Volatility-Volume Order Slicing via Statistical Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913614824210432 |
|---|---|
| author | Chattopadhyay, Ritwika Malichkar, Abhishek Ren, Zhixuan Zhang, Xinyue |
| author_facet | Chattopadhyay, Ritwika Malichkar, Abhishek Ren, Zhixuan Zhang, Xinyue |
| contents | This paper addresses the challenges faced in large-volume trading, where executing substantial orders can result in significant market impact and slippage. To mitigate these effects, this study proposes a volatility-volume-based order slicing strategy that leverages Exponential Weighted Moving Average and Markov Chain Monte Carlo simulations. These methods are used to dynamically estimate future trading volumes and price ranges, enabling traders to adapt their strategies by segmenting order execution sizes based on these predictions. Results show that the proposed approach improves trade execution efficiency, reduces market impact, and offers a more adaptive solution for volatile market conditions. The findings have practical implications for large-volume trading, providing a foundation for further research into adaptive execution strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12482 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Volatility-Volume Order Slicing via Statistical Analysis Chattopadhyay, Ritwika Malichkar, Abhishek Ren, Zhixuan Zhang, Xinyue Computational Finance This paper addresses the challenges faced in large-volume trading, where executing substantial orders can result in significant market impact and slippage. To mitigate these effects, this study proposes a volatility-volume-based order slicing strategy that leverages Exponential Weighted Moving Average and Markov Chain Monte Carlo simulations. These methods are used to dynamically estimate future trading volumes and price ranges, enabling traders to adapt their strategies by segmenting order execution sizes based on these predictions. Results show that the proposed approach improves trade execution efficiency, reduces market impact, and offers a more adaptive solution for volatile market conditions. The findings have practical implications for large-volume trading, providing a foundation for further research into adaptive execution strategies. |
| title | Volatility-Volume Order Slicing via Statistical Analysis |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2412.12482 |