Summary of Granger Causality in Extremes, by Juraj Bodik and Olivier C. Pasche
Granger Causality in Extremes
by Juraj Bodik, Olivier C. Pasche
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel mathematical framework for Granger causality in extremes is introduced, designed to identify causal links from extreme events in time series. The framework leverages the causal tail coefficient to infer causality mainly from extreme events, which is particularly important during highly volatile periods. The paper establishes equivalences between causality in extremes and other causal concepts, including classical Granger causality, Sims causality, and structural causality. The authors also propose a novel inference method for detecting the presence of Granger causality in extremes from data, which is model-free, can handle non-linear and high-dimensional time series, outperforms current state-of-the-art methods in all considered setups, and was found to uncover coherent effects when applied to financial and extreme weather observations. This framework has potential applications in various fields, including finance and meteorology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how things are connected during unusual events is developed. Imagine trying to figure out what causes a big storm or a sudden stock market crash. The current methods for doing this focus on the normal patterns, but miss important connections that happen only when things get extreme. This paper introduces a new approach that looks specifically at these extreme events and can identify the underlying causes. It’s like looking at a map to see where the storm is coming from, rather than just tracking its path. |
Keywords
» Artificial intelligence » Inference » Time series » Tracking
