Modification of Price Performance Analytics: A Beta-G Family of Distributions

  • Rasaki OLANREWAJU Institute for Basic Sciences, Technology and Innovation (PAUSTI) Pan African University, Kenya
  • Oluwayemisi ALABA Department of Statistics, University of Ibadan, Nigeria
  • Funmi OYELUDE Political Science and Public Administration Department Babcock University, Nigeria
  • Saheed AJOBO Department of Statistics, University of Ibadan, Nigeria

Abstract

Modification of the pro-forma of wholesale price performance of commodities has received less attention due to its technicality, mathematical derivations, rigorous computations and bounded observations. This study examined nine (9) robust members of the Beta-G family of distributions for the modification of the price performance analytics of Value-at-Risk indexes - Sharpe Ratio, Multi-period & Annualized variations, and Tracking Error indexes.


The retail prices of cereals in Kano state, Nigeria were the empirical data used to verify derived solutions via tangent method for root finding. Log-logistic distributional noise for the prices of rice, maize, sorghum millet, guinea corn, cowpea, groundnut, beans, wheat and cassava produced the minimum positive risk-free rate unit of volatility of 1.6518, 0.5674, 11.3893, 9.2828, 8.8755, 10.1269, 4.1438, 41.8871, and 7.1491 respectively. Chen random noise absolved the characterized fluctuations/noisy traits in the prices of the cereals to give minimum annual and quarterly coefficient of variations of (0.8909 & 0.445); (0.8327 & 0.4163742); (0.7852 & 0.3926); (0.7819 & 0.3909); (0.8812 & 0.4406); (0.81163 & 0.4058); (0.8281 & 0.4141); (0.7873 & 0.3937); and (7.1491 & 0.8538) for maize, sorghum, millet, guinea corn, cowpea, groundnut, beans, wheat, and cassava respectively.

References

[1] Adams, B., Williams, K. R. 2019. Zone Pricing in Retail Oligopoly. American Economic Journal: Microeconomics, 11, 124–156.
[2] Alexander, C. 2005. The Present and Future of Financial Risk Management. Journal of Financial Econometrics, 3(1): 3–25.
[3] Balasubramanian, S., Bhattacharya, S., Krishnan, V. V. 2015. Pricing Information Goods: A Strategic Analysis of the Selling and Pay-Per Use Mechanisms. Marketing Science, 34(2): 218-234.
[4] Diermeier, J., Solnik, B. 2001. Global Pricing of Equity. Financial Analysts Journal, 57: 37–47.
[5] Ellickson, P. B., Misra, S. 2008. Supermarket Pricing Strategies, Marketing Science, 27: 811–828.
[6] Gizelis, C. A., Vergados, D. D. 2011. A Survey of Pricing Schemes in Wireless Networks. IEEE Communications Surveys & Tutorials, 13(1): 126-145.
[7] Hitsch, J.G., Hortaçsu, A., Xiliang, L. 2019. Prices and Promotions in U.S. Retail Markets: Evidence from Big Data. Working paper. No. 2019-117.
[8] Kaplan, G., Menzio, G., Rudanko, L., Trachter, N. 2019. Relative Price Dispersion: Theory and Evidence. American Economic Journal: Microeconomics, 11: 68–124.
[9] Kaplan, G., Menzio, G. 2015. The Morphology of Price Dispersion, International Economic Review, 56: 1165–1205.
[10] Niyato, D., Alsheikh, M.A., Wang, P., Kim, D.I., Han, Z. 2016. Market Model and Optimal Pricing Scheme of Big Data and Internet of Things (IoT)arXiv:1602.03202v1[cs.GT] 7Feb2016
Published
2021-12-31
How to Cite
OLANREWAJU, Rasaki et al. Modification of Price Performance Analytics: A Beta-G Family of Distributions. Journal of Advanced Studies in Finance, [S.l.], v. 12, n. 2, p. 87-103, dec. 2021. ISSN 2068-8393. Available at: <https://journals.aserspublishing.eu/jasf/article/view/6709>. Date accessed: 23 jan. 2022. doi: https://doi.org/10.14505/jasf.v12.2(24).01.
Section
Journal of Advanced Studies in Finance