Summary of Pairwise Markov Chains For Volatility Forecasting, by Elie Azeraf
Pairwise Markov Chains for Volatility Forecasting
by Elie Azeraf
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Pairwise Markov Chain (PMC) is a probabilistic graphical model that’s been underutilized for continuous value prediction due to challenges with modeling observations. This paper introduces an algorithm that addresses this issue and enables the PMC to extend predictive models by introducing hidden states and allowing non-stationarity. The algorithm is applied to volatility forecasting, comparing it to GARCH(1,1) and feedforward neural models across various scenarios. The results show the PMC’s performance enhancements for each scenario, highlighting its value. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Pairwise Markov Chain (PMC) is a new way to predict continuous values like stock prices or weather patterns. Right now, it’s not being used as much because it’s hard to understand what’s happening when you’re observing things. This paper makes it easier to use the PMC by creating an algorithm that helps with prediction. It also shows how the PMC can make other models better just by adding a few extra steps. The PMC is tested on predicting stock market volatility and it does really well, beating out some popular methods. |
