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Challenges of Implementing and Managing a Derivatives Program Frank Zhang, CFA, FRM, FSA, MSCF, PRM Head of Structured Derivatives and Assets Strategies ING Wealth Management SOA Investment Symposium  March 25-26, 2008  New York City
A Virtual Team for Equity Derivatives Hedging Program VA/FIA Hedging Program Other Mid-Back Office √  Investment Management √  Market Risk Management ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Teams Within Market Risk Management Market Risk Management Hedging Operations and Performance Management Advanced Financial Modeling √  Structured Derivatives and Assets Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
We recently established a new FIA hedging program Study the derivative Strategies   ,[object Object],A Case Study – Setting Up a New FIA Hedging Program 1 Set up mid/back office functions 2 3
[object Object],Back testing over 20 years Historical implied volatility surfaces and interest rate curves Derivatives pricing modules Nested stochastic derivatives pricing Weekly conference calls from offices cross the country Discussions with Chief Actuary & Chief Risk Officer, Chief Financial Officer, Head of Capital, Head of ALM, Head of Pricing BU ALCO and US Region ALCO approvals  Case Study (Cont’d 1)
Sample Back Testing P/L Breakdown
[object Object],Case Study (Cont’d 2) Daily hedging production Systems/data Cash management P/L and performance attribution Risk reporting and monitoring Reconciliation between hedging and accounting
FIA Hedging Diagram ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],C++  Pricing  Engine IIM Atlanta Trades Execution Admin  System ,[object Object],[object Object],Reporting Hedge Accounting PD Crediting ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Policy  Information Database
FIA Hedging Account P/L Tracking Diagram ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Liability (FIADB)   (Initial Liability + New Policies) New/Renewal Policies Daily MTM P/L Financial Reporting/Accounting Reconciliation and earnings volatility FIA Crediting Process Crediting should reflect long term average savings, but may reflect short term P/L experience, if desired sometimes.  ,[object Object],[object Object],[object Object],[object Object],[object Object],General Account $ Hedging Account $ Liability Option Premium $ Interest  Credits $ Daily  G/L $ Daily OTC Options Buy/Sell $
[object Object],Case Study (Cont’d 3) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How to decide a good trading rule Liability Model (VA/FIA) Hedging projection system May be stochastic on stochastic simulations Model distributions similar to actual block ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],20 Years Market Data ,[object Object],[object Object],[object Object],[object Object],[object Object],Daily Tick Data For Realistic Trading Multiple Criteria
“ Liability Roll Forward” & “Liability Attribution” Processes Beginning Period Liability Option Value & Greeks Ending Period Liability Option Value & Greeks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Expected vs. Actual Changes  In Option Values and Greeks
“ Hedge Performance Attribution” Process Net Gain(Loss) over the period ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],ING’s proprietary Performance Attribution Process has been used to successfully track and manage the various asset and liability drivers that contribute to hedge program net gains and losses. It represents an important tool for deepening ING’s understanding and gaining valuable insights into the key drivers of successful VA hedge program performance, allowing ING to quickly identify issues and take corrective action. It serves as an important feedback & control mechanism for things like fund manager performance relative to benchmarks, market parameters, the hedge process, and policyholder behavior. Attributable To
“ Tracking Error Decomposition” Net Gain / (Loss) due to Tracking Error ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Attributable To

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2008 Investment Symposium Zhang 3 24 08

  • 1. Challenges of Implementing and Managing a Derivatives Program Frank Zhang, CFA, FRM, FSA, MSCF, PRM Head of Structured Derivatives and Assets Strategies ING Wealth Management SOA Investment Symposium March 25-26, 2008 New York City
  • 2.
  • 3.
  • 4.
  • 5.
  • 6. Sample Back Testing P/L Breakdown
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.

Editor's Notes

  1. To mimic the type of options (put or call) and the distributions of option tenors (years to maturity), given the fact that we don’t yet have a true hedging projection system (Pegasus Release 2), we have built two simplified liability portfolios – one for VA and one for FIA. See appendix for details. To compare the results of different trading rules tested, we selected a few bad market crash windows during the last 20 years, including the worst equity crash of 1987, for tail events. We build a 20-year historical data bank to incorporate price/dividend data, swap curves movements, and volatility surface movements. To truly test the trading rule as it would be experienced during the trading days, it is required to look at the changes during the trading days, not just looking at the daily closing prices. Except for the most recent days, we constructed daily “ticks” using Brownian Bridge method that made the testing much more robust and realistic. The daily tick data greatly enhanced our data history. See appendix for more details of the “Brownian Bridge” methodology. Our main criteria are transaction costs, hedge effectiveness, performance in tail events. Due to the complexity of the system and multiple criteria for selecting a good trading rule, we will use the graph to assist our comparison of the testing cases.