DOI QR코드

DOI QR Code

MOBILE APP FOR COMPUTING OPTION PRICE OF THE FOUR-UNDERLYING ASSET STEP-DOWN ELS

  • JUNSEOK, KIM (DEPARTMENT OF MATHEMATICS, KOREA UNIVERSITY) ;
  • DAEUN, JEONG (DEPARTMENT OF MATHEMATICS, GANGNEUNG-WONJU NATIONAL UNIVERSITY) ;
  • HANBYEOL, JANG (DEPARTMENT OF FINANCIAL ENGINEERING, KOREA UNIVERSITY) ;
  • HYUNDONG, KIM (DEPARTMENT OF MATHEMATICS, GANGNEUNG-WONJU NATIONAL UNIVERSITY)
  • 투고 : 2022.11.21
  • 심사 : 2022.12.14
  • 발행 : 2022.12.25

초록

We present the user-friendly graphical user interface design and implementation of Monte Carlo simulation (MCS) for computing option price of the four-underlying asset step-down equity linked securities (ELS) using the Android platform. The ELS has been one of the most important and influential financial products in South Korea. Most ELS products are based on one-, two-, and three-underlying assets. However, currently there is a demand for higher coupon payment from ELS products because of the increased interest rate in financial market. In order to allow the investors to have higher coupon payment, it is necessary to design a multi-asset ELS such as four-asset step-down ELS. We conduct the computational experiments to demonstrate the performance of the Android platform for pricing four-asset step-down ELS. Furthermore, we perform a comparison test with a three-asset step-down ELS.

키워드

과제정보

The first author (J.S. Kim) was supported by the Brain Korea 21 FOUR through the National Research Foundation of Korea funded by the Ministry of Education of Korea. The corresponding author (H. Kim) was supported by the Research Institute of Natural Science of Gangneung-Wonju National University.

참고문헌

  1. Y. Liang and X. Xu, Variance and dimension reduction Monte Carlo method for pricing European multi-asset options with stochastic volatilities, Sustainability, 11(3) (2019), 815.  https://doi.org/10.3390/su11030815
  2. L. Bruno, Monte Carlo methods and market models for European swaptions pricing, Tesi di Laurea in Asset pricing, Luiss Guido Carli, Master's degree thesis, (2020), 77. 
  3. C. Yoo, Y. Choi, S. Kim, S. Kwak, Y. Hwang and J. Kim, Fast pricing of four asset Equity-Linked Securities using Brownian bridge, J. KSIAM, 25(3) (2021), 82-92. 
  4. H. Hwang, Y. Choi, S. Kwak, Y. Hwang, S. Kim and J. Kim, Efficient and accurate finite difference method for the four underlying asset ELS, The Pure and Applied Mathematics, 28(4) (2021), 329-341.  https://doi.org/10.7468/JKSMEB.2021.28.4.329
  5. R.F. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the econometric society, (1982), 987-1007. 
  6. T. Bollerslev, Generalized autoregressive conditional heteroskedasticity, Journal of econometrics, 31(3) (1986), 307-327.  https://doi.org/10.1016/0304-4076(86)90063-1
  7. J. Yu, Forecasting volatility in the New Zealand stock market, Applied Financial Economics, 12(3) (2002), 193-202.  https://doi.org/10.1080/09603100110090118
  8. D. Alberg, H. Shalit and R. Yosef, Estimating stock market volatility using asymmetric GARCH models, Applied Financial Economics, 18(15) (2008), 1201-1208.  https://doi.org/10.1080/09603100701604225
  9. P.R. Winters, Forecasting sales by exponentially weighted moving averages, Management science, 6(3) (1960), 324-342.  https://doi.org/10.1287/mnsc.6.3.324
  10. W. Kuang, Conditional covariance matrix forecast using the hybrid exponentially weighted moving average approach, Journal of Forecasting, 40(8) (2021), 1398-1419.  https://doi.org/10.1002/for.2776
  11. P.S. Hagan, D. Kumar, A.S. Lesniewski and D.E. Woodward, Managing smile risk, The Best of Wilmott, 1 (2002), 249-296. 
  12. D. Brigo and F. Mercurio, Lognormal-mixture dynamics and calibration to market volatility smiles, International Journal of Theoretical and Applied Finance, 5(04) (2002), 427-446.  https://doi.org/10.1142/S0219024902001511
  13. R. Engle, Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models, Journal of Business & Economic Statistics, 20(3) (2002), 339-350.  https://doi.org/10.1198/073500102288618487
  14. C.T. Brownlees and R. Engle, Volatility, correlation and tails for systemic risk measurement, Available at SSRN (2012), 1611229. 
  15. A.W. Ayele, E. Gabreyohannes and Y.Y. Tesfay, Macroeconomic determinants of volatility for the gold price in Ethiopia: the application of GARCH and EWMA volatility models, Global Business Review, 18(2) (2017), 308-326.  https://doi.org/10.1177/0972150916668601
  16. Y. Zhu, Comparison of Three Volatility Forecasting Models, Doctoral dissertation, The Ohio State University (2018). 
  17. D. Gabor and S. Brooks, The digital revolution in financial inclusion: international development in the fintech era, New Polit. Econ., 22(4) (2017), 423-436.  https://doi.org/10.1080/13563467.2017.1259298
  18. E.Z. Milian, M.D.M. Spinola and M.M. de Carvalho, Fintechs: A literature review and research agenda, Electron. Commer. Res. Appl., 34 (2019), 100833.  https://doi.org/10.1016/j.elerap.2019.100833
  19. W. Jian, J. Ban, J. Han, S. Lee and D. Jeong, Mobile platform for pricing of Equity-Linked Securities, J. KSIAM, 21(3) (2017), 181-202. 
  20. H. Jang, H. Han, H. Park, W. Lee, J. Lyu, J. Park, H. Kim, C. Lee, S. Kim, Y. Choi and J. Kim, Android application for pricing two-and three-asset Equity-Linked Securities, J. KSIAM, 23(3) (2019), 237-251. 
  21. P. Glasserman, Monte Carlo methods in financial engineering, Springer Science & Business Media, 2013. 
  22. J. Wang and C. Liu, Generating multivariate mixture of normal distributions using a modified Cholesky decomposition, Proc. 38th Conf. Winter Simul., 2006.