Portfolio Liquidation Cost Allocation for Spread-Traded Positions
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Solution Overview
Problem
Existing systems face challenges in accurately calculating performance bonds for financial portfolios, particularly for large positions, as liquidation costs are difficult to estimate due to their impact on market prices, leading to increased complexity in determining recoverable amounts.
Innovation Solution
A method that accesses portfolio data to identify liquidation costs for outright-traded and spread-traded products, using estimated allocations to optimize liquidation costs and determine a performance bond based on these calculations, which involves sorting spread-traded products by cost functions and allocating positions to minimize overall liquidation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a holder of a portfolio goes bankrupt or otherwise defaults, the performance bond for that portfolio can be used to reduce losses resulting from the holder no longer being able to cover its positions, but attempting to liquidate a large position in a particular financial product can itself significantly affect the market for that product
Solution Approach 1:
The patent segments the liquidation process by dividing the portfolio into different products and further dividing each product into multiple allocation scenarios. Instead of liquidating a large position as a single block, the system breaks it down into smaller allocable units across different products (X1 through Xk) and evaluates multiple allocation scenarios to find the optimal liquidation path that minimizes market impact while ensuring full recovery.
Solution Approach 2:
The patent applies preliminary action by calculating and comparing multiple allocation scenarios before actual liquidation occurs. The system pre-determines the optimal allocation N(X1) through N(Xk) that minimizes liquidation costs by evaluating estimated allocations Nest(X1) through Nest(Xk) in advance, allowing the portfolio holder to understand the potential market impact and prepare accordingly rather than reacting during crisis liquidation.
2Measurement precision
If liquidation costs are calculated based on accurate estimates of recoverable amounts, then performance bond requirements can be optimized, but estimating liquidation recovery becomes more difficult as position sizes increase
Solution Approach 1:
The patent segments the complex liquidation estimation problem into manageable components by dividing the portfolio into individual products and then into allocable portions. Each product's liquidation cost is estimated separately based on its specific characteristics, and the total liquidation cost is aggregated from these segmented estimates. This segmentation allows for more accurate measurement without overwhelming complexity.
Solution Approach 2:
The patent applies dynamics by creating a flexible allocation system that can adapt to different liquidation scenarios. The optimal allocation N(X1) through N(Xk) is not fixed but is determined dynamically by comparing multiple estimated allocations Nest(X1) through Nest(Xk) and selecting the scenario that minimizes liquidation costs. This dynamic approach allows the system to handle varying position sizes and market conditions effectively.
3Productivity
If multiple allocation scenarios are evaluated to minimize liquidation costs, then optimized performance bonds can be determined, but the calculation process becomes more complex
Solution Approach 1:
The patent segments the allocation optimization process into distinct evaluable scenarios. Instead of attempting to optimize all allocations simultaneously in a monolithic calculation, the system divides the problem into separate estimated allocations for different products, evaluates each scenario independently, and then compares results. This segmentation makes the complex optimization process more manageable and computationally efficient.
Solution Approach 2:
The patent applies partial action by evaluating a selected number of significant allocation scenarios rather than exhaustively analyzing every possible allocation combination. The system identifies and compares key estimated allocations Nest(X1) through Nest(Xk) that are most likely to yield optimal results, achieving sufficient optimization without the prohibitive complexity of exhaustive analysis.
Data Source
AI summary
A set of estimated allocations Nest(X1) through Nest(Xk) of portfolio positions to products X1 through Xk may be determined, with products X1 through Xk including portfolio products and spread-traded products based on some of the portfolio products. Utilizing the set of estimated allocations, an optimized liquidation cost LCopt may be designated. Data indicating at least a portion of a performance bond based on the optimized liquidation LCopt may be output.