含辅助信息的日内动量隐马尔可夫模型
文章 arXiv papers · 作者: Hugh Christensen et al.
总结
本文介绍一种用于日内动量交易的隐状态方法。该方法将嘈杂的证券收益建模为潜在动量状态生成的观测，旨在避免数字滤波器在趋势反转时可能引入的滞后。研究通过交叉验证、惩罚似然准则和基于模拟的边际似然选择来评估隐状态数量；这些方法倾向于选择两个或三个状态。在离散化的单变量高斯发射模型下，参数使用 Baum-Welch 算法和马尔可夫链蒙特卡洛估计。
该框架还通过输入输出隐马尔可夫模型纳入辅助信息。示例使用已实现波动率比率和日内季节性，并用样条捕捉预测关系。贝叶斯推断和前向算法用于预测下一期收益。本文介绍一种建模方法并报告模型选择结果，但提供的说明没有交易表现结果或详细数据背景。文中所述的发射分布假设有局限；非参数扩展和异步预测被提出作为可能的改进。
核心观点
- 使用潜在动量状态表示嘈杂日内收益背后的过程。
- 该模型旨在避免市场动量改变方向时信号出现滞后。
- 交叉验证、惩罚似然和基于模拟的选择倾向于采用两个或三个隐状态。
- 波动率比率和日内季节性可通过样条作为辅助信息纳入模型。
- 前向算法支持对下一期收益进行贝叶斯预测。
标签
全文
# Hidden Markov Models Applied To Intraday Momentum Trading With Side Information # Hidden Markov Models Applied To Intraday Momentum Trading With Side Information A Hidden Markov Model for intraday momentum trading is presented which specifies a latent momentum state responsible for generating the observed securities' noisy returns. Existing momentum trading models suffer from time-lagging caused by the delayed frequency response of digital filters. Time-lagging results in a momentum signal of the wrong sign, when the market changes trend direction. A key feature of this state space formulation, is no such lagging occurs, allowing for accurate shifts in signal sign at market change points. The number of latent states in the model is estimated using three techniques, cross validation, penalized likelihood criteria and simulation-based model selection for the marginal likelihood. All three techniques suggest either 2 or 3 hidden states. Model parameters are then found using Baum-Welch and Markov Chain Monte Carlo, whilst assuming a single (discretized) univariate Gaussian distribution for the emission matrix. Often a momentum trader will want to condition their trading signals on additional information. To reflect this, learning is also carried out in the presence of side information. Two sets of side information are considered, namely a ratio of realized volatilities and intraday seasonality. It is shown that splines can be used to capture statistically significant relationships from this information, allowing returns to be predicted. An Input Output Hidden Markov Model is used to incorporate these univariate predictive signals into the transition matrix, presenting a possible solution for dealing with the signal combination problem. Bayesian inference is then carried out to predict the securities $t+1$ return using the forward algorithm. Simple modifications to the current framework allow for a fully non-parametric model with asynchronous prediction.
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