Modeling the price of hybrid equity warrants under stochastic volatility and interest rate
Keywords:
Equity warrants, stochastic, Cox-Ingersoll-Ross model, Heston model, hybrid modelsAbstract
Previous studies revealed that most local researchers frequently used the Black Scholes model to price equity warrants. However, the Black Scholes model was perceived of possessing too many drawbacks, such as big errors of estimation and mispricing of equity warrants. In this work, we consider the problem of pricing hybrid equity warrants based on a hybrid model of stochastic volatility and stochastic interest rate. The integration of stochastic interest rate using the Cox-Ingersoll-Ross (CIR) model, along with stochastic volatility of the Heston model was first developed as a hybrid model. We solved the governing stochastic equations and come up with analytical pricing formulas for hybrid equity warrants. This provides an alternative method for valuation of equity warrants, compared to the usual practice of utilizing the Black Scholes pricing formula.
References
Xiao, W., Zhang, X., Zhang, X., and Chen, X.. 2014. The valuation of equity warrants under the fractional Vasicek process of the shortterm interest rate. Physica A: Statistical Mechanics and its Applications, (Sept 2014), 320-337.
Lauterbach, B., and Schultz, B. 1990. Pricing warrants: an empirical study of the black-scholes and its alternatives. The Journal of Finance,(Sept. 1990), 1181-1209.
Aziz, K., A., A., Idris, M., F., I., M., Saian, R., and Daud, W., S., W. 2015. Adaptation of warrant price with black scholes model and historical volatility. In AIP Conference proceedings.
Arulanandam, D., B., V.,Sin, K., W., and Muita, M., A. 2017. Relevance of black scholes model on Malaysia KLCI options: an empirical study. Intercontinental Journal of Finance Research Review, (Feb. 2017).
N. I. I. I. Gunawan, S. N. I. Ibrahim and N. A. Rahim. 2017. A review on black-scholes model warrants in Bursa Malaysia. In AIP Conference Proceedings.
Vajargah, K., F. 2013. Application of doss transformation in approxiamtion of stochastic differential equations. Journal of Computer Science and Computational Mathematics, vol. 3, no. 3, 2013.
Duffie, D., and Glynn, P. 1995. Efficient monte carlo simulation of security prices. The Annals of Applied Probability, (May. 2001), 897-905.
Kurtz, T., G., and Philip, P. 1991. Wong-Zakai corrections, random evolutions and numerical schemes for SDE’s. Stochastic Analysis Academic Press,(Jan. 1991), 331-348.
Jacod, J., and Protter, P. 1998. Asymptotic error distributions for the Euler method for stochastic differential equations. The Annals of
Probability, (Jan. 1998), 267-307.
Mwamba, J., M., Thabo, L., and Uwilingiye, J. 2014. Modeling short term interest rate with stochastic differential equation in continuous time:linear and nonlinear models.MPRA Munich personal repec archive
Mansor, N., J., and Jaffar, M., M. 2014. Black-scholes finite difference modeling in forecasting of call warrant prices in Bursa Malaysia. In AIP Conference Proceedings.
Kim, Y., J. 2001. Option pricing under stochastic interest rate: an empirical investigation.Asia-Pacific Financial Markets,(Jan. 2001) 23-44.
Cao, J., Lian, G., and Roslan, T., R., N. 2016. Pricing variance swaps under stochastic volatility and stochastic interest rate. Applied Mathematics and Computation, (Jan. 2016),72-81.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 COMPUSOFT: An International Journal of Advanced Computer Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.
