Stochastic Modeling Workshop Introduction

Transcription

1 Stochastic Modeling Workshop Introduction Southeastern Actuaries Conference Duncan Briggs November 19, 2003

2 What do we mean by stochastic modeling? Modeling of outcomes under a large number of randomly-generated future experience scenarios. The scenario generator should be consistent with statistical distribution of possible values for stochastically-modeled variables. Variables modeled stochastically can be limited to one (e.g., interest rates), or several (e.g., interest rates, equity returns and mortality). 2

3 Deterministic versus Stochastic Deterministic modeling derives outcomes under a finite set of fully-defined scenarios e.g., New York Seven interest rate scenarios scenarios and outcomes don t have associated probability weightings Stochastic modeling derives the statistical distribution of possible outcomes facilitates the quantification of risk/return trade-off Typically uses a large number of scenarios; eliminates the chance that the deterministic approach omits a significant scenario 3

4 A number of factors are leading to an increasing level of interest in stochastic modeling... Management information demands desire to understand distribution of possible results, not just expected results stochastic techniques are an integral part of many risk management programs Equity market falls many variable annuity benefits (GMDB, GMIB, GMWB, etc.) are in-the-money; stochastic modeling can be used to value liability options Low interest rates credited rates have fallen close to guaranteed levels in many cases, leading to spread compression; stochastic modeling can help quantify the impact of further falls in interest rates 4

5 A number of factors are leading to an increasing level of interest in stochastic modeling... Regulatory RBC C3 Phase I in effect, but only applies to a relatively small number of companies; requires use of stochastic interest rate scenarios to determine C3 component of RBC RBC C3 Phase 2 applies to guarantees under variable annuities; probably in effect for year-end 2004 stochastic equity scenarios required to determine cost of guarantees proposed level of capital based on CTE90 discussion of extending this approach to set reserve levels (e.g., CTE 60) 5

6 A number of factors are leading to an increasing level of interest in stochastic modeling... Regulatory AG39 requires stochastic testing to assess adequacy of reserves for variable annuity guaranteed living benefits International Accounting Standards may necessitate the use of stochastic techniques to value options and guarantees Rating agencies have been willing to consider lowering capital requirements based on results of stochastic testing. Technology stochastic modeling is computation-intensive improvements in processing speed are making stochastic modeling more feasible 6

7 Stochastic model results depend on the scenarios There are different kinds of stochastic economic scenario generators and different parameters for each kind. We will describe the kinds and provide considerations for choosing parameters We ll also cover how many scenarios are enough? 7

8 Stochastic modeling is evolving from just interest and equity returns to include other kinds of uncertain elements, such as mortality Mortality assumptions have been traditionally modeled as a deterministic process, represented by a table of mortality rates However, stochastic mortality analysis may be more effective in certain circumstances: The analysis has a limited number of lives at risk The economic consequences of death have a high severity but low probability of occurrence, such as the case of stop loss reinsurance We will present some results of a stochastic mortality analysis 8

9 Policyholder behavior should be considered in stochastic projections Changes in economic conditions can alter policyholder behavior How do we model policyholder behavior in a stochastic environment? How sensitive are results to changes in policyholder behavior? We have a presentation that addresses these questions with respect to variable annuity guarantees 9

10 Model speed is a key consideration when running scenarios What strategies can be employed to improve model speed and efficiency? Can we reduce the number of scenarios required without compromising the integrity of results? We have presentations on model speed and a case study of a project where we had to deal with the possibility of hundreds of millions of cell projections 10

11 Agenda 2:00 2:20 Introductory Remarks. Duncan Briggs 2:20 2:50 Economic Scenario Generators Doug Doll 2:50 3:10 Stochastic Mortality Noel Harewood 3:10 3:40 Policyholder Behavior Marc Altschull and Doug Robbins 3:40 3:55 Break more... 11

12 Agenda (cont.) 3:55 4:15 Stochastic Case Study Cheryl Tibbits 4:15 4:35 Model Speed via Representative Scenarios David Weinsier 4:35 4:45 Presentation of Results 4:45 5:00 Q&A 12