Abstract: Nonparametric models such as machine learning methods that are counterparts of parametric financial models were applied to pricing American index options. 10 year S&P 100 Index American options were adopted as experimental dataset and the both training (in-sample) and test (out-of-sample) errors of machine learning and ad-hoc pricing which is a conventional financial pricing model were calculated and compared to each other. We found out that Bayesian neural network outperforms the other pricing methods. Furthermore, we suggested an ensemble method which takes advantage of both machine learning method and ad-hoc pricing method and as a consequence, it shows the best performance.

• Publication Date: 22-Feb-2015
• DOI: 10.15224/978-1-63248-041-5-85
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Abstract: Healthcare reforms in sustaining the best healthcare financing are the most common debating issues in both developed and developing countries. Policy maker and researchers keep analyzing and experimenting an innovative financing mechanism in order to mobilize resources for sustainable long term healthcare spending. Among other financing mechanism, Medical Saving Account (MSA) is one of the strategies being assessed in ensuring the efficiency issues especially in cost containment as well as in reducing healthcare cost. Due to failure of realizing the large-scale healthcare reform, MSA are proposed in conjunction with high-deductibles health insurance which it could be as a substitute of healthcare reforms. Due to highly subsidized health care expenditures especially in public health care facilities, some developing countries will be overwhelmed with extremely increased health care cost in the future. In other words, if this practice is not improved and monitored, the subsidy fund would

• Publication Date: 22-Feb-2015
• DOI: 10.15224/978-1-63248-041-5-83
• Views: 0
• Downloads: 0