Open AccessEstimating the Tail Index of Conditional Distribution of Asset Returns
Author(s) -
Feruzbek Davletov
Publication year - 2022
Publication title -
international journal of financial research
Language(s) - English
Resource type - Journals
eISSN - 1923-4031
pISSN - 1923-4023
DOI - 10.5430/ijfr.v13n2p14
Subject(s) - econometrics , extreme value theory , estimator , economics , generalized pareto distribution , financial market , normality , asset (computer security) , tail dependence , conditional variance , value at risk , conditional probability distribution , variance (accounting) , gaussian , financial economics , statistics , mathematics , autoregressive conditional heteroskedasticity , finance , computer science , risk management , physics , volatility (finance) , computer security , multivariate statistics , quantum mechanics , accounting
Massive stock market failures in the past decades cast a doubt on the standard normality assumption of many economic models. Despite decent research on the non-Gaussian characteristics of many financial time series, the question of tail heaviness still remains open. We conduct diagnostic analysis on the conditional distribution of asset returns of small/large companies (Russell 2000 and S&P 500) to look for clear evidence on the presence of heavy tails. We employ extreme value (EVT) tools in order to estimate the shape parameter () of Generalized Pareto distribution (GPD) using a well-known “Hill estimator”. It turns out that the shape parameter lies in the interval implying that the conditional distribution of asset returns supposedly has finite mean and variance. We also find an evidence that the tail estimates experience structural breaks during 2008 Global Financial Crisis.