Advanced SearchSearch Tips
Option Pricing and Sensitivity Evaluation Methodology: Improvement of Speed and Accuracy
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Option Pricing and Sensitivity Evaluation Methodology: Improvement of Speed and Accuracy
Choi, Young-Soo; Oh, Se-Jin; Lee, Won-Chang;
  PDF(new window)
This paper presents how to improve the efficiency and accuracy in the pricing and sensitivity evaluation for derivatives, since the need for the evaluation of complicated derivatives is increased. The Monte Carlo(MC) simulation using the quasi random number instead of pseudo random number can improve the elapsed time and accuracy for the valuation of European-type derivatives. However, the quasi MC simulation method has its limit for applying it in the multi-dimensional case such as American-type and path-dependent options due to the increased correlation between dimensions as the dimension of random numbers is increased. In order to complement this problem, we develop a modified method in which correlation values are controlled to be below a pre-specified value. Thus, this method is applicable for the pricing of either derivatives ill which underlying assets or risk factors are several or derivatives having path-dependent or early redemption property. Furthermore, we illustrate that it is important to take an appropriate grid interval for the use of finite difference method(FDM) by applying the FDM to one example of non-symmetrical butterfly spreads.
Option pricing;finite difference method;Monte Carlo simulation;Quasi random number;American put option;
 Cited by
Boyle, P. P. (1977). Options: A Monte Carlo approach, Journal of Financial Economics, 4, 323-338 crossref(new window)

Boyle P., Broadie, M. and Glasserman, P. (1997). Monte Carlo methods for security pricing, Journal of Economic Dynamics and Control, 21, 1267-1321 crossref(new window)

Brennan, M. J. and Schwartz, E. S. (1977). The valuation of American put options, Journal of Finance, 32, 449-462 crossref(new window)

Cox, J. C., Ross, S. A. and Rubinstein, M. (1979). Option pricing: A simplified approach, Journal of Financial Economics, 3, 229-63

Jarrow, R. and A. Rudd. (1983). Option Pricing, Homewood, IL: R. D. Irwin

Joe, S. and Kuo, F. Y. (2003). Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator, ACM Transactions on Mathematical Software, 29, 49-57 crossref(new window)

Leisen, D. P. J. and Reimer, M. (1996). Binomial models for option valuation examining and improving convergence, Applied Mathematical Finance, 3, 319-346 crossref(new window)

Longstaff, F. A. and Schwartz, E. S. (2001). Valuing American options by simulations: A simple least-squares approach, Review of Financial Studies, 14, 113-147 crossref(new window)

Tavella, D. and C. Randall (2000). Pricing Financial Instruments: The Finite Difference Method, John Wiley & Son, New York

Tian. Y. (1993). A modified lattice approach to option pricing, Journal of Futures Markets, 13, 563-577 crossref(new window)