FPGA Options Pricing Engine Latency Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for options pricing in the financial industry suffer from computational latency due to the intensive nature of models like the Cox, Ross, and Rubinstein (CRR) option pricing model, which is both iterative and binomial, leading to delays in providing pricing information to traders, resulting in potential missed trading opportunities.
Innovation Solution
The implementation of reconfigurable logic, such as Field Programmable Gate Arrays (FPGAs), to accelerate options pricing operations by configuring an options pricing engine that computes implied volatility and theoretical fair market prices in hardware, using techniques like iterative banded m-ary search and parallelized pipelining, and employing lookup tables to speed up calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional GPP-based software is used for options pricing, then the system is easier to implement and maintain, but computational latency increases significantly
Solution Approach 1:
The patent replaces the mechanical software execution system on general-purpose processors with a hardware-based Field Programmable Gate Array (FPGA) system. This substitution transforms the options pricing computation from a software-based iterative process into a hardware-accelerated parallel computation, dramatically reducing computational latency while maintaining implementability through reconfigurable logic devices.
Solution Approach 2:
The patent segments the options pricing computation into distinct functional modules including parameter reception, iterative banded m-ary search execution, lookup table queries, and result output. These segmented functions are implemented as separate hardware modules on the FPGA, allowing parallel execution and reducing overall computational latency.
2Measurement precision
If iterative binomial models like CRR are used for accurate options pricing, then measurement precision improves, but computational time increases
Solution Approach 1:
The patent pre-computes and stores critical values in lookup tables during system initialization, including binomial tree parameters, volatility values, and pricing factors. When a pricing request arrives, the system retrieves these pre-computed values from the lookup tables rather than recalculating them, significantly reducing computation time while maintaining pricing accuracy.
Solution Approach 2:
The patent implements an iterative banded m-ary search algorithm that dynamically adjusts search parameters based on the pricing context. The algorithm divides the volatility space into bands and uses m-ary search to efficiently converge on the implied volatility, changing the search strategy parameters adaptively to balance precision and speed.
3Productivity
If hardware acceleration using FPGA is implemented, then processing speed increases, but device complexity and initial implementation difficulty increase
Solution Approach 1:
The patent designs a universal FPGA-based pricing engine that can handle multiple types of options (calls, puts, American, European) and various pricing models through reconfigurable logic. The same hardware platform can be reprogrammed to accommodate different pricing requirements, reducing long-term complexity despite initial implementation challenges.
Solution Approach 2:
The patent introduces a firmware layer as an intermediary between the hardware FPGA logic and the software application interface. This firmware mediator handles complex control logic, coordinate between different hardware modules, and provide a simplified interface for external systems, easing the implementation burden.
Data Source
AI summary
A high speed technique for options pricing in the financial industry is disclosed that can provide both high throughput and low latency. Parallel/pipelined architectures are disclosed for computing an option's theoretical fair price. Preferably these parallel/pipelined architectures are deployed in hardware, and more preferably reconfigurable logic such as Field Programmable Gate Arrays (FPGAs) to accelerate the options pricing operations relative to conventional software-based options pricing operations.