Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The Computational Complexity of High-Dimensional Correlation Search
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A note on "beyond market baskets: generalizing association rules to correlations"
ACM SIGKDD Explorations Newsletter
Playing hide-and-seek with correlations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
TAPER: A Two-Step Approach for All-Strong-Pairs Correlation Query in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Short Communication: Graphing Kendall's τ
Computational Statistics & Data Analysis
On rank correlation in information retrieval evaluation
ACM SIGIR Forum
A new rank correlation coefficient for information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Volatile correlation computation: a checkpoint view
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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A critical challenge in quantitative financial risk analysis is the effective computation of volatility and correlation. However, the dynamic nature of financial data environments create the challenges for robust correlation computing, particularly when the number of financial instruments and the volume of transactions grow dramatically. To this end, in this paper, we present an organized study of rank correlation computing for financial risk analysis in dynamic environments. Specifically, we focus on Kendall's τ, which is widely recognized as a robust correlation measure for evaluating financial risk. Kendall's τ is not widely used in practice partially because its computation complexity is O(n2), making it difficult to frequently recompute in dynamic environments. After carefully studying the computational properties of Kendall's τ, we reveal that Kendall's τ is very computation-friendly for incremental computing of correlations, since the relativity of existing observations will not change as new observations come in. Based on this finding, we develop a τGrow algorithm for dynamically computing Kendall's τ. Also, even for one-time static Kendall's τ computation, we observe that the Kendall's τ correlations on smaller time pieces can provide concise summaries of how Kendall's τ evolves over the whole period. Finally, the effectiveness and the efficiency of the proposed methods have been demonstrated through the experiments on real-world financial data.