This is a summary of links featured on Quantocracy on Saturday, 08/11/2018. To see our most recent links, visit the Quant Mashup. Read on readers!
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Optimal Portfolio Construction Using Machine Learning [Quant Insti]In this post, we will learn about the Stereoscopic Portfolio Optimization framework and how it can be used to improve a quantitative trading strategy. Well also review concepts such as Gaussian Mixture Models, K-Means Clustering, and Random Forests. Our objective is to determine whether we can reject the null hypothesis that the SPO model is not a viable option for creating optimal short-term
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Endogenous market risk [SR SV]Understanding endogenous market risk (setback risk) is critical for timing and risk management of strategic macro trades. Endogenous market risk here means a gap between downside and upside risk to the mark-to-market value that is unrelated to a trades fundamental value proposition. Rather this specific downside skew arises from the markets internal dynamics and indicates the
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The Lognormal Mixture Variance Model [Jonathan Kinlay]The LNVM model is a mixture of lognormal models and the model density is a linear combination of the underlying densities, for instance, log-normal densities. The resulting density of this mixture is no longer log-normal and the model can thereby better fit skew and smile observed in the market. The model is becoming increasingly widely used for interest rate/commodity hybrids. SSALGOTRADING AD In