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2021 | Modeling of Complex Data Sets and Risk Analysis | 95--108
Tytuł artykułu

EVT in Risk Estimation on Commodities Exchanges

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Extreme value theory (EVT) is used to estimate the probability of very rare or extreme events. The aim of this research is to answer the question: is EVT useful to risk estimation during unexpected market breakdowns. Based on present work of Boessenkool [2017], Busababodhin et al. [2015], Ganczarek-Gamrot, Krężołek, and Trzpiot [2021] we applied this methodology to commodity market. In analyses we used daily rates of return of commodities quotations: gold (GC), silver (SI), copper (HG), crude oil (CL), natural gas (NG), brant crude (BZ), grains like corn (ZC), soybean meal (ZM), red wheat (KE), cotton (TT), coffee (KT) and lumber (LBS). We considered value from the beginning of quotations to 23 July 2020. Value at Risk (VaR) was used to estimate the risk. VaR was calculated based on L-moments from distributions commonly used in EVT. The paper is organized as follows. The first section concerns on methodology, the second one presents empirical distributions in the period of analysis for commodities rates of return. The last one is an application of presented methodology on these markets. (fragment of text)
Twórcy
  • University of Economics in Katowice, Poland
  • University of Economics in Katowice, Poland
  • University of Economics in Katowice, Poland
Bibliografia
  • Boessenkool B. (2017), extremeStat: quantile estimation, https://cran.r-project.org/web/packages/extremeStat/vignettes/extremeStat.html (accessed: 12.01.2021).
  • Busababodhin P., Seo Y.A., Park J.-S., Kumphon B. (2015), LH-moment estimation of Wakeby distribution with hydrological applications, "Stochastic Environmental Research and Risk Assessment", Vol. 30(6), pp. 1757-1767.
  • Chai T., Draxler R.R. (2014), Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature, "Geoscientific Model Development", Vol. 7, pp. 1247-1250.
  • Fréchet M. (1927), Sur la loi de probabilité de l'écart maximum, "Annales de la Société Polonaise de Mathématique", T. 6(1), pp. 93-116.
  • Ganczarek-Gamrot A., Krężołek D., Trzpiot G. (2021), Using EVT to assess risk on energy market, Springer, Berlin-Heidelberg (in print).
  • Gomes M.I., Neves M.M. (2011), Estimation of the extreme value index for randomly censored data, "Biometrical Letters", Vol. 48(1), pp. 1-22.
  • Hosking J.R.M. (1986), The theory of probability weighted moments, IBM Research Report RC12210, T.J. Watson Research Center, Yorktown Heights, New York.
  • Hosking J.R.M. (1990), L-moments - Analysis and estimation of distributions using linear combinations of order statistics, "Journal of the Royal Statistical Society. Series B", Vol. 52, pp. 105-124.
  • Hosking J.R.M. (1994), The four-parameter kappa distribution, "IBM Journal of Research and Development", Vol. 38(3), pp. 251-258.
  • Hosking J.R.M. (1996), FORTRAN routines for use with the method of L-moments, Version 3, IBM Research Report RC20525, T.J. Watson Research Center, York-town Heights, New York.
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  • Kupiec P. (1995), Techniques for verifying the accuracy of risk management model, "Journal of Derivatives", Vol. 2, s. 173-184.
  • Manfredo M.R., Leuthold R.M. (1999), Value-at-risk analysis: A review and the potential for agricultural applications, "Review of Agricultural Economics", Vol. 21, pp. 99-111.
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  • Singh V.P. (1998), Pearson Type III distribution [in:] V.P. Singh, Entropy-based parameter estimation in hydrology, Springer Science+Business Media, Dordrecht, pp. 231-251, https://link.springer.com/chapter/10.1007/978-94-017-1431-0_14 (accessed: 12.01.2021).
  • Zakaria Z.A., Shabri A., Ahmad U.N. (2012), Estimation of the generalized logistic distribution of extreme events using partial L-moments, "Hydrological Sciences Journal", Vol. 57(3), pp. 424-432.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171620668

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