Variable Annuity Hedging Program Generator
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Solution Overview
Problem
Hedging the guaranteed benefits of variable annuity contracts is complex due to active fund management, numerous investment options, diverse guarantee structures, path-dependent benefits, and interest-rate dependence, making existing solutions computationally intensive and difficult to verify.
Innovation Solution
A computer-based method generating both interpreted and compiled code for hedging variable annuity contracts, using vectorized integrated scenario sets, multi-threaded calculations, and user-selectable projection modes to maximize data locality and distribute computation across servers, while allowing for easy verification and high performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hedging methods are used for variable annuity contracts, then comprehensive coverage of all guarantee structures is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the hedging problem by separating the valuation of guaranteed benefits into distinct computational modules that can be processed independently. Each variable annuity contract's guarantee is evaluated separately using standardized procedures, allowing the overall complex problem to be broken down into manageable segments that reduce computational burden while maintaining accuracy.
Solution Approach 2:
The patent changes key parameters by using simplified assumptions for benefit valuation, such as standard mortality tables, fixed interest rate curves, and predefined guarantee structures. These parameter standardizations reduce the computational complexity by eliminating the need to calculate every possible scenario while preserving the essential characteristics needed for accurate hedging.
2Measurement precision
If detailed analysis of all VA contract features is performed, then hedging accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing standard valuation factors, such as mortality rates, interest rate curves, and guarantee benefit formulas, before the actual hedging computation. This allows the main processing stage to use these pre-computed values directly, significantly reducing processing time while maintaining valuation accuracy.
Solution Approach 2:
The patent uses copying by creating standardized templates for common guarantee structures and benefit valuation methods. Once a valuation model is developed for a particular guarantee type, it can be copied and applied to multiple similar contracts, reducing redundant calculations and accelerating processing while preserving accuracy through consistent application of the same validated models.
3Adaptability or versatility
If custom projection code is allowed for user specifications, then flexibility and adaptability improve, but verification difficulty increases
Solution Approach 1:
The patent implements feedback mechanisms that automatically validate custom projection code against predefined correctness criteria and performance standards. The system provides immediate feedback to users about whether their custom code meets required specifications, making verification systematic and automated rather than manual and error-prone, thus maintaining flexibility while reducing verification difficulty.
4Productivity
If vectorized integrated scenario sets are used with optimized data arrangement, then computational efficiency improves, but data structure complexity increases
Solution Approach 1:
The patent merges multiple data structures into unified vectorized scenario sets that integrate all relevant parameters (mortality, interest rates, fund values, guarantee benefits) into a single coherent structure. This consolidation improves computational efficiency by enabling vectorized operations and reducing memory access overhead, while the systematic organization of the merged structure manages the inherent complexity through consistent data relationships.
Data Source
AI summary
A computer-based method for generating a program for hedging guaranteed benefits of a set of variable annuity (VA) contracts, including automatically generating interpreted and compiled contract value projection code; generating vectorized integrated scenario sets including arbitrage-free interest rate, hedging instrument, and VA fund values based on financial market data and regression and cointegration analysis of the VA funds and user-selected hedging instruments; multi-threaded vectorized calculating of present value PVGij of projected VA benefit guarantee cash flows for each VA contract i for each vectorized integrated scenario set j; calculating of differences of PVGij to determine sensitivities to financial parameters (“Greeks”) for each contract; calculating of present value PVAij of projected asset cash flows for each asset i for each vectorized integrated scenario set j; calculating of differences of PVAij to determine sensitivities to financial parameters (“Greeks”) for each asset; and, presenting of summary and detail asset and liability Greeks.