Structural clustering of volatility regimes for dynamic trading strategies

• URL: http://arxiv.org/abs/2004.09963v3
• Date: Wed, 24 Nov 2021 04:29:21 GMT
• Title: Structural clustering of volatility regimes for dynamic trading strategies
• Authors: Arjun Prakash, Nick James, Max Menzies, Gilad Francis
• Abstract summary: We develop a new method to find the number of volatility regimes in a nonstationary financial time series by applying unsupervised learning to its volatility structure. We create and validate a dynamic trading strategy that learns the optimal match between the current distribution of a time series and its past regimes.
• Score: 4.129225533930966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a new method to find the number of volatility regimes in a nonstationary financial time series by applying unsupervised learning to its volatility structure. We use change point detection to partition a time series into locally stationary segments and then compute a distance matrix between segment distributions. The segments are clustered into a learned number of discrete volatility regimes via an optimization routine. Using this framework, we determine a volatility clustering structure for financial indices, large-cap equities, exchange-traded funds and currency pairs. Our method overcomes the rigid assumptions necessary to implement many parametric regime-switching models, while effectively distilling a time series into several characteristic behaviours. Our results provide significant simplification of these time series and a strong descriptive analysis of prior behaviours of volatility. Finally, we create and validate a dynamic trading strategy that learns the optimal match between the current distribution of a time series and its past regimes, thereby making online risk-avoidance decisions in the present.

Related papers

• Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings [0.0]
  patio-temporal forecasting is crucial in transportation, logistics, and supply chain management. We propose a dynamic, multi-modal approach that integrates the strengths of traditional forecasting methods and instruction tuning of small language models. Our framework enables on-premises customization with reduced computational and memory demands, while maintaining inference speed and data privacy/security.
  arXiv  Detail & Related papers  (2024-08-24T16:32:58Z)
• TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series [57.4208255711412]
  Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS) We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-02T16:45:19Z)
• Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping [0.0]
  The proliferation and ubiquity of temporal data across many disciplines has sparked interest for similarity, classification and clustering methods. Traditional distance measures such as the Euclidean are not well-suited due to the time-dependent nature of the data. This thesis proposes novel elastic alignment methods that use parametric & diffeomorphic warping transformations.
  arXiv  Detail & Related papers  (2023-09-25T10:51:47Z)
• The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting [50.48888534815361]
  We show that models trained with the Channel Independent (CI) strategy outperform those trained with the Channel Dependent (CD) strategy. Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series. We propose a modified CD method called Predict Residuals with Regularization (PRReg) that can surpass the CI strategy.
  arXiv  Detail & Related papers  (2023-04-11T13:15:33Z)
• Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies [3.351714665243138]
  We introduce Spatio-Temporal Momentum strategies, which unify both time-series and cross-sectional momentum strategies by trading assets based on their cross-sectional momentum features over time. We demonstrate that the model is able to retain its performance over benchmarks in the presence of high transaction costs. In particular, we find that the model when coupled with least absolute shrinkage and turnover regularization results in the best performance over various transaction cost scenarios.
  arXiv  Detail & Related papers  (2023-02-20T18:59:05Z)
• Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots [75.20174445166997]
  This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series. We adopt a deep learning framework for methodologically addressing the prediction of the synchronization state. We find that the task of predicting the state of synchronization of two time series is in general rather difficult, but for certain pairs of stocks attainable with very satisfactory performance.
  arXiv  Detail & Related papers  (2022-10-26T10:22:28Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture [2.580765958706854]
  We introduce the Momentum Transformer, an attention-based architecture which outperforms the benchmarks. We observe remarkable structure in the attention patterns, with significant peaks of importance at momentum turning points. Through the addition of an interpretable variable selection network, we observe how CPD helps our model to move away from trading predominantly on daily returns data.
  arXiv  Detail & Related papers  (2021-12-16T00:04:12Z)
• Deep Explicit Duration Switching Models for Time Series [84.33678003781908]
  We propose a flexible model that is capable of identifying both state- and time-dependent switching dynamics. State-dependent switching is enabled by a recurrent state-to-switch connection. An explicit duration count variable is used to improve the time-dependent switching behavior.
  arXiv  Detail & Related papers  (2021-10-26T17:35:21Z)
• Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning [35.34574502348672]
  We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic and the asymmetrical heavy-tail property of financial time series.
  arXiv  Detail & Related papers  (2020-10-16T09:35:52Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.