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We introduce a data driven and model free approach for computing conditional expectations. The new method is based on classical techniques combined with machine learning methods. In particular, we consider kernel density estimation based on simulated risk factors combined with a control variate....
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The illiquidity of long-maturity options has made it difficult to study the term structures of option spanning …
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precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use …
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distributions, which are instead obtained by transforming the options' implied risk-neutral distributions according to the posited …
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