The modeling of extreme stochastic dependence using copulas and extreme value theory: case study from energy prices
-
2017-06-05 https://doi.org/10.14419/gjma.v5i2.7256 -
Copulas, energy commodity spot prices, extreme value theory, tail dependence. -
Abstract
In this paper, we investigate the properties of tail dependence with an approach which is based on the copula models and extreme value theory to obtain a joint distribution function of extreme events and to quantify the dependence between random variables. To achieve this objective, we quantify the large co-movements between the random variables returns which are based on the data set daily quotes of exceeds the threshold value of random variables. In this study, stochastic dependence was modeled by the copulas which it provides a good approach for constructing multivariate probability distributions with flexible marginal’s and different forms of dependence. Choosing the right copula is very important in modeling. The multivariate distributions are easily simulated using the copulas. Finally we can describe the copula family which correctly represents the dependence. To demonstrate the usefulness of the proposed models, we confine our analysis to big price changes of energy commodity spot prices. The empirical findings demonstrated that the copula model which is combined the extreme value theory is a good approach to model the together extreme large changes.
-
References
[1] Embrechts P, Kluppelberg C& Mikosch T(1997),Modeling extremal events: for insurance and finance, Springer Verlag, Berlin.
[2] Nelsen RB (2006), an introduction to copulas, 2nd ed., Springer-Verlag,New York.
[3] ZhangL& and Singh VP(2007), Gumbel-Hougaardcopula for trivariate rainfall frequency analysis, J. Hydrol. Eng, 12,pp.409-419.
[4] Trivedi PK& Zimme DM (2005), Copula modeling: an introduction for practitioners, Foundations and Trends in Econometrics,Vol.1,no 1, pp.1-111.
[5] JoeJ(1997), Multivariate models and dependence concepts, Chapman& Hall,London.
[6] Frees EW &Valdez EA (1998), Understanding relationships using copulas, North American Actuarial Journal, 2(1), pp.1-26.
[7] Cherubini U,Luciano E & Vecchiato W(2004), Copula methods in finance, Wiley,New York.
[8] MikoschT(2006),Copulas: tales and facts, Extremes,9(3),pp.20,.
[9] GrigoriuM(2016),Do seismic intensity measures(IMs) measure up?, Probability Engineering Mechanics, 46,pp.80-93.
[10] Sun GH(2015),Tail dependence study of SSE composite index and SZSE component index based on the copula, Applied Mathematics, 4, pp.1065-1069.
[11] Halder C &Das K(2016), Understanding extreme stock trading volume by generalized Paretodistribution, North Caroline Journal of Mathematics and Statistics ,vol.2,pp.45-60.
[12] Poon S, RockingerM&Town J (2004), Extreme value dependence in financial markets: diagnostics, models and financial implications, Review of Financial Studies,17,pp.581-610.
[13] Beirlant J& Goegebeur Y (2006),A goodness of fit statistics for Pareto-type behavior, Journal of Computational and Applied Mathematics,186,pp.99-116.
[14] DaheuvelsP(1981), Nonparametric test of independence, In Analytical methods 861 of Lecture Notes in Mathematics, Springer,Berlin,pp.42-50.
[15] SklarA(1981), Functitions de repartition a n dimensions et leurs merges, Publ. Inst.Statist.Univ.Paris, 8, pp.229-23.
[16] Genest C& Favre AC (2007), everything you always wanted to know about copula modeling but were afraid to ask, Journal of Hydrologic Engineering, 12(4), pp.347-368.
[17] McNeilAJ, Frey R& Embrechts P (2005), Quantitative risk management: concepts, techniques, tools, Princeton University Press,Princeton.
-
Downloads
-
How to Cite
önalan, ömer. (2017). The modeling of extreme stochastic dependence using copulas and extreme value theory: case study from energy prices. Global Journal of Mathematical Analysis, 5(2), 29-36. https://doi.org/10.14419/gjma.v5i2.7256Received date: 2017-01-20
Accepted date: 2017-02-18
Published date: 2017-06-05