Curve Engine Pricing Framework for Financial Interdependencies
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Solution Overview
Problem
Current pricing models for financial instruments consider each market microstructure independently, neglecting interdependencies between instruments across different microstructures and the broader economic macrostructure, leading to incomplete pricing reflections.
Innovation Solution
A system and method for constructing a virtual financial complex network using computing devices to blend market color data with price data, forming an optimization model that determines minimum market prices for financial instruments across multiple markets, accounting for inter- and intra-market relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If financial instruments are priced according to their respective microstructure groupings independently, then each individual microstructure pricing is reflective and accurate, but the pricing does not account for interdependencies between instruments across different microstructures and the broader economic macrostructure
Solution Approach 1:
The pricing framework is segmented into multiple hierarchical levels: microstructure level (individual market segments), mesostructure level (market groups), and macrostructure level (economic factors). Each level can be processed and priced independently using specialized models, while the hierarchical architecture allows integration of results across levels, thus maintaining pricing accuracy while managing complexity through modular organization.
Solution Approach 2:
The patent introduces a new dimensional perspective by mapping financial instruments onto a multi-level hierarchical structure that adds macroeconomic and mesostructural dimensions to traditional microstructure pricing. This dimensional expansion enables simultaneous consideration of local microstructure characteristics and global economic interdependencies without requiring a complete redesign of the entire pricing system.
2Reliability
If a global pricing framework considering inter- and intra-market interdependencies is implemented, then comprehensive pricing reflection is achieved, but computational complexity and processing requirements increase significantly
Solution Approach 1:
The computational framework is divided into sequential processing stages corresponding to different hierarchical levels. Microstructure pricing computations are performed first for individual market segments, then aggregated to mesostructure level, and finally integrated at macrostructure level. This segmentation of computational tasks reduces overall complexity by breaking down the global pricing problem into manageable sub-problems that can be solved independently and iteratively.
Solution Approach 2:
The framework performs preliminary computations at lower hierarchical levels before proceeding to higher levels. Microstructure pricing and risk assessments are completed in advance, providing input data for mesostructure and macrostructure analyses. This preliminary action approach ensures that foundational pricing information is ready and processed efficiently, reducing the computational burden on higher-level models while maintaining comprehensive pricing coverage.
3Adaptability or versatility
If market color data is blended with price data to determine blended pricing information, then more comprehensive pricing factors are incorporated, but data processing and integration complexity increases
Solution Approach 1:
Market color data and price data are processed and blended separately at different hierarchical levels before integration. At the microstructure level, market color indicators (liquidity, volatility, trading volume) are processed independently from price data. The processed results are then aggregated and blended at mesostructure and macrostructure levels, allowing flexible adaptation to different data types while managing processing complexity through hierarchical organization.
Solution Approach 2:
The blending mechanism is designed as a universal process that can handle multiple types of data (market color, price, volume, liquidity indicators) and multiple hierarchical levels through a consistent methodology. The same blending algorithms and data processing techniques are applied across different market segments and time horizons, providing adaptability to various pricing scenarios while reducing overall processing complexity through standardized procedures.
Data Source
AI summary
Systems and methods for pricing financial instruments include constructing, via at least one computing device comprising one or more processors executing computer-executable instructions stored in memory, a virtual financial complex network comprising one or more interrelated financial markets. Market color data related to at least one of the financial markets is then blended with price data to determine blended pricing information. This blended pricing information is then used to define an objective function that when solved, via an optimization model, determines a minimum market price for each financial instrument across the one or more financial markets.