Using Particle Swarm Optimization Algorithm to Calibrate the Term Structure Model

Abstract

One of the advantages of stochastic differential equations (SDE) is that they can follow a variety of different trends so that they can establish complex dynamic systems in the economic and financial fields. Although some estimation methods have been proposed to identify the unknown parameters in virtue of the results in the SDE model to speed up the process, these solutions only focus on using explicit approach to solve SDEs, and therefore they are not reliable to deal with data source merged being large and varied. Thus, this study makes progress in creating a new implicit way to fill in the gaps of accurately calibrating the unknown parameters in the SDE model. Essentially, the primary goal of the article is to generate rigid SDE simulation. Meanwhile, the particle swarm optimization method serves a purpose to search and simultaneously obtain the optimal estimation of the model unknown parameters in the complicated experiment of parameter space in an effective way. Finally, in an interest rate term structure model, it is verified that the method effectively deals with parameter estimation in the SDE model.

Digital Universal Financial Credit Risk Analysis Using Particle Swarm Optimization Algorithm with Structure Decision Tree Learning-Based Evaluation Model

“…Many researchers have looked at credit risk assessment and assessment models using a variety of techniques. Zhou et al and Lowd and Davis [6,7] suggested a particle swarm optimization algorithm-based financial credit risk assessment technique. e study in [8] provided an xgbfs-based financial credit risk assessment approach that decreases the dimension of the user's credit data, trains the xgboost assessment model, and then analyses the user's credit risk.…”

Bond and Option Prices under Skew Vasicek Model with Transaction Cost

“…erefore, the process is a suitable noise for financial models, when interest rate amounts increase. Besides, to make a more effective model, researchers use different methods to calibrate models' parameters by considering the real data market [8][9][10][11][12]. e maximum likelihood estimation (MLE) is a famous method to calibrate the model's parameters.…”