Bootstrap inference for conditional risk measures

Research output: ThesisDoctoral ThesisInternal

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Abstract

Risk measures play a key role in financial risk management and are enforced by current legislation to protect financial stability. In particular Value-at-Risk (VaR) and Expected Shortfall (ES) are used to assess the market risks associated with financial assets. These risk measures are frequently applied conditionally to account for the temporal dependence of financial data. To quantify the uncertainty induced by parameter estimation, practitioners often construct confidence intervals by resorting to resampling methods. This thesis provides a theoretical justification for intervals constructed for conditional risk measures. New resampling methods are proposed and validated to quantify the parameter uncertainty around the conditional VaR and ES estimates.
Original languageEnglish
Awarding Institution
  • Maastricht University
Supervisors/Advisors
  • Palm, Franz, Supervisor
  • Beutner, E.A., Advisor
  • Smeekes, Stephan, Advisor
Award date20 Jun 2019
Place of PublicationMaastricht
Publisher
Print ISBNs9789463803724
DOIs
Publication statusPublished - 2019

Keywords

  • Risk management
  • Finance
  • Value-at-Risk
  • Expected Shortfall
  • Bootstrap

Cite this

Heinemann, Alexander M.. / Bootstrap inference for conditional risk measures. Maastricht : ProefschriftMaken Maastricht, 2019. 243 p.
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Bootstrap inference for conditional risk measures. / Heinemann, Alexander M.

Maastricht : ProefschriftMaken Maastricht, 2019. 243 p.

Research output: ThesisDoctoral ThesisInternal

TY - THES

T1 - Bootstrap inference for conditional risk measures

AU - Heinemann, Alexander M.

PY - 2019

Y1 - 2019

N2 - Risk measures play a key role in financial risk management and are enforced by current legislation to protect financial stability. In particular Value-at-Risk (VaR) and Expected Shortfall (ES) are used to assess the market risks associated with financial assets. These risk measures are frequently applied conditionally to account for the temporal dependence of financial data. To quantify the uncertainty induced by parameter estimation, practitioners often construct confidence intervals by resorting to resampling methods. This thesis provides a theoretical justification for intervals constructed for conditional risk measures. New resampling methods are proposed and validated to quantify the parameter uncertainty around the conditional VaR and ES estimates.

AB - Risk measures play a key role in financial risk management and are enforced by current legislation to protect financial stability. In particular Value-at-Risk (VaR) and Expected Shortfall (ES) are used to assess the market risks associated with financial assets. These risk measures are frequently applied conditionally to account for the temporal dependence of financial data. To quantify the uncertainty induced by parameter estimation, practitioners often construct confidence intervals by resorting to resampling methods. This thesis provides a theoretical justification for intervals constructed for conditional risk measures. New resampling methods are proposed and validated to quantify the parameter uncertainty around the conditional VaR and ES estimates.

KW - Risk management

KW - Finance

KW - Value-at-Risk

KW - Expected Shortfall

KW - Bootstrap

U2 - 10.26481/dis.20190620ah

DO - 10.26481/dis.20190620ah

M3 - Doctoral Thesis

SN - 9789463803724

PB - ProefschriftMaken Maastricht

CY - Maastricht

ER -