Abstract
Most option pricing methods use mathematical distributions to approximate underlying asset behavior. However, pure mathematical distribution approaches have difficulty approximating the real distribution. This study first introduces an innovative computational method for pricing European options based on the real payoff distribution of the underlying asset. This computational approach can also be applied to applications related to expected value that require real distributions rather than mathematical distributions. This study makes the following contributions: (a) solving the risk neutral issue related to price options with real payoff distributions; (b) proposing a simple method for adjusting standard deviation based on the need to apply short term volatility to real world applications; (c) demonstrating an option pricing algorithm that is easy to apply to cross field applications.
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References
Amin K, Jarrow R (1992) Pricing options on risky assets in a stochastic interest rate economy. Math Financ 2: 217–237
Bates DS (1991) The crash of ’87: was it expected? The evidence from options markets. J Financ 46:1009–1044
Bates D (1996) Jumps and stochastic volatility: exchange rate processes implicit in Deutsch mark options. Review of financial studies 9:69–107
Bates DS (1996) Jumps and stochastic volatility: exchange rate processes implicit in Deutsche mark options. Rev Financ Stud 9:69–107
Benaroch M (2002) Managing information technology investment risk: a real options perspective. J Manage Inform Syst 19(2):43–84
Black F, Scholes M (1973) The pricing of options and corporate liabilities. J Polit Econ 81(3):637–59
Carverhill AP, Cheuk THF (2003) Alternative neural network approach for option pricing and hedging. Available at SSRN: http://ssrn.com/abstract=480562 or DOI: 10.2139/ssrn.480562
Clemons EK (1991) Evaluating strategic investment in information system. Commun ACM 34(1):22–36
Cox JC, Ross SA (1976) The valuation of options for alternative stochastic processes. J Financ Econ 3: 145–166
Dos Santos BL (1991) Justifying Investment in new information technologies. J Manage Inform Syst 7(4):71–89
Hutchinson JM, Lo AW, Poggio T (1994) A nonparametric approach to pricing and hedging derivatives Securities Via Learning Networks. J Finance 49:851–889
Madan DB, Peter C, Eric CC (1998) The variance gamma process and options pricing. Euro Finance Rev 2(1):79–105
Meissner G, Kawano N (2001) Capturing the volatility smile of options on high-tech stocks: a combined GARCH-Neural network approach. J Econ Financ 25:276–292
Merton R (1973) Theory of rational option pricing. Bell J Econ 4: 141–183
Rubinstein M (1994) Implied binomial trees. J Financ 49:771–818
Scott LO (1997) Pricing stock options in a jump-diffusion model with stochastic volatility and interest rates: applications of fourier inversion methods. Mathe Financ 7: 345–358
Stoll HR (1969) The relationship between put and call option prices. J Finance 23: 801–824
Yacine A-S, Andrew WL (1996) Nonparametric estimation of state-price densities implicit in financial asset prices. J Finance 52: 499–548
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Sheng, CC., Chiu, HY. & Chen, AP. Using computational methodology to price European options with actual payoff distributions. Soft Comput 11, 1115–1122 (2007). https://doi.org/10.1007/s00500-007-0154-2
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DOI: https://doi.org/10.1007/s00500-007-0154-2