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Modelling and Estimating of VaR Through the GARCH Model

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Advanced Engineering, Technology and Applications (ICAETA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1983))

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Abstract

This study focuses on the analysis of fiscal series with time-varying conditional variance utilizing the ARIMA-GARCH with Value at Risk (VaR) model. ARIMA-GARCH can predict risk when stock variance is Heteroscedasticity. The price of the Reliance stock is analyzed for fifty months. This research indicates that the VaR is a useful technique to reduce risk exposure and perhaps avoid losses when investing in the Reliance stock. The findings show that ARIMA (0,0,0)-GARCH (1,1) has the best fit, with an Akaike information criterion (AIC) value of −5915.325, at a confidence level of 95%. The GARCH technique is used to determine the conditional variance of the residuals and contrasts it with the delta-normal method. At a 95% confidence level, the VaR is used to calculate the likelihood of losing an investment by 2.7% or more in a single day.

Supported by Manonmaniam Sundaranar University.

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Acknowledgement

The authors would like to express their gratitude to the editor and learned reviewers for their valuable comments and suggestions to improve the earlier version of this manuscript. There is no conflict of interest as declared by the authors.

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Authors and Affiliations

  1. Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, 627012, India

    K. Senthamarai Kannan & V. Parimyndhan

Authors
  1. K. Senthamarai Kannan
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  2. V. Parimyndhan
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Correspondence to V. Parimyndhan .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Alessandro Ortis

  2. Istinye University, Istanbul, Türkiye

    Alaa Ali Hameed

  3. National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Akhtar Jamil

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Financial Support and Funding

This work was financially supported by Bharat Ratna Dr. M. G. Ramachandarn Centenary Research Fellowship by Manonmaniam Sundaranar University, Tirunelveli.

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Kannan, K.S., Parimyndhan, V. (2024). Modelling and Estimating of VaR Through the GARCH Model. In: Ortis, A., Hameed, A.A., Jamil, A. (eds) Advanced Engineering, Technology and Applications. ICAETA 2023. Communications in Computer and Information Science, vol 1983. Springer, Cham. https://doi.org/10.1007/978-3-031-50920-9_25

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  • DOI: https://doi.org/10.1007/978-3-031-50920-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50919-3

  • Online ISBN: 978-3-031-50920-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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