Derivative Portfolio Compression via Contract Aggregation
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Solution Overview
Problem
Current systems for managing exchange traded derivatives result in significant gross notional exposure, leading to burdensome capital requirements and inefficient operational processes due to the lack of scalability across maturity dates.
Innovation Solution
A clearing and reporting system that utilizes compression algorithms to reduce the gross notional amount outstanding of exchange traded derivatives positions, creating new derivatives products that represent multiple contracts, thereby optimizing capital requirements and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exchange traded derivatives positions are managed using traditional gross notional outstanding calculation, then regulatory capital requirements can be calculated, but the representation of clearing firm risk becomes inaccurate and inefficient
Solution Approach 1:
The patent merges multiple exchange traded derivatives contracts into a single compressed contract that represents the net risk exposure. By combining multiple contracts with similar risk characteristics into one aggregated position, the system achieves accurate risk representation while simplifying operational processes and reducing computational requirements.
Solution Approach 2:
The system changes the parameter of gross notional outstanding by introducing a compression factor that adjusts the notional amount based on the actual risk exposure. This parameter change allows the system to move from a conservative, inaccurate risk measurement to an accurate and efficient representation of clearing firm risk.
2Loss of information
If multiple exchange traded derivatives contracts are stored and processed individually, then complete contract details are maintained, but memory storage requirements and computing power consumption increase significantly
Solution Approach 1:
The patent combines multiple individual contract records into a single compressed contract entry. This merging reduces the quantity of data stored while preserving the essential risk information through aggregation. The compressed contract maintains the net risk exposure characteristics while eliminating redundant storage of individual contract details.
Solution Approach 2:
The system extracts only the critical risk-related parameters from individual contracts and stores them in the compressed representation. By taking out and retaining only the essential information (such as net notional amount, risk characteristics, and exposure metrics), the system reduces storage requirements while maintaining necessary contract details for risk management.
3Productivity
If compression algorithms are applied to reduce gross notional outstanding, then capital requirements and operational overhead are reduced, but the complexity of the compression process increases
Solution Approach 1:
The compression process is segmented into distinct modules that handle different aspects of contract aggregation and risk calculation. By dividing the complex compression algorithm into manageable segments (such as contract grouping, risk parameter calculation, and compression factor application), the system reduces the perceived complexity while maintaining operational efficiency.
Solution Approach 2:
The patent introduces an intermediary compression factor calculation layer that simplifies the relationship between individual contracts and the compressed representation. This intermediary layer acts as a mediator that translates complex contract data into simplified risk metrics, reducing the complexity of the overall compression process while achieving operational efficiency.
Data Source
AI summary
An illustrative computing device may include a processor and a non-transitory memory device for storing a data structure capable of being compressed, where the data structure includes a plurality of data elements and each of the plurality of data elements includes a date field and a quantity field. The computing device may process instructions to arrange the plurality of data elements in a consecutive series in date order based on a value stored in the date field of each data element, determine whether a gap appears in the consecutive series of data elements based on a value stored in the quantity field of each element, remove the determined gaps in each of the data elements, and repeat the determining and removing steps until a predetermined criterion has been reached.