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  • Banking risk with Appendix August 2015

    Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 250, 1, 2016 DOI: 10.1016/j.ejor.2015.09.057

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Parameters measuring bank risk and their estimation

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Parameters measuring bank risk and their estimation. / Tsionas, Efthymios.
In: European Journal of Operational Research, Vol. 250, No. 1, 01.04.2016, p. 291-304.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Tsionas, E 2016, 'Parameters measuring bank risk and their estimation', European Journal of Operational Research, vol. 250, no. 1, pp. 291-304. https://doi.org/10.1016/j.ejor.2015.09.057

APA

Tsionas, E. (2016). Parameters measuring bank risk and their estimation. European Journal of Operational Research, 250(1), 291-304. https://doi.org/10.1016/j.ejor.2015.09.057

Vancouver

Tsionas E. Parameters measuring bank risk and their estimation. European Journal of Operational Research. 2016 Apr 1;250(1):291-304. Epub 2015 Oct 23. doi: 10.1016/j.ejor.2015.09.057

Author

Tsionas, Efthymios. / Parameters measuring bank risk and their estimation. In: European Journal of Operational Research. 2016 ; Vol. 250, No. 1. pp. 291-304.

Bibtex

@article{839d8deeba8344db821f9891427e343a,
title = "Parameters measuring bank risk and their estimation",
abstract = "The paper develops estimation of three parameters of banking risk based on an explicit model of expected utility maximization by financial institutions subject to the classical technology restrictions of neoclassical production theory. The parameters are risk aversion, prudence or downside risk aversion and generalized risk resulting from a factor model of loan prices. The model can be estimated using standard econometric techniques, like GMM for dynamic panel data and latent factor analysis for the estimation of covariance matrices. An explicit functional form for the utility function is not needed and we show how measures of risk aversion and prudence (downside risk aversion) can be derived and estimated from the model. The model is estimated using data for Eurozone countries and we focus particularly on (i) the use of the modeling approach as a device close to an “early warning mechanism”, (ii) the bank- and country-specific estimates of risk aversion and prudence (downside risk aversion), and (iii) the derivation of a generalized measure of risk that relies on loan-price uncertainty. Moreover, the model provides estimates of loan price distortions and thus, allocative efficiency.",
keywords = "Financial stability, Banking, Expected utility maximization, Sub-prime crisis, Financial crisis",
author = "Efthymios Tsionas",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 250, 1, 2016 DOI: 10.1016/j.ejor.2015.09.057",
year = "2016",
month = apr,
day = "1",
doi = "10.1016/j.ejor.2015.09.057",
language = "English",
volume = "250",
pages = "291--304",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Parameters measuring bank risk and their estimation

AU - Tsionas, Efthymios

N1 - This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 250, 1, 2016 DOI: 10.1016/j.ejor.2015.09.057

PY - 2016/4/1

Y1 - 2016/4/1

N2 - The paper develops estimation of three parameters of banking risk based on an explicit model of expected utility maximization by financial institutions subject to the classical technology restrictions of neoclassical production theory. The parameters are risk aversion, prudence or downside risk aversion and generalized risk resulting from a factor model of loan prices. The model can be estimated using standard econometric techniques, like GMM for dynamic panel data and latent factor analysis for the estimation of covariance matrices. An explicit functional form for the utility function is not needed and we show how measures of risk aversion and prudence (downside risk aversion) can be derived and estimated from the model. The model is estimated using data for Eurozone countries and we focus particularly on (i) the use of the modeling approach as a device close to an “early warning mechanism”, (ii) the bank- and country-specific estimates of risk aversion and prudence (downside risk aversion), and (iii) the derivation of a generalized measure of risk that relies on loan-price uncertainty. Moreover, the model provides estimates of loan price distortions and thus, allocative efficiency.

AB - The paper develops estimation of three parameters of banking risk based on an explicit model of expected utility maximization by financial institutions subject to the classical technology restrictions of neoclassical production theory. The parameters are risk aversion, prudence or downside risk aversion and generalized risk resulting from a factor model of loan prices. The model can be estimated using standard econometric techniques, like GMM for dynamic panel data and latent factor analysis for the estimation of covariance matrices. An explicit functional form for the utility function is not needed and we show how measures of risk aversion and prudence (downside risk aversion) can be derived and estimated from the model. The model is estimated using data for Eurozone countries and we focus particularly on (i) the use of the modeling approach as a device close to an “early warning mechanism”, (ii) the bank- and country-specific estimates of risk aversion and prudence (downside risk aversion), and (iii) the derivation of a generalized measure of risk that relies on loan-price uncertainty. Moreover, the model provides estimates of loan price distortions and thus, allocative efficiency.

KW - Financial stability

KW - Banking

KW - Expected utility maximization

KW - Sub-prime crisis

KW - Financial crisis

U2 - 10.1016/j.ejor.2015.09.057

DO - 10.1016/j.ejor.2015.09.057

M3 - Journal article

VL - 250

SP - 291

EP - 304

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -