Summary of Non-parametric Estimation Of Multi-dimensional Marked Hawkes Processes, by Sobin Joseph and Shashi Jain
Non-Parametric Estimation of Multi-dimensional Marked Hawkes Processes
by Sobin Joseph, Shashi Jain
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computational Finance (q-fin.CP); Statistical Finance (q-fin.ST)
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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 This research proposes a novel methodology for estimating the conditional intensity of the marked Hawkes process, an extension of the Hawkes process that features variable jump size across each event. The authors introduce two distinct models: Shallow Neural Hawkes with marks and Neural Network for Non-Linear Hawkes with Marks. These approaches take past arrival times and their corresponding marks as input to obtain the arrival intensity, preserving the interpretability associated with the marked Hawkes process. The method is validated through synthetic datasets with known ground truth and applied to model cryptocurrency order book data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to understand and analyze events that happen at varying rates. It’s like trying to predict when someone will send an email – it’s not just about the number of emails sent, but also how often they’re sent and why. The researchers created two special models to help with this task. They tested these models on fake data and real-world examples from cryptocurrency markets. |
Keywords
* Artificial intelligence * Neural network
