexity of these models, and their relative accuracy in particular situations, no single model is accepted as being correct. Each model has its shortcomings and recognized deficiencies.Interest rates are an important factor for property-liability DFA models, as they
