On the robustness of higher-moment factors in explaining average expected returns: Evidence from Australia

Doan, Minh Phuong; Lin, Chien‐Ting

doi:10.1016/j.ribaf.2011.06.001

Cited by 8 publications

(1 citation statement)

References 21 publications

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“…The higher order moments are used in this investigation due to their ability to capture the general aspects of the distribution of price change, [12,13,14]. The higher order moments show the quantity of outliers within the distribution, [11].…”

Section: Introductionmentioning

confidence: 99%

An investigation of higher order moments of empirical financial data and their implications to risk

Clerk

Savel’ev

2022

Heliyon

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Higher order standardised moments Value-at-risk Gaussian mixturesHere, we analyse the behaviour of the higher order standardised moments of financial time series when we truncate a large data set into smaller and smaller subsets, referred to below as time windows. We look at the effect of the economic environment on the behaviour of higher order moments in these time windows. We observe two different scaling relations of higher order moments when the data sub sets' length decreases; one for longer time windows and another for the shorter time windows. These scaling relations drastically change when the time window encompasses a financial crisis. We also observe a qualitative change of higher order standardised moments compared to the gaussian values in response to a shrinking time window. Moreover, we model the observed scaling laws by analysing the hierarchy of rare events on higher order moments. We extend the analysis of the scaling relations to incorporate the effects these scaling relations have upon risk. We decompose the return series within these time windows and carry out a Value-at-Risk calculation. In doing so, we observe the manifestation of the scaling relations through the change in the Value-at-Risk level.

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