Estimation of option's parameters based on particle swarm optimization algorithm
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DOI: 10.23977/iset.2019.034
Author(s)
Guang He, Xiaoli Lu
Corresponding Author
Guang He
ABSTRACT
As a landmark in contingent claim theory, Black-Scholes model has been widely used in financial markets. However, these exists a difficulty that some important variables should be estimated when applying the model to price options. The more accurately these parameters are estimated, the more accurate approximate values of option prices will be. In order to seek estimates of these parameters, an improved particle swarm optimization algorithm is considered, in which the mutation operation is made on particles past optimal and global optimal positions. Then we apply the algorithm to obtain the approximations of volatility and risk-free rate in European option model. Compared with the binary particle swarm optimization algorithm with bit change mutation, our algorithm is better in stability and convergence speed.
KEYWORDS
Black-Scholes model, Option pricing, Volatility, Particle swarm optimization algorith