New IJF now accepts submissions exclusively via the Editorial Scholar portal — a dedicated submission & review system.
Go to Editorial Scholar →
Associated Management Consultants Pvt. Ltd. | COPE Member
Scopus Q2 UGC-CARE |
Indian Journal of Finance logo

Indian Journal of Finance

ISSN: 0973-8711 Frequency: Monthly Peer Review: Double-blind Published since: 2007 Language: English
A publication of AMCPL
amcon.co.in
New Delhi, India
Indexed in: Scopus Q2 UGC-CARE Group II Google Scholar J-Gate NISCAIR Crossref

Original Article

Open Access Original Article

Effectiveness of Tridiagonal Path Dependent Option Valuation in Weak Derivative Market Environment

Ravindran Ramasamy1,  Mahalakshmi Suppiah2,  Zulkifflee Mohamed3

1 Professor, Graduate School of Business, Universiti Tun Abdul Razak, Jalan Tangsi, 50480 Kuala Lumpur

2 Student, Graduate School of Business, Universiti Tun Abdul Razak, Jalan Tangsi, 50480 Kuala Lumpur

3 Associate Professor, Bank Rakyat School of Business & Entrepreneurship, Universiti Tun Abdul Razak, Jalan Tangsi, 50480 Kuala Lumpur

Volume 9
Issue 9
Pages 7–22
Year 2015
Received: Sept. 16, 2014 Accepted: April 26, 2015 Published: Sept. 1, 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.

Keywords Call and Put Options Crank-Nicolson Method Derivatives Partial Differential Equation Path Dependent
How to Cite

Ravindran Ramasamy, Mahalakshmi Suppiah, Zulkifflee Mohamed (2015). Effectiveness of Tridiagonal Path Dependent Option Valuation in Weak Derivative Market Environment. Indian Journal of Finance, 9(9), 7–22. https://doi.org/10.17010/ijf/2015/v9i9/77192

References
  1. 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.
  2. 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
  3. 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.
  4. 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
  5. 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
  6. Clarke, N., & Parrott, K. (1999). Multigrid for American option pricing with stochastic volatility. Applied Mathematical Finance, 6 (3), 177-195. DOI:10.1080/135048699334528
  7. Curien, E. D., Jean-Noel, M., & Stephane. (2003). Stable pricing methods for hybrid structures. Risk, 16 (12), 12-29.
  8. 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.
  9. 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
  10. Dyrting, S. (2004). Pricing equity options everywhere. Quantitative Finance, 4 (6), 663-676. DOI:10.1080/14697680500039142
  11. 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.
  12. 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.
  13. International Accounting Standards Board. (2003). IAS 39 financial instruments: Recognition and measurement. Retrieved from <a href=http://www.iasplus.com/en/standards target="_blank">http://www.iasplus.com/en/standards</a>
  14. 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.
  15. 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
  16. Kudryavtsev, O., & Levendorskiiĭ, S. (2009). Fast and accurate pricing of barrier options under Lévy processes. Finance & Stochastics, 13 (4), 531-562.
  17. 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
  18. Kwok, Y. - K., & Lau, K. - W. (2001b). Pricing algorithms for options with exotic path-dependence. Journal of Derivatives, 9 (1), 28-38.
  19. 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.
  20. Nagaraj, R. (1996). India's capital market growth - Trends, explanations and evidence. Economic and Political Weekly, 31( 35 -36 - 37), 2553-2563.
  21. 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.
  22. 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
  23. Zhang, X. L. (1997). Numerical analysis of American option pricing in a jump-diffusion model. Mathematics of Operations Research, 22 (3), 668-690.
Editorial Scholar

Submit your Research to IJF

Submit your Manuscript → Author Guidelines