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Heavy-tails in economic data: fundamental assumptions, modelling and analysis

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  • Jo~ao P. da Cruz
  • Pedro G. Lind

Abstract

The study of heavy-tailed distributions in economic and financial systems has been widely addressed since financial time series has become a research subject.After the eighties, several "highly improbable" market drops were observed (e.g. the 1987 stock market drop known as "Black Monday" and on even more recent ones, already in the 21st century) that produce heavy losses that were unexplainable in a GN environment. The losses incurred in these large market drop events did not change significantly the market practices or the way regulation is done but drove some attention back to the study of heavy-tails and their underlying mechanisms. Some recent findings in these context is the scope of this manuscript.

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  • Jo~ao P. da Cruz & Pedro G. Lind, 2012. "Heavy-tails in economic data: fundamental assumptions, modelling and analysis," Papers 1202.0142, arXiv.org.
    • Handle: RePEc:arx:papers:1202.0142
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    Cited by:

    1. George-Jason Siouris & Alex Karagrigoriou, 2017. "A Low Price Correction for Improved Volatility Estimation and Forecasting," Risks, MDPI, vol. 5(3), pages 1-14, August.

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