Session 94 PD, Actuarial Modeling Techniques for Model Efficiency: Part 2. Moderator: Anthony Dardis, FSA, CERA, FIA, MAAA

Transcription

1 Session 94 PD, Actuarial Modeling Techniques for Model Efficiency: Part 2 Moderator: Anthony Dardis, FSA, CERA, FIA, MAAA Presenters: Ronald J. Harasym, FSA, CERA, FCIA, MAAA Andrew Ching Ng, FSA, MAAA SOA Antitrust Disclaimer SOA Presentation Disclaimer

2 Forecasting Stochastic Required Capital Ron Harasym Vice President & Actuary Andrew Ng Vice President & Actuary 2016 SOA Annual Meeting Session 94 PD Actuarial Modeling Techniques for Model Efficiency Part 2 October 25, 2016 Las Vegas, NV

3 Time for Some Really Serious Questions! Question #1: How much acreage do you need for a flock of 50,000 sheep? How About 50 sheep? Question #2: What if you could get similar value from a flock of just 50 sheep! 1000: SOA Annual Meeting October 25,

4 Agenda Background: Stochastic Required Capital Valuation Forecasting Stochastic Required Capital SOA Annual Meeting October 25,

5 The Metric Stochastic Required Capital (SRC) is the average, over the least favorable stochastic asset liability simulations, of the projected maximum present value of balance sheet deficiency Projections are based on portfolio run-off asset/liability simulations Based on statutory accounting, instead of GAAP or market value balance sheet Conditional tail expectation, a CTE measure, is used to reflect downside risk of loss Can define internal required statutory-based capital 2016 SOA Annual Meeting October 25,

6 Scenario Size & Initial Market Conditions Matter The above analysis is based on SRC valuation of a large block of life business assuming recent market conditions. The convergence profile varies by initial market condition. A more stressed initial environment will likely have wider ranges and slower convergence SOA Annual Meeting October 25,

7 Challenges of Stochastic Capital Modeling A reliable stochastic capital measure often needs to be reflective of a large set of scenarios, with the size of scenario set driven by: the portfolio s sensitivity to risk drivers, run-off horizon, and the CTE level Applications Process time for normal scale Tolerance of any repetitive manual process is substantially Need to deal with large set of super size files and limited storage space can grow exponentially as size of scenario set increases production often no longer work lower 2016 SOA Annual Meeting October 25,

8 Agenda Background: Stochastic Required Capital Valuation Forecasting Stochastic Required Capital SOA Annual Meeting October 25,

9 A Real Life Case Study Develop super fast stochastic required capital forecasting capability Product Lines Forecast Horizon Market Dynamics Capital Metric Ordinary Life Fixed Deferred Annuity One year beyond in-force date Interest rates, equity, credit CTE on run-off projection Enable super fast valuation of SRC at forecast date reflecting a wide range of plausible market movements 2016 SOA Annual Meeting October 25,

10 Forecasted Stochastic Required Capital Sample Forecast SRC Analytics Dynamics of Risk Drivers over Forecasting Horizon Above illustration is based on capital data point/surface of a large block of life insurance business 2016 SOA Annual Meeting October 25,

11 Risk Driver #1 Sample Forecast SRC Analytics Risk Driver #2 Dynamics of Risk Drivers over Forecasting Horizon Forecasted Stochastic Required Capital Above illustration is based on capital data point/surface of a large block of life insurance business 2016 SOA Annual Meeting October 25,

12 Forecasting SRC Modeling: Value Added Strengthen a company s capital management program Enable better understanding of profile and dynamics of risk and capital Facilitate development of capital sensitivity measures (i.e., Greeks ) Allow robust capital planning to address impact due to volatile markets Answer management s What If questions effectively & efficiently 2016 SOA Annual Meeting October 25,

13 Forecasting Needs Nested Stochastic Simulations Individual Stochastic Required Capital Valuation T 0 Forecast Horizon T 1 Real World Portfolio Run-Off Horizon Real World T n 2016 SOA Annual Meeting October 25,

14 Mission (Im)Possible Forecasting SRC necessitates very fast development of hundreds of thousands of CTE valuations over a range of future market conditions CTE measures need more simulations as only the tail scenarios matter Stochastic metrics require large simulation set to be reliable and useful Conventional brute force simulation approach is neither practical nor cost justifiable for stochastic capital metrics 2016 SOA Annual Meeting October 25,

15 Making It Possible: A Hybrid Approach Tail Scenario Selection Least Square Monte Carlo Principal Components Low Discrepancy Sequences Scenario Stratification Proxy Functions The only way of discovering the limits of the possible is to venture a little way past them into the impossible. Sir Arthur C. Clarke s Second Law Model Point Clustering Grid Computing 2016 SOA Annual Meeting October 25,

16 Making It Possible: The Use of Proxy Functions Define Proxy Functions Re- Evaluate Fit 2016 SOA Annual Meeting October 25,

17 Forecasted Stochastic Required Capital Making It Possible: The Use of Proxy Functions Dynamics of Risk Drivers over Forecasting Horizon 2016 SOA Annual Meeting October 25,

18 Forecasted Stochastic Required Capital Making It Possible: The Use of Proxy Functions Valuation of SRC Dynamics of Risk Drivers over Forecasting Horizon 2016 SOA Annual Meeting October 25,

19 Forecasted Stochastic Required Capital Making It Possible: The Use of Proxy Functions Valuation of SRC Valuation of SRC Valuation of SRC Dynamics of Risk Drivers over Forecasting Horizon 2016 SOA Annual Meeting October 25,

20 Forecasted Stochastic Required Capital Making It Possible: The Use of Proxy Functions Valuation of SRC Valuation of SRC Valuation of SRC Dynamics of Risk Drivers over Forecasting Horizon 2016 SOA Annual Meeting October 25,

21 Forecasted Stochastic Required Capital Making It Possible: The Use of Proxy Functions Valuation of SRC Valuation of SRC Valuation of SRC Dynamics of Risk Drivers over Forecasting Horizon 2016 SOA Annual Meeting October 25,

22 Forecasted Stochastic Required Capital Making It Possible: The Use of Proxy Functions Valuation of SRC Valuation of SRC Valuation of SRC Dynamics of Risk Drivers over Forecasting Horizon 2016 SOA Annual Meeting October 25,

23 Making It Possible: The Use of Proxy Functions Define Proxy Function Assume SRC at time T 1 can be expressed as a closed form function of SRC at T 0 and the dynamics of risk variables between T 0 and T 1 Parameterize Proxy Function Pick fitting points and perform brute force SRC valuations Identify the optimal proxy fitting function through regression Validate proxy function with extensive out-of-sample testing Re-evaluate Proxy Function Set up the closed form proxy function calculation Define sets of market dynamics for future SRC evaluation Re-evaluate proxy to derive all SRC valuations real time 2016 SOA Annual Meeting October 25,

24 Making It Possible: Combo Scenario Techniques Real time forecasting of SRC is a scenario intensive exercise Creative development and usage of efficiency techniques make it possible with nearly a 1000:1 scenario reduction Extension of LSMC Property (5:1) Scenario Stratification (8:1) Tail Scenario Selection (4:1) LSMC Proxy Fitting (6:1) 50,000 scenarios: credible SRC valuation 10,000 1,200 scenarios 300 scenarios Process starts with 10,000 scenarios (per fitting point) 50 simulations: for each SRC valuation 2016 SOA Annual Meeting October 25,

25 Making It Possible: Modular Apps! Tail Scenario Picker Scenario Stratifier LSMC Scenario Selector Model Point Cluster Model Data Extractor Scenario Xformer Nested Scenario Creator SRC Calculator Principal Component Analyzer Sobel Sequence Seeker Apps were developed for every key step with large scale processing capability LSMC Proxy Fitter SRC Forecaster 2016 SOA Annual Meeting October 25,

26 What s the BIG deal Data Processed Proxy Fitting Proxy Validation Distinct scenarios utilized 20+ million 3+ million Run-off simulations 0.3+ million 1.7+ million Project file size 5TB + Project file count 660, SOA Annual Meeting October 25,

27 What s the BIG deal Data Processed Proxy Fitting Proxy Validation Distinct scenarios utilized 20+ million 3+ million Run-off simulations 0.3+ million 1.7+ million Project file size 5TB + Project file count 660, SOA Annual Meeting October 25,

28 The Magic of Ultra Deep Model Efficiency Any sufficiently advanced technology is indistinguishable from magic. - Sir Arthur C. Clarke s Third Law Stochastic Required Capital Valuation Error % 2016 SOA Annual Meeting October 25,

29 The Magic of Ultra Deep Model Efficiency Any sufficiently advanced technology is indistinguishable from magic. - Sir Arthur C. Clarke s Third Law Stochastic Required Capital Valuation Error % 2016 SOA Annual Meeting October 25,

30 The Magic of Ultra Deep Model Efficiency Any sufficiently advanced technology is indistinguishable from magic. - Sir Arthur C. Clarke s Third Law Stochastic Required Capital Valuation Error % 2016 SOA Annual Meeting October 25,

31 The Magic of Ultra Deep Model Efficiency Any sufficiently advanced technology is indistinguishable from magic. - Sir Arthur C. Clarke s Third Law Stochastic Required Capital Valuation Error % 2016 SOA Annual Meeting October 25,

32 The Magic of Ultra Deep Model Efficiency Any sufficiently advanced technology is indistinguishable from magic. - Sir Arthur C. Clarke s Third Law Stochastic Required Capital Valuation Error % 2016 SOA Annual Meeting October 25,

33 The Magic of Ultra Deep Model Efficiency Any sufficiently advanced technology is indistinguishable from magic. - Sir Arthur C. Clarke s Third Law Stochastic Required Capital Valuation Error % 2016 SOA Annual Meeting October 25,

34 The Magic of Ultra Deep Model Efficiency Any sufficiently advanced technology is indistinguishable from magic. - Sir Arthur C. Clarke s Third Law Stochastic Required Capital Valuation Error % 2016 SOA Annual Meeting October 25,

35 The Magic of Ultra Deep Model Efficiency Reliability of forecasted stochastic required capital proxy can be better than a level suggested by fitting point scenarios utilized! A fitting point forecasted SRC valuation utilizes only 50 balance sheet simulations based on a set of 10,000 scenarios, partly benefiting from stress scenario selection Comparing to forecasted SRC using 50,000 brute force balance sheet simulations: errors of forecasted SRC proxy typically stay within 5% errors of forecasted SRC using 10,000 brute force simulations can easily be as high as 15% The LSMC approach has made such efficient use of the available information that it is capable of extracting an impressively large amount of information from a comparatively much smaller data set! 2016 SOA Annual Meeting October 25,

36 Key Success Factors Small Dynamic Team Individuals with diversified talent Be compact and agile to minimize overhead and communication challenge Customized Techniques Valid stress scenario selection & scenario stratification methods reflective of portfolio s attributes Model/Process Governance Modular approach coupled with automation, tight controls, and clearly defined process flows Thought Leadership Courageous to challenge status quo and be innovative to deal with challenges Robust Project Design Smart planning for maximum efficiency and embrace flexibility to deal with unknowns Efficient Execution Emphasize efficient process automation, be agile, and be relentless on execution Keep It Simple Follow principle of parsimony, be pragmatic, and no unnecessary complexity 2016 SOA Annual Meeting October 25,

37 2016 SOA Annual Meeting October 25,

38 Contact Information: Ron Harasym Vice President & Actuary Andrew Ng Vice President & Actuary (212) (212) New York Life Insurance Company 51 Madison Ave New York, NY 10010