Financial Risk Meter FRM based on Expectiles
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10453726" target="_blank" >RIV/00216208:11320/22:10453726 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=rE4nz00KG_" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=rE4nz00KG_</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jmva.2021.104881" target="_blank" >10.1016/j.jmva.2021.104881</a>
Alternative languages
Result language
angličtina
Original language name
Financial Risk Meter FRM based on Expectiles
Original language description
The Financial Risk Meter (FRM) is an established quantitative tool that, based on conditional Value at Risk (VaR) ideas, yields insight into the dynamics of network risk. Originally, the FRM has been composed via Lasso based quantile regression, but we here extend it by incorporating the idea of expectiles, thus indicating not only the tail probability but rather the actual tail loss given a stress situation in the network. The expectile variant of the FRM enjoys several advantages: Firstly, the multivariate tail risk indicator conditional expectile-based VaR (CoEVaR) can be derived, which is sensitive to the magnitude of extreme losses. Next, FRM index is not restricted to an index compared to the quantile based FRM mechanisms, but can be expanded to a set of systemic tail risk indicators, which provide investors with numerous tools in terms of diverse risk preferences. The power of FRM also lies in displaying the FRM distribution across various entities every day. In a functional data context, the FRM identifies outlying curves and serves as a signal box to display aberrant functional behavior. Two distinct patterns can be discovered under high stress and during stable periods from the empirical results in the United States stock market. Furthermore, the framework is able to identify individual risk characteristics and to capture spillover effects in a network. (C) 2021 Elsevier Inc. All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GX19-28231X" target="_blank" >GX19-28231X: DyMoDiF - Dynamic Models for the Digital Finance</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Multivariate Analysis
ISSN
0047-259X
e-ISSN
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Volume of the periodical
189
Issue of the periodical within the volume
May
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
104881
UT code for WoS article
000759649300017
EID of the result in the Scopus database
2-s2.0-85118996013