In this paper, we present a new, simple and efficient calibration procedure that uses both the short and long-term behavior of the Heston model in a coherent fashion. Using a suitable Hull and White-type formula, we develop a methodology to obtain an approximation to the implied volatility. Using this approximation, we calibrate the full set of parameters of the Heston model. One of the reasons that makes our calibration for short times to maturity so accurate is that we take into account the term structure for large times to maturity: We may thus say that calibration is not "memoryless," in the sense that the option's behavior far away from maturity does influence calibration when the option gets close to expiration. Our results provide a way to perform a quick calibration of a closed-form approximation to vanilla option prices, which may then be used to price exotic derivatives. The methodology is simple, accurate, fast and it requires a minimal computational effort.
Since 2006, when "computational thinking" term was popularized by Jeannette Wing as a new basic skill for the twenty-first century, teaching and learning of computational thinking in K-12 education has been the focus of much research in the scientific community. This research presents a systematic review of the literature on tools used for teaching computational thinking from 2006 through 2015, in order to contribute to gaps identifying. In the results, we have not found any tool that implements Alan Turing theories and formalisms.
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