Rene V. MayorgaProfessor, Industrial Systems Engineeringhttp://hdl.handle.net/10294/52862019-05-24T23:27:37Z2019-05-24T23:27:37ZEstimating the Inverse Function of Compound Options Pricing Model Using Artificial Neural NetworksHasanabadi, Hamed ShafieeMayorga, Rene V.http://hdl.handle.net/10294/53202014-05-21T07:00:11Z2014-05-20T00:00:00ZEstimating the Inverse Function of Compound Options Pricing Model Using Artificial Neural Networks
Hasanabadi, Hamed Shafiee; Mayorga, Rene V.
Compound options are second order derivatives which give their holders the right for exercising over other derivatives. They are options on options. Compound options have many financial applications. Pricing methods for exotic options such as compounds are much more complex than the regular options. There are different models for pricing compound options. Simulating direct function of compound option pricing model based on the Black-Scholes model needs 7 input variables including current underlying asset price, basic option strike price, the time to expiration of the basic option, the volatility of the underlying asset price, the risk-free interest rate, compound option strike price, and time to expiration of the compound option.
2014-05-20T00:00:00ZA NOVEL METHOD FOR ESTIMATING THE INVERSE FUNCTION OF BLACK-SCHOLES OPTION PRICING MODEL USING ARTIFICIAL NEURAL NETWORKSHasanabadi, Hamed ShafieeMayorga, Rene V.http://hdl.handle.net/10294/53192014-05-21T07:00:11Z2014-05-20T00:00:00ZA NOVEL METHOD FOR ESTIMATING THE INVERSE FUNCTION OF BLACK-SCHOLES OPTION PRICING MODEL USING ARTIFICIAL NEURAL NETWORKS
Hasanabadi, Hamed Shafiee; Mayorga, Rene V.
Black-Scholes (BS) model is a well-known model for pricing options. Option is a derivative
financial instrument which gives its owner the right of buying the underlying asset at a pre
specified date for a pre specified price. BS model calculates the option price using 5 input
variables and parameters including current underlying price, strike price, time to maturity,
interest rate and the volatility of the underlying asset price.
2014-05-20T00:00:00ZFORECASTING RETURN VOLATILITY OF CRUDE OIL FUTURE PRICES USING ARTIFICIAL NEURAL NETWORKS; BASED ON INTRA MARKETS VARIABLES AND FOCUS ON THE SPECULATION ACTIVITYHasanabadi, Hamed ShafieeKhan, SaqibMayorga, Rene V.http://hdl.handle.net/10294/53182014-05-21T07:00:10Z2014-05-15T00:00:00ZFORECASTING RETURN VOLATILITY OF CRUDE OIL FUTURE PRICES USING ARTIFICIAL NEURAL NETWORKS; BASED ON INTRA MARKETS VARIABLES AND FOCUS ON THE SPECULATION ACTIVITY
Hasanabadi, Hamed Shafiee; Khan, Saqib; Mayorga, Rene V.
Considering the strong linkages between commodity and equity markets during the few past years, the motivation of the study in this Chapter is to forecast the crude oil future prices return volatilities of based on the information from the intra markets variables.
2014-05-15T00:00:00Z