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Short-term Kullback–Leibler divergence analysis to extract unstable periods in financial time series

Author

Listed:
  • Ryuji Ishizaki

    (Fukuoka Prefectural University)

  • Masayoshi Inoue

    (Professor Emeritus of Kagoshima University)

Abstract

A new method is presented for estimating a short-term Kullback-Leibler divergence to analyze the statistical characteristics of significant fluctuations in financial time series. The short-term Kullback–Leibler divergence is derived by transforming variations in financial time series into binary symbolic dynamics. This method quantifies the extent to which the occurrence of significant fluctuations deviates from independent Bernoulli trials in short-term financial time series. The study presents the calculation results of short-term Kullback–Leibler divergence for the USD/JPY exchange rate and the Nikkei 225 Index. The proposed technique serves as a valuable tool for analyzing and characterizing rapid changes in financial dynamics, with potential applications in advancing the analysis of financial market behaviors and trends. Its applications extend to risk assessment and decision-making processes.

Suggested Citation

  • Ryuji Ishizaki & Masayoshi Inoue, 2024. "Short-term Kullback–Leibler divergence analysis to extract unstable periods in financial time series," Evolutionary and Institutional Economics Review, Springer, vol. 21(2), pages 227-236, September.
    • Handle: RePEc:spr:eaiere:v:21:y:2024:i:2:d:10.1007_s40844-024-00284-0
    DOI: 10.1007/s40844-024-00284-0
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    References listed on IDEAS

    as
    1. Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2016. "Time-dependent scaling patterns in high frequency financial data," LSE Research Online Documents on Economics 68645, London School of Economics and Political Science, LSE Library.
    2. Ishizaki, Ryuji & Inoue, Masayoshi, 2018. "Time-series analysis of multiple foreign exchange rates using time-dependent pattern entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 967-974.
    3. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa & de Oliveira, Wilson & Stosic, Tatijana, 2016. "Foreign exchange rate entropy evolution during financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 233-239.
    4. Lahmiri, Salim & Bekiros, Stelios, 2020. "Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Ishizaki, Ryuji & Inoue, Masayoshi, 2013. "Time-series analysis of foreign exchange rates using time-dependent pattern entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3344-3350.
    6. Ishizaki, Ryuji & Inoue, Masayoshi, 2020. "Analysis of local and global instability in foreign exchange rates using short-term information entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
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    Cited by:

    1. Mieko Tanaka-Yamawaki, 2024. "Special issue: Data-driven mathematical sciences and econophysics," Evolutionary and Institutional Economics Review, Springer, vol. 21(2), pages 199-201, September.

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    More about this item

    Keywords

    Kullback–Leibler divergence; Financial time series; Binary symbolic dynamics;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • F30 - International Economics - - International Finance - - - General
    • G00 - Financial Economics - - General - - - General

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