Clustering high‐frequency financial time series based on information theory-Reference-Cited by-同舟云学术

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Clustering high‐frequency financial time series based on information theory

Published:2021-09-08 Issue: Volume: Page:
ISSN:1524-1904
Container-title:Applied Stochastic Models in Business and Industry
language:en
Short-container-title:Appl Stochastic Models Bus Ind

Author:

Liu Haitao¹^ORCID,Zou Jian¹²,Ravishanker Nalini³

Affiliation:

1. Data Science Program Worcester Polytechnic Institute Worcester Massachusetts USA

2. Department of Mathematical Sciences Worcester Polytechnic Institute Worcester Massachusetts USA

3. Department of Statistics University of Connecticut Storrs Connecticut USA

Funder

National Science Foundation

Publisher

Wiley

Subject

Management Science and Operations Research,General Business, Management and Accounting,Modeling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/asmb.2644

Reference36 articles.

1. Risk and return in high‐frequency trading;Baron M;J Financ Quant Anal,2019

2. Multivariate density forecast evaluation and calibration in financial risk management: high‐frequency returns on foreign exchange;Diebold FX;Rev Econ Stat,1999

3. Evaluation of clustering algorithms for financial risk analysis using MCDM methods;Kou G;Inf Sci,2014

4. Clustering nonlinear, nonstationary time series using BSLEX;Harvill JL;Methodol Comput Appl Probab,2017

5. Clustering financial time series: an application to mutual funds style analysis;Pattarin F;Comput Stat Data Anal,2004

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2. The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?;Entropy;2022-09-19

3. The Linear Relationship Model with LASSO for Studying Stock Networks;Entropy;2022-06-09

4. Review of Statistical Approaches for Modeling High-Frequency Trading Data;Sankhya B;2022-04-28

5. Biclustering high‐frequency financial time series based on information theory;Statistical Analysis and Data Mining: The ASA Data Science Journal;2022-04

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