Effectiveness of Tridiagonal Path Dependent Option Valuation in Weak Derivative Market Environment
DOI:
https://doi.org/10.17010/ijf/2015/v9i9/77192Keywords:
Call and Put Options
, Crank-Nicolson Method, Derivatives, Partial Differential Equation, Path DependentC3
, C4, G3, M1Paper Submission Date
, September 16, 2014, Paper sent back for Revision, February 6, 2015, Paper Acceptance Date, April 26, 2015.Abstract
Accurate option price path is needed for risk management and for financial reporting as the accounting standards insist on mark to market value for derivative products. The binary models - Black Scholes model and Longstaff methods provide an insight on option pricing, but they are seldom validated with real data. Most of the research studies demonstrate the validity through algebra or by solving partial differential equations with strong market data. As these computations are tedious, and they are rarely applied in weak and incomplete markets. In this paper, we tested the actual values of options of Tata Consultancy Services whose shares and options are actively traded on the NSE, and applied the Crank Nicolson central difference method for estimating the path of the option pricing with five exercise prices, two out of money, at the money, and two in the money contracts of call and put options at three volatilities. The actual option values were compared with the forecasted option values and the errors were estimated. The plots of actual option values and the forecasted values produced by the central difference method converged excellently, producing minimum sums of squared error. Even in the weak and incomplete markets, the Crank Nicolson method worked well in producing the price path of options. This algorithm will be useful for hedging decisions and also for accurate forecasting and accounting reporting.Downloads
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Ameur, H. B., Breton, M., Karoui, L., & L'Ecuyer, P. (2007). A dynamic programming approach for pricing options embedded in bonds. Journal of Economic Dynamics & Control, 31 (7), 2212-2233.
Bank, R. E., Wan, J. W. L., & Qu, Z. (2005). Kernel preserving multigrid methods for convection-diffusion equations. SIAM Journal on Matrix Analysis & Applications, 27 (4), 1150-1171. DOI:10.1137/040619533
Bayraktar, E., & Xing, H. (2009). Pricing American options for jump diffusions by iterating optimal stopping problems for diffusions. Mathematical Methods of Operations Research, 70 (3), 505-525.
Boyle, P. P., & Tian, Y. (1998). An explicit finite difference approach to the pricing of barrier options. Applied Mathematical Finance, 5 (1), 17-43. DOI:10.1080/135048698334718
Boyle, P., & Potapchik, A. (2008). Prices and sensitivities of Asian options: A survey. Insurance : Mathematics & Economics, 42 (1), 189-211. doi:10.1016/j.insmatheco.2007.02.003
Clarke, N., & Parrott, K. (1999). Multigrid for American option pricing with stochastic volatility. Applied Mathematical Finance, 6 (3), 177-195. DOI:10.1080/135048699334528
Curien, E. D., Jean-Noel, M., & Stephane. (2003). Stable pricing methods for hybrid structures. Risk, 16 (12), 12-29.
d'Halluin, Y., Forsyth, P.A., Vetzal, K.R., Labahn, G. (2001). A numerical PDE approach for pricing callable bonds. Applied Mathematical Finance, 8 (1), 49-77.
Düring, B., Fournié, M., & Jüngel, A. (2003). High order compact finite difference schemes for a nonlinear Black-Scholes equation. International Journal of Theoretical & Applied Finance, 6 (7), 767-781. DOI: 10.1142/S0219024903002183
Dyrting, S. (2004). Pricing equity options everywhere. Quantitative Finance, 4 (6), 663-676. DOI:10.1080/14697680500039142
Ehrhardt, M., & Mickens, R. E. (2008). a fast, stable and accurate numerical method for the Black–Scholes equation of American options. International Journal of Theoretical & Applied Finance, 11 (5), 471-501.
Gilli, M., Këllezi, E., & Pauletto, G. (2002). Solving finite difference schemes arising in trivariate option pricing. Journal of Economic Dynamics & Control, 26 (9/10), 1499-1524.
International Accounting Standards Board. (2003). IAS 39 financial instruments: Recognition and measurement. Retrieved from http://www.iasplus.com/en/standards
Jayakumar, G. S., David. S., John, T.B., & Dawood, A.S. (2012). Weak form efficiency : Indian stock market. SCMS Journal of Indian Management, 9 (4), 80-95.
Jiwu, S., Yonggeng, G., Xiaotie, D., & Weimin, Z. (2005). A sliced-finite difference method for the American option. IIE Transactions, 37 (10), 939-944. DOI:10.1080/07408170591007849
Kudryavtsev, O., & LevendorskiiÄ, S. (2009). Fast and accurate pricing of barrier options under Lévy processes. Finance & Stochastics, 13 (4), 531-562.
Kwok, Y. - K., & Lau, K. - W. (2001a). Accuracy and reliability considerations of option pricing algorithms. Journal of Futures Markets, 21(10), 875-903. DOI: 10.1002/fut.2001
Kwok, Y. - K., & Lau, K. - W. (2001b). Pricing algorithms for options with exotic path-dependence. Journal of Derivatives, 9 (1), 28-38.
Mallikarjunappa, T., & Dsouza, J. J. (2013). A study of semi-strong form of market efficiency of Indian stock market. Amity Global Business Review, 8, 60-68.
Nagaraj, R. (1996). India's capital market growth - Trends, explanations and evidence. Economic and Political Weekly, 31( 35 -36 - 37), 2553-2563.
Shu, J., Gu, Y., & Zheng, W. (2002). A novel numerical approach of computing American option. International Journal of Foundations of Computer Science, 13 (5), 685-693.
Wang, G., & Wang, S. (2006). On stability and convergence of a finite difference approximation to a parabolic variational inequality arising from American option valuation. Stochastic Analysis & Applications, 24 (6), 1185-1204. DOI:10.1080/07362990600958952
Zhang, X. L. (1997). Numerical analysis of American option pricing in a jump-diffusion model. Mathematics of Operations Research, 22 (3), 668-690.