This is a summary of links featured on Quantocracy on Wednesday, 12/13/2017. To see our most recent links, visit the Quant Mashup. Read on readers!
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Time Series Analysis for Financial Data VI ARCH and GARCH models [Auquan]In this mini series on Time Series modelling for Financial Data, so far weve used AR, MA and a combination of these models on asset prices to try and model how our asset behaves. Weve found that we were able to model certain time periods well with these models and failed at other times. This was because of volatility clustering or heteroskedasticity. In this post, we will discuss conditional
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Hierarchical clustering of Exchange-Traded Funds [Quant Dare]Clustering has already been discussed in plenty of detail, but today I would like to focus on a relatively simple but extremely modular clustering technique, hierarchical clustering, and how it could be applied to ETFs. Well also be able to review the python tools available to help us with this. Clustering Suitability First of all, ETFs are well suited for clustering, as they are each trying to
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Iron Condor Results Summary – Part 6 – IC Returns vs Initial Conditions Correlation [DTR Trading]In the last article, we looked at correlations between Iron Condor returns and Iron Condor structures / trade management. Specifically, we started with the following list of areas to investigate: Correlation between Iron Condor strategy structure / management and result metrics Which result metrics most influence equity curve shape Correlation between result metrics Correlation between initial