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A Study of the International Stock Market Behavior During COVID-19 Pandemic Using a Driven Iterated Function System

Author

Listed:
  • Aman Gupta

    (Manipal University Jaipur)

  • Cyril Shaju

    (Indian Institute of Technology Roorkee)

  • Pratibha

    (Indian Institute of Technology Roorkee)

  • Kamal

    (Indian Institute of Technology Roorkee
    Indian Institute of Technology Roorkee)

Abstract

We propose a novel approach to visualize and compare financial markets across the globe using chaos game representation (CGR) of iterated function systems (IFS). We modified a fractal method, widely used in life sciences, and applied it to study the effect of COVID-19 on global financial markets. This modified driven IFS approach is used to generate compact fractal portraits of the financial markets in form of percentage CGR (PC) plots and subtraction percentage (SP) plots. The markets over different periods are compared and the difference is quantified through a parameter called the proximity (Pr) index. The reaction of the financial market across the globe and volatility to the current pandemic of COVID-19 is studied and modeled successfully. The imminent bearish and a surprise bullish pattern of the financial markets across the world is revealed by this fractal method and provides a new tool to study financial markets.

Suggested Citation

  • Aman Gupta & Cyril Shaju & Pratibha & Kamal, 2023. "A Study of the International Stock Market Behavior During COVID-19 Pandemic Using a Driven Iterated Function System," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 57-68, January.
    • Handle: RePEc:kap:compec:v:61:y:2023:i:1:d:10.1007_s10614-021-10199-2
    DOI: 10.1007/s10614-021-10199-2
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    References listed on IDEAS

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    Cited by:

    1. Kaihao Liang & Shuliang Li & Wenfeng Zhang & Zhuokui Wu & Jiaying He & Mengmeng Li & Yuling Wang, 2024. "Evolution of Complex Network Topology for Chinese Listed Companies Under the COVID-19 Pandemic," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1121-1136, March.

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