Modeling the distribution of extreme returns in the Chinese stock market

Hussain, Saiful Izzuan; Li, Steven

doi:10.1016/j.intfin.2014.11.007

Cited by 23 publications

(8 citation statements)

References 40 publications

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“…The works of Tolikas (2008) and Tolikas and Gettinby (2009) argue that overall the GL distribution proves a better fit when multiple sub periods are used. Hussain (2015) concludes that the GEV distribution is the best distribution to fit the extremes that exist in the right tail of indexes. Gilli and Kellezi (2006) on the other hand identified the GP distribution as the better choice for modelling extreme events in financial markets over the GEV distribution.…”

Section: Critical Analysismentioning

confidence: 95%

The Appropriate Extreme Value Distribution for Extreme Returns: A Look at GEV& GL

An¹

2016

ASSRJ

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We focus on the problem of modelling extreme events in the financial market. The choice of the distribution that adequately models the extreme behavior of the financial time series. Extreme Value Theory outlines the framework for determining the best fit distribution for the data. However, the generalized extreme value distribution and the generalized Pareto distribution are the traditional distributions that most analysts resort to using. However, recent works have shown that the generalized logistic distribution can also capture the effect of the extreme due to its fat tailed characteristic. In this paper, we determine if this is true and analyze the importance of the generalized logistic distribution in modelling extreme events in the financial market in order to accurately conduct risk measure analysis.

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Section: Critical Analysismentioning

confidence: 95%

The Appropriate Extreme Value Distribution for Extreme Returns: A Look at GEV& GL

An¹

2016

ASSRJ

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“…In this paper, the focus is only on BMM because this approach is unrivaled in demonstrating extreme share price volatility for a given interval. Among researchers who also used this method in their analysis are [8], [9], [10]. In a study conducted by [11], it was found that whenever the returns were independent and identically distributed (i.i.d), an extreme limiting distribution had to be the Generalized Extreme Value Distribution (GEV).…”

Section: Extreme Value Theory and Extreme Risk Modellingmentioning

confidence: 99%

Random Walk Behaviour of Malaysia Share Return in Different Economic Circumstance

Marsani

Shabri

2019

MATEMATIKA

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This journal renders the random walk behaviour of the Malaysian daily share return, through tests of efficient market hypothesis (EMH) based on three different financial periods, namely growth, financial crisis, and recovery period. This review also covers the behaviour of extreme return for weekly and monthly series generated from Block maxima-minima method. Autocorrelation Function test (ACF) and Ljung-Box test had been employed to measure average correlation between observations, while Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), Kwiatkowski Phillips Schmidt Shin (KPSS) test had been used to scan the unit root and the stationarity. Multiple variance ratio tests had also been conducted to examine the random walk behaviour. Serial correlation test indicated that the movement of daily return during the financial crisis period was weak-form efficiency. The unit root and stationary tests suggested that each daily series was stationary, but trend stationary for extreme cases. Variance ratio tests indicated that the return during the recovery period was weak-form inefficiency due to the short lag autocorrelation in series.

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“…Our work is stimulated by increasing sign of uncertainty in the qualities of share returns and swollen recognition of the effects when adopting inaccurate stationarity assumption while analysing the series. Based on recent literature see (Tolikas, 2014;Hussain & Li, 2015;Marsani, Shabri & Jan, 2017) has not yet to be understood, and this study seeks to obtain validation. The study presented here is one of the first investigations to examine in detail the stationarity in extreme stock return by addressing how can one endorse extreme share returns is theoretically nonstationarity.…”

Section: Introductionmentioning

confidence: 99%

Non-Stationary in Extreme Share Return: World Indices Application

Marsani¹,

Shabri²

2020

ASMSJ

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This paper investigates the behaviour of the extreme share return for the 26 different major indices shares by exploring their stationarity. Extreme return for weekly and monthly series is generated by using block maxima method. Four-employed test permits us to spot non-stationarity in extreme movement. The Augmented Dickey-Fuller and Kwiatkowski Phillips Schmidt Shin (KPSS) test scanned the unit root and the stationarity, and Mann-Kendall and Spearman's test inspected the trend and correlation in the series. Our approach approximates global stock returns for weekly and monthly series market activity. We ﬁnd most of the extreme stock to be active in shift movement, and we have confirmed that the movement of extreme share return for the majority of the stock indices in the weekly and monthly interval is non-stationary. This testified statistical property in the series can be used as the first crucial appraisal when scrutinizing extreme share return for future research. Keywords: Extreme share returns; block maxima method; non-stationary; stock market; major indices

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Modeling the distribution of extreme returns in the Chinese stock market

Cited by 23 publications

References 40 publications

The Appropriate Extreme Value Distribution for Extreme Returns: A Look at GEV& GL

The Appropriate Extreme Value Distribution for Extreme Returns: A Look at GEV& GL

Random Walk Behaviour of Malaysia Share Return in Different Economic Circumstance

Non-Stationary in Extreme Share Return: World Indices Application

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