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We investigated term structure models for commodity prices to value derivative-linked securities (DLS) traded in Korea. We especially highlighted geometric Brownian motion (GBM) model considering a convenience yield and Schwartz model reflecting mean-reverting property.

One of key characteristics of the paper is that this paper provides theoretical models for multi underlying assets and the model combining GBM model and Schwartz model. Furthermore, it gives us an analysis for quanto adjustment which occurs in the valuation of DLS. In case of GBM model, quanto adjustment seems to be relatively simple by adjusting a constant ratio to risk-free interest rate. Unlike GBM model, we find out that, in case of Schwartz model, such adjustment can be achieved only when the stochastic process of foreign exchange rate is considered.

After having valuation, both models show stable results for DLS prices using WTI index as an underlying asset. However, they results in outcomes, which are relatively not stable, on valuing DLS written on multi underlying assets including nickel.

Evolutionary operation (EVOP) is a continuous improvement system which explores a region of process operating conditions by deliberately creating some systematic changes to the process vairable levels without jeopardizing the product. It is aimed at securing a satisfactory operating condition in full‐scale manufacturing processes, which is generally different from that obtained in laboratory or pilot experiments. Information on how to improve the process is generated from a simple experiemental design. Traditional EVOP procedures are established on the assumption that the variance of the response variable should be small and stable in the region of the process operation. However, it is often the case taht process noises have an influence on the stability of the process. This process instability is due to many factors such as raw materials, ambient temperature, and equipment wear. Therefore, process variables should be optimized continously not only to meet the target value but also to keep the variance of the response variables as low as possible. We propose a scheme to achieve robust process improvement. As a process performance measure, we adopted the mean square error (MSE) of the replicate response values on a specific operating condition, and used the Kruskal‐Wallis test to identify significant differences between the process operating conditions.