Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems

Omoyeni Ogundipe

doi:doi:10.11648/j.ijber.20231202.12

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Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems

Omoyeni Ogundipe

Published in International Journal of Business and Economics Research (Volume 12, Issue 2)

Received: 4 February 2023 Accepted: 27 February 2023 Published: 9 March 2023

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Abstract

This paper explores the application of statistical inference using entropy to characterize the transfer of data across financial systems. The transfer of data between financial systems is a critical process that affects the accuracy and reliability of financial information. In this study, we propose a new approach to quantify the amount of information transferred between two financial systems based on entropy measures. Specifically, we use Transfer entropy to measure the information content of data transferred between financial systems. The Kullback-Leibler separation of conditional probabilities is a design statistic known as Transfer entropy. This method enables the determination, measurement, and testing of information transmission without being limited to linear movements. We demonstrate the effectiveness of our approach by analyzing using this empirical technique, the importance of the borrowing exchange industry in comparison to the exchanges for debt securities. In addition to this, this paper investigates the greater connectivity that exists among operational risks, as represented, respectively, by iTraxx and VIX Europe. We show that our entropy-based approach can detect changes in the information content of data transferred between the two exchanges, which can be indicative of changes in market conditions or the behavior of market participants. Overall, this study highlights the potential of entropy-based statistical inference methods for characterizing the transfer of data across financial systems. This approach has the potential to provide valuable insights into the behavior of financial markets and the efficiency of financial systems, which can inform policy decisions and improve the accuracy and reliability of financial information.

Published in	International Journal of Business and Economics Research (Volume 12, Issue 2)
DOI	10.11648/j.ijber.20231202.12
Page(s)	54-60
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Transfer Entropy, The Kullback-Leibler Divergence, iTraxx, VIX

References

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Omoyeni Ogundipe. (2023). Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems. International Journal of Business and Economics Research, 12(2), 54-60. https://doi.org/10.11648/j.ijber.20231202.12

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ACS Style

Omoyeni Ogundipe. Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems. Int. J. Bus. Econ. Res. 2023, 12(2), 54-60. doi: 10.11648/j.ijber.20231202.12

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AMA Style

Omoyeni Ogundipe. Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems. Int J Bus Econ Res. 2023;12(2):54-60. doi: 10.11648/j.ijber.20231202.12

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@article{10.11648/j.ijber.20231202.12,
  author = {Omoyeni Ogundipe},
  title = {Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems},
  journal = {International Journal of Business and Economics Research},
  volume = {12},
  number = {2},
  pages = {54-60},
  doi = {10.11648/j.ijber.20231202.12},
  url = {https://doi.org/10.11648/j.ijber.20231202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20231202.12},
  abstract = {This paper explores the application of statistical inference using entropy to characterize the transfer of data across financial systems. The transfer of data between financial systems is a critical process that affects the accuracy and reliability of financial information. In this study, we propose a new approach to quantify the amount of information transferred between two financial systems based on entropy measures. Specifically, we use Transfer entropy to measure the information content of data transferred between financial systems. The Kullback-Leibler separation of conditional probabilities is a design statistic known as Transfer entropy. This method enables the determination, measurement, and testing of information transmission without being limited to linear movements. We demonstrate the effectiveness of our approach by analyzing using this empirical technique, the importance of the borrowing exchange industry in comparison to the exchanges for debt securities. In addition to this, this paper investigates the greater connectivity that exists among operational risks, as represented, respectively, by iTraxx and VIX Europe. We show that our entropy-based approach can detect changes in the information content of data transferred between the two exchanges, which can be indicative of changes in market conditions or the behavior of market participants. Overall, this study highlights the potential of entropy-based statistical inference methods for characterizing the transfer of data across financial systems. This approach has the potential to provide valuable insights into the behavior of financial markets and the efficiency of financial systems, which can inform policy decisions and improve the accuracy and reliability of financial information.},
 year = {2023}
}

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TY  - JOUR
T1  - Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems
AU  - Omoyeni Ogundipe
Y1  - 2023/03/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ijber.20231202.12
DO  - 10.11648/j.ijber.20231202.12
T2  - International Journal of Business and Economics Research
JF  - International Journal of Business and Economics Research
JO  - International Journal of Business and Economics Research
SP  - 54
EP  - 60
PB  - Science Publishing Group
SN  - 2328-756X
UR  - https://doi.org/10.11648/j.ijber.20231202.12
AB  - This paper explores the application of statistical inference using entropy to characterize the transfer of data across financial systems. The transfer of data between financial systems is a critical process that affects the accuracy and reliability of financial information. In this study, we propose a new approach to quantify the amount of information transferred between two financial systems based on entropy measures. Specifically, we use Transfer entropy to measure the information content of data transferred between financial systems. The Kullback-Leibler separation of conditional probabilities is a design statistic known as Transfer entropy. This method enables the determination, measurement, and testing of information transmission without being limited to linear movements. We demonstrate the effectiveness of our approach by analyzing using this empirical technique, the importance of the borrowing exchange industry in comparison to the exchanges for debt securities. In addition to this, this paper investigates the greater connectivity that exists among operational risks, as represented, respectively, by iTraxx and VIX Europe. We show that our entropy-based approach can detect changes in the information content of data transferred between the two exchanges, which can be indicative of changes in market conditions or the behavior of market participants. Overall, this study highlights the potential of entropy-based statistical inference methods for characterizing the transfer of data across financial systems. This approach has the potential to provide valuable insights into the behavior of financial markets and the efficiency of financial systems, which can inform policy decisions and improve the accuracy and reliability of financial information.
VL  - 12
IS  - 2
ER  -

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Author Information

Omoyeni Ogundipe

Department of Mathematics and Statistics, Austin Peay State University, Clarksville, USA

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Plain Text BibTeX RIS

APA Style

Omoyeni Ogundipe. (2023). Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems. International Journal of Business and Economics Research, 12(2), 54-60. https://doi.org/10.11648/j.ijber.20231202.12

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ACS Style

Omoyeni Ogundipe. Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems. Int. J. Bus. Econ. Res. 2023, 12(2), 54-60. doi: 10.11648/j.ijber.20231202.12

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AMA Style

Omoyeni Ogundipe. Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems. Int J Bus Econ Res. 2023;12(2):54-60. doi: 10.11648/j.ijber.20231202.12

Copy | Download

@article{10.11648/j.ijber.20231202.12,
  author = {Omoyeni Ogundipe},
  title = {Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems},
  journal = {International Journal of Business and Economics Research},
  volume = {12},
  number = {2},
  pages = {54-60},
  doi = {10.11648/j.ijber.20231202.12},
  url = {https://doi.org/10.11648/j.ijber.20231202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20231202.12},
  abstract = {This paper explores the application of statistical inference using entropy to characterize the transfer of data across financial systems. The transfer of data between financial systems is a critical process that affects the accuracy and reliability of financial information. In this study, we propose a new approach to quantify the amount of information transferred between two financial systems based on entropy measures. Specifically, we use Transfer entropy to measure the information content of data transferred between financial systems. The Kullback-Leibler separation of conditional probabilities is a design statistic known as Transfer entropy. This method enables the determination, measurement, and testing of information transmission without being limited to linear movements. We demonstrate the effectiveness of our approach by analyzing using this empirical technique, the importance of the borrowing exchange industry in comparison to the exchanges for debt securities. In addition to this, this paper investigates the greater connectivity that exists among operational risks, as represented, respectively, by iTraxx and VIX Europe. We show that our entropy-based approach can detect changes in the information content of data transferred between the two exchanges, which can be indicative of changes in market conditions or the behavior of market participants. Overall, this study highlights the potential of entropy-based statistical inference methods for characterizing the transfer of data across financial systems. This approach has the potential to provide valuable insights into the behavior of financial markets and the efficiency of financial systems, which can inform policy decisions and improve the accuracy and reliability of financial information.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Application of Statistical Inference Using Entropy to Characterize the Transfer of Data Across Financial Systems
AU  - Omoyeni Ogundipe
Y1  - 2023/03/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ijber.20231202.12
DO  - 10.11648/j.ijber.20231202.12
T2  - International Journal of Business and Economics Research
JF  - International Journal of Business and Economics Research
JO  - International Journal of Business and Economics Research
SP  - 54
EP  - 60
PB  - Science Publishing Group
SN  - 2328-756X
UR  - https://doi.org/10.11648/j.ijber.20231202.12
AB  - This paper explores the application of statistical inference using entropy to characterize the transfer of data across financial systems. The transfer of data between financial systems is a critical process that affects the accuracy and reliability of financial information. In this study, we propose a new approach to quantify the amount of information transferred between two financial systems based on entropy measures. Specifically, we use Transfer entropy to measure the information content of data transferred between financial systems. The Kullback-Leibler separation of conditional probabilities is a design statistic known as Transfer entropy. This method enables the determination, measurement, and testing of information transmission without being limited to linear movements. We demonstrate the effectiveness of our approach by analyzing using this empirical technique, the importance of the borrowing exchange industry in comparison to the exchanges for debt securities. In addition to this, this paper investigates the greater connectivity that exists among operational risks, as represented, respectively, by iTraxx and VIX Europe. We show that our entropy-based approach can detect changes in the information content of data transferred between the two exchanges, which can be indicative of changes in market conditions or the behavior of market participants. Overall, this study highlights the potential of entropy-based statistical inference methods for characterizing the transfer of data across financial systems. This approach has the potential to provide valuable insights into the behavior of financial markets and the efficiency of financial systems, which can inform policy decisions and improve the accuracy and reliability of financial information.
VL  - 12
IS  - 2
ER  -

Copy | Download