The Effect of the Hurst Parameter on Value at Risk Estimation in Fractional Geometric Brownian motion Price Simulation

Tendayi Matina and Edmore Mangwende
Additional contact information
Tendayi Matina: University of Zimbabwe, Mathematics and Computational Sciences Department, Harare, Zimbabwe
Edmore Mangwende: University of Zimbabwe, Mathematics and Computational Sciences Department, Harare, Zimbabwe

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 12, 1849-1857

Abstract: This study assessed the impact of the Hurst parameter on the accuracy of Value at Risk (VaR) estimation using fractional Geometric Brownian motion (fGBM) for stock price simulation. The fGBM model, known for its ability to capture long-term memory in financial time series, was employed to simulate stock prices with varying Hurst parameters. The accuracy of VaR estimations obtained from these simulations was then assessed using mean absolute error metric. The research findings revealed that the Hurst parameter significantly influences the accuracy of VaR estimation in fGBM models. The study identified 0.7 as the optimal Hurst parameter value that enhances VaR estimation accuracy, highlighting the importance of incorporating long-term memory effects in risk assessment. The insights have practical implications for investors and financial institutions seeking to enhance risk management practices. The researchers recommended further researches using different levels of Hurst parameters and VaR at different significant levels.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijriss/ ... sue-12/1849-1857.pdf (application/pdf)
https://rsisinternational.org/journals/ijriss/arti ... on-price-simulation/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcp:journl:v:8:y:2024:i:12:p:1849-1857

Access Statistics for this article

International Journal of Research and Innovation in Social Science is currently edited by Dr. Nidhi Malhan

More articles in International Journal of Research and Innovation in Social Science from International Journal of Research and Innovation in Social Science (IJRISS)
Bibliographic data for series maintained by Dr. Pawan Verma ().