• Corpus ID: 246063638

Lead-lag detection and network clustering for multivariate time series with an application to the US equity market

  title={Lead-lag detection and network clustering for multivariate time series with an application to the US equity market},
  author={Stefanos Bennett and Mihai Cucuringu and Gesine Reinert},
In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution with a time delay; the result is a lead-lag structure amongst the time series variables. In this paper, we propose a method for the detection of lead-lag clusters of time series in multivariate systems. We demonstrate that the web of pairwise lead-lag relationships between time series can be helpfully construed as a… 

Graph similarity learning for change-point detection in dynamic networks

This work designs a method to perform online network change-point detection that can adapt to the network domain and localise changes with no delay and requires a shorter data history to detect changes than most existing state-of-the-art baselines.

DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluation

Three adaptive time series learning methods are introduced, called Dynamic Model Selection (DMS), Adaptive Ensemble (AE), and Dynamic Asset Alloca- tion (DAA), which handle model selection, ensembling, and contextual evaluation in financial time series.

DIGRAC: Digraph Clustering Based on Flow Imbalance

DIGRAC optimizes directed flow imbalance for clustering without requiring label supervision, like existing GNN methods, and can naturally incorporate node features, unlike existing spectral methods.

Explaining Preferences with Shapley Values

This paper proposes PREF-SHAP, a Shapley value-based model explanation framework for pairwise comparison data, which derives the appropriate value functions for preference models and extends the framework to model and explain context specific information, such as the surface type in a tennis game.



Emergence of Statistically Validated Financial Intraday Lead-Lag Relationships

According to the leading models in modern finance, the presence of intraday lead-lag relationships between financial assets is negligible in efficient markets. With the advance of technology,

Emergence and temporal structure of Lead–Lag correlations in collective stock dynamics

Detecting Leaders from Correlated Time Series

An efficient algorithm is proposed which is able to track the lagged correlation and compute the leaders incrementally, while still achieving good accuracy, and the detected leaders demonstrate high predictive power on the event of general time series entities, which can enlighten both climate monitoring and financial risk control.

Information-theoretic approach to lead-lag effect on financial markets

Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both types of

How Lead-Lag Correlations Affect the Intraday Pattern of Collective Stock Dynamics

The degree of correlation among stock returns affects the possibility to diversify the risk of investment, and it plays a major role in financial spillover. During the last decade, the increasing

Lead–lag relationships in foreign exchange markets

A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets

This document is a preliminary version of an in-depth review on the state of the art of clustering financial time series and the study of correlation networks and will form a basis for implementation of an open toolbox of standard tools to study correlations, hierarchies, networks and clustering in financial markets.

High Frequency Lead/lag Relationships Empirical facts

Granger Causality: A Review and Recent Advances

  • A. ShojaieE. Fox
  • Computer Science
    Annual Review of Statistics and Its Application
  • 2021
Recent advances that address various shortcomings of the earlier approaches are discussed, from models for high-dimensional time series to more recent developments that account for nonlinear and non-Gaussian observations and allow for subsampled and mixed-frequency time series.