• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.23 no.1
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• pp.19-30
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• 2019

In this paper, we develop an accurate explicit finite difference method for the two-dimensional Black-Scholes equation with a hybrid boundary condition. In general, the correlation term in multi-asset options is problematic in numerical treatments partially due to cross derivatives and numerical boundary conditions at the far field domain corners. In the proposed hybrid boundary condition, we use a linear boundary condition at the boundaries where at least one asset is zero. After updating the numerical solution by one time step, we reduce the computational domain so that we do not need boundary conditions. To demonstrate the accuracy and efficiency of the proposed algorithm, we calculate option prices and their Greeks for the two-asset European call and cash-or-nothing options. Computational results show that the proposed method is accurate and is very useful for nonlinear boundary conditions.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.2
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• pp.121-137
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• 2022

In this paper, we investigate an efficient hybrid method for solving two-asset time fractional Black-Scholes partial differential equations. The proposed method is based on the Crank-Nicolson the radial basis functions methods. We show that, this method is convergent and we obtain good approximations for solution of our problems. The numerical results show high accuracy of the proposed method without needing high computational cost.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.585-599
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• 2024

Options pricing remains a critical aspect of finance, dominated by traditional models such as Black-Scholes and binomial tree. However, as market dynamics become more complex, numerical methods such as Monte Carlo simulation are accommodating uncertainty and offering promising alternatives. In this paper, we examine how effective different options pricing methods, from traditional models to machine learning algorithms, are at predicting KOSPI200 option prices and maximizing investment returns. Using a dataset of 2023, we compare the performance of models over different time frames and highlight the strengths and limitations of each model. In particular, we find that machine learning models are not as good at predicting prices as traditional models but are adept at identifying undervalued options and producing significant returns. Our findings challenge existing assumptions about the relationship between forecast accuracy and investment profitability and highlight the potential of advanced methods in exploring dynamic financial environments.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.55-66
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• 2012

Option pricing models using L$\acute{e}$evy processes are suggested as an alternative to the Black-Scholes model since empirical studies showed that the Black-Sholes model could not reflect the movement of underlying assets. In this paper, we investigate whether the Variance Gamma model can reflect the movement of underlying assets in the Korean stock market better than the Black-Scholes model. For this purpose, we estimate parameters and perform likelihood ratio tests using KOSPI 200 data based on the density for the log return and the option pricing formula proposed in Madan et al. (1998). We also calculate some statistics to compare the models and examine if the volatility smile is corrected through regression analysis. The results show that the option price estimated under the Variance Gamma process is closer to the market price than the Black-Scholes price; however, the Variance Gamma model still cannot solve the volatility smile phenomenon.

• Journal of applied mathematics & informatics
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• v.31 no.5_6
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• pp.769-784
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• 2013

We consider the modulus-based successive overrelaxation method for the linear complementarity problems from the discretization of Black-Scholes American options model. The $H_+$-matrix property of the system matrix discretized from American option pricing which guarantees the convergence of the proposed method for the linear complementarity problem is analyzed. Numerical experiments confirm the theoretical analysis, and further show that the modulus-based successive overrelaxation method is superior to the classical projected successive overrelaxation method with optimal parameter.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.459-479
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• 2007

Theories related to financial market has received big attention from the statistics community. However, not many courses on the topic are provided in statistics departments. Because the financial theories are entangled with many complicated mathematical and physical theories as well as ambiguously stated financial terminologies. Based on our experience on the topic, we try to explain the rather complicated terminologies and theories with easy-to-understand words. This paper will briefly cover the topics of basic terminologies of derivatives, Black-Scholes pricing idea, and related basic mathematical terminologies.

• Communications of the Korean Mathematical Society
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• v.30 no.3
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• pp.297-311
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• 2015

We present a robust and accurate boundary condition for pricing financial options that is a hybrid combination of the payoff-consistent extrapolation and the Dirichlet boundary conditions. The payoff-consistent extrapolation is an extrapolation which is based on the payoff profile. We apply the new hybrid boundary condition to the multi-dimensional Black-Scholes equations with a high correlation. Correlation terms in mixed derivatives make it more difficult to get stable numerical solutions. However, the proposed new boundary treatments guarantee the stability of the numerical solution with high correlation. To verify the excellence of the new boundary condition, we have several numerical tests such as higher dimensional problem and exotic option with nonlinear payoff. The numerical results demonstrate the robustness and accuracy of the proposed numerical scheme.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.211-233
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• 2004

This purpose of paper is to propose a European option pricing formula when the rate of return follows the leptokurtic distribution instead of normal. This distribution explains well the volatility smile and furthermore the option prices calculated under the leptokurtic distribution are shown to be closer to the market prices than those of Black-Scholes model. We make an estimation of the implied volatility and kurtosis to verify the fitness of the pricing formula that we propose here.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.21-30
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• 2022

To mitigate the environmental impacts of the energy sector, the government of South Korea has made a continuous effort to facilitate the development and commercialization of renewable energy. As a result, the efficiency of renewable energy plants is not a consideration in the potential site selection process. To contribute to the overall sustainability of this increasingly important sector, this study utilizes the Black-Scholes model to evaluate the economic value of potential sites for off-site wind farms, while analyzing the environmental mitigation of these potential sites in terms of carbon emission reduction. In order to incorporate the importance of flexibility and uncertainty factors in the evaluation process, this study has developed a site evaluation model focused on system dynamics and real option approaches that compares the expected revenue and expected cost during the life cycle of off-site wind farm sites. Using sensitivity analysis, this study further investigates two uncertainty factors (namely, investment cost and wind energy production) on the economic value and carbon emission reduction of potential wind farm locations.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.3
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• pp.88-95
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• 2024

In this article, we propose an efficient and accurate adaptive time-stepping numerical method for the Black-Scholes (BS) equations. The numerical scheme used is the finite difference method (FDM). The proposed adaptive time-stepping computational scheme is based on the maximum norm of the discrete Laplacian values of option values on a discrete domain. Most numerical solvers for the BS equations require a small time step when there are large variations in the solutions. To resolve this problem, we propose an adaptive time-stepping algorithm that uses a small time step size when the maximum norm of the discrete Laplacian values on a discrete domain is large; otherwise, a larger time step size is used to speed up the computation. To demonstrate the high performance of the proposed adaptive time-stepping methodology, we conduct several computational experiments. The numerical tests confirm that the proposed adaptive time-stepping method improves both the efficiency and accuracy of computations for the BS equations.