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Systemic Risk Measures: DistVaR and ...

Klyman, Jared.

Systemic Risk Measures: DistVaR and Other "Too Big To Fail" Risk Measures.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Systemic Risk Measures: DistVaR and Other "Too Big To Fail" Risk Measures.
Author:	Klyman, Jared.
Description:	117 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-06, Section: A, page: .
Notes:	Adviser: Patrick Cheridito.
Contained By:	Dissertation Abstracts International72-06A.
Subject:	Applied Mathematics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3452603
ISBN:	9781124594590

Systemic Risk Measures: DistVaR and Other "Too Big To Fail" Risk Measures.
Klyman, Jared.

Systemic Risk Measures: DistVaR and Other "Too Big To Fail" Risk Measures. - 117 p.

Source: Dissertation Abstracts International, Volume: 72-06, Section: A, page: .

Thesis (Ph.D.)--Princeton University, 2011.

In this paper systemic risk measures designed to find risk when firms or markets are in "distress" are introduced, motivated by trying to quantify what it means to be "too big to fail." Specific risk measures of multiple random variables are defined, building on the extensive risk measure literature for a single random variable. This work expands on the CoVaR measure from [1]. DistVaR ("Distressed Value-at-Risk") and DistES ("Distressed Expected Shortfall") are introduced, which generalize the notion of VaR and AVaR to the case where a separate financial entity is in distress. In addition, DistES may be seen as a generalization of the coherent allocation of AVaR. In contrast to CoVaR (and CoES), these measures have less dependence on the "local behavior" of the joint distribution of the random variables. For this reason, quantile regression is a viable tool to calculate CoVaR, but not DistVaR. However, using quantile regression for CoVaR may be compared with simply modeling the underlying random variables as multivariate Gaussian. In addition, this notion of depending on "local behavior" is made more precise by showing that DistVaR and DistES are smoother with respect to their parameters, specifically requiring weaker conditions for continuity. Also, Monte Carlo simulation is an important, practical tool for calculating DistVaR and DistES, but not CoVaR and CoES.

ISBN: 9781124594590Subjects--Topical Terms:

530992
Applied Mathematics.

Systemic Risk Measures: DistVaR and Other "Too Big To Fail" Risk Measures.
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100 1 $a Klyman, Jared. $3 530991
245 1 0 $a Systemic Risk Measures: DistVaR and Other "Too Big To Fail" Risk Measures.
300 $a 117 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-06, Section: A, page: .
500 $a Adviser: Patrick Cheridito.
502 $a Thesis (Ph.D.)--Princeton University, 2011.
520 $a In this paper systemic risk measures designed to find risk when firms or markets are in "distress" are introduced, motivated by trying to quantify what it means to be "too big to fail." Specific risk measures of multiple random variables are defined, building on the extensive risk measure literature for a single random variable. This work expands on the CoVaR measure from [1]. DistVaR ("Distressed Value-at-Risk") and DistES ("Distressed Expected Shortfall") are introduced, which generalize the notion of VaR and AVaR to the case where a separate financial entity is in distress. In addition, DistES may be seen as a generalization of the coherent allocation of AVaR. In contrast to CoVaR (and CoES), these measures have less dependence on the "local behavior" of the joint distribution of the random variables. For this reason, quantile regression is a viable tool to calculate CoVaR, but not DistVaR. However, using quantile regression for CoVaR may be compared with simply modeling the underlying random variables as multivariate Gaussian. In addition, this notion of depending on "local behavior" is made more precise by showing that DistVaR and DistES are smoother with respect to their parameters, specifically requiring weaker conditions for continuity. Also, Monte Carlo simulation is an important, practical tool for calculating DistVaR and DistES, but not CoVaR and CoES.
520 $a All of these risk measures may be seen as a special case of generalized Co- and Dist-style risk measures on Orlicz hearts, as in [8] for one-dimensional risk measures.
520 $a The final goal is to perform a real-world study wherein these values are calculated. A new reason why multivariate Black Scholes is not an appropriate model choice is given. Instead, a model which avoids procyclicity is considered: regime switching lognormals in continuous time. A new approach is devised to find the distribution, and then to calculate the risk measures. Several companies are considered, using the market value of their assets, to determine risk level. As expected, DistES is more conservative, ceteris paribus . In addition, we comment on why DistVaR or DistES are more appropriate risk measures for regulations than the alternatives. *Please refer to dissertation for footnotes.
590 $a School code: 0181.
650 4 $a Applied Mathematics. $3 530992
650 4 $a Economics, Finance. $3 212585
650 4 $a Operations Research. $3 227148
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690 $a 0508
690 $a 0796
710 2 $a Princeton University. $3 212488
773 0 $t Dissertation Abstracts International $g 72-06A.
790 1 0 $a Cheridito, Patrick, $e advisor
790 $a 0181
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3452603