Risk estimation for short-term financial data through pooling of stable fits

Marzia De Donno, Riccardo Donati (), Gino Favero () and Paola Modesti ()
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Riccardo Donati: Redexe S.r.l.
Gino Favero: University of Parma
Paola Modesti: University of Parma

Financial Markets and Portfolio Management, 2019, vol. 33, issue 4, No 3, 447-470

Abstract: Abstract We suggest a new, parsimonious, method to fit financial data with a stable distribution. As a result of a stable fitting via maximum likelihood estimation (MLE), we find that some assets have similar values as stability indices, independently of the time interval considered. This fact can be exploited to pool the assets in groups and to choose a parameter $$\alpha $$α as an ex ante stability index, valid for every asset in the pool sector. With this fixed parameter, MLE is used again to obtain the other stable parameters. We discuss an innovative risk measure, based on the Expected Shortfall, which exploits the above procedure. We show that it gives a good estimation of risk even when only short time series are available. Finally, we introduce the notion of Risk Class, which allows us to classify assets according to their risk exposition and to compare different methods for the computation of the Expected Shortfall.

Keywords: Stable distribution; Heavy tails; Stability index; Sector pool; Expected Shortfall; Risk Class (search for similar items in EconPapers)
JEL-codes: C13 C51 C53 G17 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11408-019-00340-5

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