implies that the distribution is more clustered around the mean and that extreme volatility movements are more likely to occur within the heavy tails relative to a normal distribution.
The distributions of the analysed indexes were characterised by leptokurtosis
(kurtosis ranged from 5,5-14) and in most cases the left tail skewness (skewness was less than zero), detailed descriptive statistics are presented in Table 1.
Time series data is characterized by volatility clustering, leptokurtosis
, hetero-sce-dasticity, serial correlation, and non-normality (Mandelbrot, 1963; Fama, 1965).
The change in stock prices is clear, positively skewed, and has excess kurtosis indicating that leptokurtosis
is undeniable, which also predicates the use of a jump diffusion model.
What particularly make the premium spreads estimated from Model SC significant are the asymmetry and leptokurtosis
possessed by the distributions generated from Model SC.
It is one of the most widely used and well-known volatility models due to its flexibility and accuracy to modeling stylized facts of financial asset returns, such as leptokurtosis
and volatility clustering.
Using a generalized regime switching model, Mun Fung and Hock See (2002) consider the temporary state of volatility of daily returns on crude oil futures which lead to sudden changes in mean and variance, GARCH dynamics, basis-driven time-varying transition probabilities and conditional leptokurtosis
. This flexible model provides many compound features of conditional volatility within a relatively economical configuration.
Most of the NE components come from the self-NEs (an effect of ICA), through their high skewness and positive kurtosis or leptokurtosis
(Table 5), leading to fat long pdf tails.
Financial time series data is generally characterized by volatility clustering, leptokurtosis
and heavy tailed distributions.
Simple tests for peakedness, fat tails and leptokurtosis
based on quantiles.
Techniques to Account for Leptokurtosis
and Assymetric Behaviour in Returns Distributions, Journal of Risk Finance, 11(5), 464-480.
Despite the significant differences which derive from the differences in theoretical postulates on which approaches are based, a common feature of the most popular VaR approaches is their inability to be simultaneously effective in capturing leptokurtosis
and strong time-varying volatility.