Degiannakis, Stavros and Filis, George and Siourounis, Grigorios and Trapani, Lorenzo (2021): Superkurtosis.
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Abstract
Very little is known on how traditional risk metrics behave under intraday trading. We fill this void by examining the finiteness of the returns' moments and assessing the impact of their infinity in a risk management framework. We show that when intraday trading is considered, assuming finite higher order moments, potential losses are materially larger than what the theory predicts, and they increase exponentially as the trading frequency increases - a phenomenon we call superkurtosis. Hence, the use of the current risk management techniques under intraday trading impose threats to the stability of financial markets, given that capital ratios may be severely underestimated.
Item Type: | MPRA Paper |
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Original Title: | Superkurtosis |
Language: | English |
Keywords: | Ultra-high frequency trading, risk management, finite moments, superkurtosis. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C54 - Quantitative Policy Modeling F - International Economics > F3 - International Finance > F30 - General F - International Economics > F3 - International Finance > F31 - Foreign Exchange G - Financial Economics > G1 - General Financial Markets > G10 - General G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation |
Item ID: | 110550 |
Depositing User: | George Filis |
Date Deposited: | 09 Nov 2021 14:09 |
Last Modified: | 09 Nov 2021 14:09 |
References: | Beddington, J., C. Furse, P. Bond, D. Cliff, C. Goodhart, K. Houstoun, O. Linton, and J.-P. Zigrand (2012). Foresight: the future of computer trading in financial markets: final project report. Bradley, B. O. and M. S. Taqqu (2003). Financial risk and heavy tails. In Handbook of heavy tailed distributions in finance, pp. 35-103. North-Holland. Brush, S., T. Schoenberg, and S. Ring (2015). How a mystery trader with an algorithm may have caused the ash crash. Bloomberg News 22. Geyer, C. J. and G. D. Meeden (2005). Fuzzy and randomized confidence intervals and p-values. Statistical Science, 358-366. Horvath, L. and L. Trapani (2019). Testing for randomness in a random coefficient autoregression model. Journal of Econometrics 209 (2), 338-352. Kirilenko, A., A. S. Kyle, M. Samadi, and T. Tuzun (2017). The ash crash: High-frequency trading in an electronic market. The Journal of Finance 72 (3), 967-998. Kirilenko, A. A. and A. W. Lo (2013). Moore's law versus Murphy's law: Algorithmic trading and its discontents. Journal of Economic Perspectives 27 (2), 51-72. Manganelli, S. and R. F. Engle (2001). Value at risk models in finance. Technical report. Manhire, J. (2018). Measuring black swans in financial markets. Journal of Mathematical Finance 8 (1), 227-239. Michel, R. (1976). Nonuniform central limit bounds with applications to probabilities of deviations. The Annals of Probability, 102-106. Trapani, L. (2016). Testing for (in)finite moments. Journal of Econometrics 191 (1), 57-68. Yadav, Y. (2015). How algorithmic trading undermines efficiency in capital markets. Vanderbilt Law Review 68, 1607. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/110550 |
Available Versions of this Item
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Superkurtosis. (deposited 24 Oct 2019 09:14)
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Superkurtosis. (deposited 22 Jul 2020 07:18)
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Superkurtosis. (deposited 09 Nov 2021 11:54)
- Superkurtosis. (deposited 09 Nov 2021 14:09) [Currently Displayed]
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Superkurtosis. (deposited 09 Nov 2021 11:54)
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Superkurtosis. (deposited 22 Jul 2020 07:18)