In this thesis, we propose architecture for a SaaS model in Cloud that would provide service to the financial investors who are not familiar with various mathematical models. Such finance models are used to evaluate financial instruments, for example, to price a derivative that is currently being traded before entering into a contact. An investor may approach CSP to price a particular derivative and specify the time, budget and accuracy constraints. Based on these constraints specified by investors, the service provider will compute the option value using our proposed FSM. To evaluate our proposed model, we compared pricing results with the classical model that provides a closed-form solution for option pricing to meet the accuracy constraints. After establishing the accuracy of our pricing results, we further ensured that the SLA between the FSP and the investors is honoured by meeting the constraints put forth by the investor who uses the Cloud service.

In this thesis, we propose architecture for a SaaS model in Cloud that would provide service to the financial investors who are not familiar with various mathematical models. Such finance models are used to evaluate financial instruments, for example, to price a derivative that is currently being traded before entering into a contact. An investor may approach CSP to price a particular derivative and specify the time, budget and accuracy constraints. Based on these constraints specified by investors, the service provider will compute the option value using our proposed FSM. To evaluate our proposed model, we compared pricing results with the classical model that provides a closed-form solution for option pricing to meet the accuracy constraints. After establishing the accuracy of our pricing results, we further ensured that the SLA between the FSP and the investors is honoured by meeting the constraints put forth by the investor who uses the Cloud service.