Dynamic spatiotemporal correlation coefficient based on adaptive weight

Guoli Mo (), Chunzhi Tan, Weiguo Zhang and Xuezeng Yu
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Guoli Mo: Guangxi University
Chunzhi Tan: Guangxi University
Weiguo Zhang: South China University of Technology
Xuezeng Yu: Guangxi University

Financial Innovation, 2023, vol. 9, issue 1, 1-43

Abstract: Abstract Risk management is an important aspect of financial research because correlations among financial data are essential in evaluating portfolio risk. Among various correlations, spatiotemporal correlations involve economic entity attributes and are interrelated in space and time. Such correlations have therefore drawn increasing attention in financial risk management. However, classical correlation measurements are typically based on either time series correlations or spatial dependence; they cannot be directly applied to financial data with spatiotemporal correlations. The spatiotemporal correlation coefficient model with adaptive weight proposed in this paper can (1) address the absolute quantity, dynamic quantity, and dynamic development of financial data and (2) be used for risk grading, financial risk evaluation, and portfolio management. To verify the validity and superiority of this model, cluster analysis results and portfolio performance are compared with a classical model with time series correlation or spatial correlation, respectively. Empirical findings show that the proposed coefficient is highly effective and convenient compared to others. Overall, our method provides a highly efficient financial risk management method with valuable implications for investors and financial institutions.

Keywords: Spatiotemporal correlation; Absolute distance; Growth distance; Fluctuation distance; Adaptive weight (search for similar items in EconPapers)
JEL-codes: C31 G11 G15 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1186/s40854-022-00437-3

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