High-Accurate Numerical Schemes for Black–Scholes Models with Sensitivity Analysis

Samir Kumar Bhowmik, Jakobin Alam Khan and Nasser Saad

Journal of Mathematics, 2022, vol. 2022, 1-19

Abstract: The significance of both the linear and nonlinear Black-Scholes partial differential equation model is huge in the field of financial analysis. In most cases, the exact solution to such a nonlinear problem is very hard to obtain, and in some cases, it is impossible to get an exact solution to such models. In this study, both the linear and the nonlinear Black-Scholes models are investigated. This research mainly focuses on the numerical approximations of the Black-Scholes (BS) model with sensitivity analysis of the parameters. It is to note that most applied researchers use finite difference and finite element-based schemes to approximate the BS model. Thus, an urge for a high accurate numerical scheme that needs fewer grids/nodes is huge. In this study, we aim to approximate and analyze the models using two such higher-order schemes. To be specific, the Chebyshev spectral method and the differential quadrature method are employed to approximate the BS models to see the efficiency of such highly accurate schemes for the option pricing model. First, we approximate the model using the mentioned methods. Then, we move on to use the numerical results to analyze different aspects of stock market through sensitivity analysis. All the numerical schemes have been illustrated through some graphics and relevant discussions. We finish the study with some concluding remarks.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:4488082

DOI: 10.1155/2022/4488082

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