Superkurtosis

Stavros Degiannakis, George Filis, Grigorios Siourounis and Lorenzo Trapani

MPRA Paper from University Library of Munich, Germany

Abstract: Risk metrics users assume that the moments of asset returns exist, irrespectively of the trading frequency, hence the observed values of these moments are used to capture the potential losses from asset trading (e.g. with Value-at-Risk (VaR) or Expected Shortfall (ES) calculations). Despite the fact that the behavior of traditional risk metrics is well-examined for high frequency data (e.g. at daily intervals), very little is known on how these metrics behave under Ultra-High Frequency Trading (UHFT). We fill this void by firstly examining the existence of the daily and intraday returns moments, and subsequently by assessing the impact of their (non)existence in a risk management framework. We find that the third and fourth moments of the distribution of asset returns do not exist. We next use both real and simulated data to show that, when daily trading is implemented, VaR or ES deliver estimates in line with what the theory predicts. We show, however, that when UHFT is considered, assuming finite higher order moments, potential losses are much bigger than what the theory predicts, and they increase exponentially as the trading frequency increases. We argue that two possible explanations affect potential loses; first, the exponential increase in the sample data points at UHFT; second, the fact that the data, which are sampled from a heavy-tailed distribution, tend to have higher sample moments than the theory suggests - we call this phenomenon superkurtosis. Our findings entail that traditional risk metrics are unable to properly judge capital adequacy. Hence, the use of risk management techniques such as VaR or ES, by market participants who engage with UHFT, impose serious threats to the stability of financial markets, given that capital ratios may be severely underestimated.

Keywords: Ultra high frequency trading; risk management; finite moments; superkurtosis. (search for similar items in EconPapers)
JEL-codes: C12 C54 F30 F31 G10 G15 G17 (search for similar items in EconPapers)
Date: 2019-10-16
New Economics Papers: this item is included in nep-mst and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/96563/1/MPRA_paper_96563.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/101876/1/MPRA_paper_101876.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/110549/1/MPRA_paper_101876.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/110550/8/MPRA_paper_110550.pdf revised version (application/pdf)

Related works:
Journal Article: Superkurtosis (2023)
Working Paper: Superkurtosis (2023)
Working Paper: Superkurtosis (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:96563

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().