A parallel Particle swarm optimization algorithm for option pricing
Bibliographic record
Abstract
Option pricing is one of the challenging problems of computational finance. Nature-inspired algorithms have gained prominence in real world optimization problems such as in mobile ad hoc networks. The option pricing problem fits very well into this category of problems due to the ad hoc nature of the market. Particle swarm optimization (PSO) is one of the novel global search algorithms based on swarm intelligence. We first show that PSO could be effectively used for the option pricing problem. The results are compared with standard classical Black-Scholes-Merton model for simple European options. In this study, we developed two algorithms for option pricing using Particle Swarm Optimization (PSO). The first algorithm we developed is synchronous option pricing algorithm using PSO (SPSO), and the second is parallel synchronous option pricing algorithm. The pricing results of these algorithms are close when compared with classical Black-Scholes-Merton model for simple European options. We test our Parallel Synchronous PSO algorithm in three architectures: shared memory machine using OpenMP, distributed memory machine using MPI and on a homogeneous multicore architecture running MPI and OpenMP (hybrid model). The results show that the hybrid model handles the load well as we increase the number of particles in simulation while maintaining equivalent accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".