TITLE:
Importance of Generalized Logistic Distribution in Extreme Value Modeling
AUTHORS:
K. Nidhin, C. Chandran
KEYWORDS:
Financial Risk Modeling; Stock Market Analysis; Generalized Logistic Distribution; Generalized Extreme Value Distribution; Tail Equivalence; Maximum Stability; Random Sample size; Limit Distribution
JOURNAL NAME:
Applied Mathematics,
Vol.4 No.3,
March
27,
2013
ABSTRACT:
We consider a problem from stock market modeling, precisely, choice of adequate distribution of modeling extremal behavior of stock market data. Generalized extreme value (GEV) distribution and generalized Pareto (GP) distribution are the classical distributions for this problem. However, from 2004, [1] and many other researchers have been empirically showing that generalized logistic (GL) distribution is a better model than GEV and GP distributions in modeling extreme movement of stock market data. In this paper, we show that these results are not accidental. We prove the theoretical importance of GL distribution in extreme value modeling. For proving this, we introduce a general multivariate limit theorem and deduce some important multivariate theorems in probability as special cases. By using the theorem, we derive a limit theorem in extreme value theory, where GL distribution plays central role instead of GEV distribution. The proof of this result is parallel to the proof of classical extremal types theorem, in the sense that, it possess important characteristic in classical extreme value theory, for e.g. distributional property, stability, convergence and multivariate extension etc.