用隐马尔可夫链改进波动率预测，提升模型适应市场变化能力。
Pairwise Markov Chains for Volatility Forecasting
- 引入新算法让隐马尔可夫链直接处理连续变量预测
- 在多组资产上优于GARCH(1,1)和前馈神经网络
- 适合需要捕捉市场状态切换的金融时间序列建模
成对马尔可夫链（PMC）是一种扩展自经典隐马尔可夫模型的概率图模型。尽管该模型在众多任务中表现优异，却极少用于连续值预测，主要受限于生成模型对观测值的建模难题。本文提出一种新算法，使PMC可绕过特征工程问题，充分发挥其潜力。该算法允许在任意预测模型中引入随时间更新的隐藏状态，从而实现非平稳性建模。我们使用该方法进行波动率预测，并与广泛应用的GARCH(1,1)和前馈神经网络模型在多个资产对上进行比较。考虑到波动率中存在的制度转换现象，实验表明，该方法能持续提升所扩展模型的性能，验证了其有效性。
原文摘要 · Abstract (English)
The Pairwise Markov Chain (PMC) is a probabilistic graphical model extending the well-known Hidden Markov Model. This model, although highly effective for many tasks, has been scarcely utilized for continuous value prediction. This is mainly due to the issue of modeling observations inherent in generative probabilistic models. In this paper, we introduce a new algorithm for prediction with the PMC. On the one hand, this algorithm allows circumventing the feature problem, thus fully exploiting the capabilities of the PMC. On the other hand, it enables the PMC to extend any predictive model by introducing hidden states, updated at each time step, and allowing the introduction of non-stationarity for any model. We apply the PMC with its new algorithm for volatility forecasting, which we compare to the highly popular GARCH(1,1) and feedforward neural models across numerous pairs. This is particularly relevant given the regime changes that we can observe in volatility. For each scenario, our algorithm enhances the performance of the extended model, demonstrating the value of our approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。