Sudarshan Santra

Dr. Sudarshan Santra

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Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 September 2023 in Computers & Mathematics with Applications
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Journal Article
Closed access
SSSudarshan Santra
Sudarshan Santra
JMJugal Mohapatra
Jugal Mohapatra
PDPratibhamoy Das
Pratibhamoy Das
Debajyoti ChoudhuriDebajyoti Choudhuri
Debajyoti Choudhuri

1 January 2022 in Journal of Computational and Applied Mathematics
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Journal Article
Closed access
SSSudarshan Santra
Sudarshan Santra
JMJugal Mohapatra
Jugal Mohapatra

19 April 2023 in Computational Economics
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Journal Article
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JMJugal Mohapatra
Jugal Mohapatra
SSSudarshan Santra
Sudarshan Santra
HIGINIO RAMOSHIGINIO RAMOS
HIGINIO RAMOS
In this work, we study the numerical solution for time fractional Black-Scholes model under jump-diffusion involving a Caputo differential operator. For simplicity of the analysis, the model problem is converted into a time fractional partial integro-differential equation with a Fredholm integral operator. The L1 discretization is introduced on a graded mesh to approximate the temporal derivative. A second order central difference scheme is used to replace the spatial derivatives and the composite trapezoidal approximation is employed to discretize the integral part. The stability results for the proposed numerical scheme are derived with a sharp error estimation. A rigorous analysis proves that the optimal rate of convergence is obtained for a suitable choice of the grading parameter. Further, we introduce the Adomian decomposition method to find out an analytical approximate solution of the given model and the results are compared with the numerical solutions. The main advantage of the fully discretized numerical method is that it not only resolves the initial singularity occurred due to the presence of the fractional operator, but it also gives a higher rate of convergence compared to the uniform mesh. On the other hand, the Adomian decomposition method gives the analytical solution as well as a numerical approximation of the solution which does not involve any mesh discretization. Furthermore, the method does not require a large amount of computer memory and is free of rounding errors. Some experiments are performed for both methods and it is shown that the results agree well with the theoretical findings. In addition, the proposed schemes are investigated on numerous European option pricing jump-diffusion models such as Merton’s jump-diffusion and Kou’s jump-diffusion for both European call and put options.