Summary of Distributionally Robust Optimization As a Scalable Framework to Characterize Extreme Value Distributions, by Patrick Kuiper et al.
Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions
by Patrick Kuiper, Ali Hasan, Wenhao Yang, Yuting Ng, Hoda Bidkhori, Jose Blanchet, Vahid Tarokh
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Risk Management (q-fin.RM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents distributionally robust optimization (DRO) estimators for multidimensional Extreme Value Theory (EVT) statistics, aiming to mitigate over-conservative estimates while enhancing out-of-sample performance. It focuses on semi-parametric max-stable distributions built from spatial Poisson point processes, which are powerful but only asymptotically valid for large samples. To address potential model misspecification error inherent in these applications, the authors study DRO estimators informed by semi-parametric max-stable constraints in the space of point processes. Both tractable convex formulations and neural network-based estimators are validated using synthetically generated data and a real dataset of financial returns. The proposed method is demonstrated to be a novel formulation in the multivariate EVT domain, offering improved performance compared to relevant alternate proposals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make statistics about extreme events more reliable by creating new ways to analyze them. Extreme Value Theory (EVT) helps us understand rare but important events like financial crises or natural disasters. But existing methods only work well when we have a lot of data, which is not always the case. To fix this problem, the authors create special types of statistics that are more robust and can handle uncertainty better. They test these new statistics using fake data and real-world financial data to show they are effective. |
Keywords
» Artificial intelligence » Neural network » Optimization
