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Capturing time variation within systemic risk estimation

Hovstadius, Christian LU and Lindgren, Baltsar LU (2023) NEKN02 20231
Department of Economics
Abstract
Systemic risk can be defined as the risk to the whole financial system. Financial institutions may contribute more or less to this risk, and measuring the systemic risk
contributions of institutions is of central importance for regulators. This is important
since it makes it possible to determine to what extent different institutions contribute
to the overall systemic risk of the financial system and hence which ones are more or
less systemically important. Adrian & Brunnermeier (2011) proposes the systemic risk
measure CoVaR, which builds on the framework of Value-at-Risk (VaR). The definition
of CoVaR is the qth% VaR of institution j (or in the case of this essay, the European
financial system) given that another institution i is... (More)
Systemic risk can be defined as the risk to the whole financial system. Financial institutions may contribute more or less to this risk, and measuring the systemic risk
contributions of institutions is of central importance for regulators. This is important
since it makes it possible to determine to what extent different institutions contribute
to the overall systemic risk of the financial system and hence which ones are more or
less systemically important. Adrian & Brunnermeier (2011) proposes the systemic risk
measure CoVaR, which builds on the framework of Value-at-Risk (VaR). The definition
of CoVaR is the qth% VaR of institution j (or in the case of this essay, the European
financial system) given that another institution i is at its qth% VaR. ∆CoVaR measures
the change in the VaR for institution j given that institution i is in distress (compared
to its normal state), and estimates the marginal risk contribution for a given institution.
To obtain time variation in the estimates, the authors suggests using state variables that
condition the mean and volatility of the risk measure. This essay tries to answer the
question whether the systemic risk estimates obtained by using the CoVaR methodology,
and the systemic risk contribution rankings between banks, are sensitive to the selection
of these state variables. Using equity price data for 141 European banks, and data for 20
state variables during the time period from 31st of December 2002 to 30th of September
2022, this essay estimates VaR, CoVaR, ∆CoVaR and ∆$CoVaR using quantile regressions, following the methodology of Adrian & Brunnermeier (2011). Using four different
state variable selection methods including the one suggested by Adrian & Brunnermeier,
the supervised and unsupervised machine learning methods of Lasso regression and PCA,
and a randomized method, systemic risk contributions over time are estimated and the
banks are ranked according to these estimates. The results of this essay suggests that
the CoVaR risk measure indeed is sensitive to the choice of state variables selection
method, where both the estimates as well as rankings differs between the methods. (Less)
Please use this url to cite or link to this publication:
author
Hovstadius, Christian LU and Lindgren, Baltsar LU
supervisor
organization
course
NEKN02 20231
year
type
H1 - Master's Degree (One Year)
subject
keywords
Systemic risk, CoVaR, State variables, Lasso, PCA
language
English
id
9118800
date added to LUP
2023-11-24 08:56:52
date last changed
2023-11-24 08:56:52
@misc{9118800,
  abstract     = {{Systemic risk can be defined as the risk to the whole financial system. Financial institutions may contribute more or less to this risk, and measuring the systemic risk
contributions of institutions is of central importance for regulators. This is important
since it makes it possible to determine to what extent different institutions contribute
to the overall systemic risk of the financial system and hence which ones are more or
less systemically important. Adrian & Brunnermeier (2011) proposes the systemic risk
measure CoVaR, which builds on the framework of Value-at-Risk (VaR). The definition
of CoVaR is the qth% VaR of institution j (or in the case of this essay, the European
financial system) given that another institution i is at its qth% VaR. ∆CoVaR measures
the change in the VaR for institution j given that institution i is in distress (compared
to its normal state), and estimates the marginal risk contribution for a given institution.
To obtain time variation in the estimates, the authors suggests using state variables that
condition the mean and volatility of the risk measure. This essay tries to answer the
question whether the systemic risk estimates obtained by using the CoVaR methodology,
and the systemic risk contribution rankings between banks, are sensitive to the selection
of these state variables. Using equity price data for 141 European banks, and data for 20
state variables during the time period from 31st of December 2002 to 30th of September
2022, this essay estimates VaR, CoVaR, ∆CoVaR and ∆$CoVaR using quantile regressions, following the methodology of Adrian & Brunnermeier (2011). Using four different
state variable selection methods including the one suggested by Adrian & Brunnermeier,
the supervised and unsupervised machine learning methods of Lasso regression and PCA,
and a randomized method, systemic risk contributions over time are estimated and the
banks are ranked according to these estimates. The results of this essay suggests that
the CoVaR risk measure indeed is sensitive to the choice of state variables selection
method, where both the estimates as well as rankings differs between the methods.}},
  author       = {{Hovstadius, Christian and Lindgren, Baltsar}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Capturing time variation within systemic risk estimation}},
  year         = {{2023}},
}