Return to search

Analyzing value at risk and expected shortfall methods: the use of parametric, non-parametric, and semi-parametric models

Value at Risk (VaR) and Expected Shortfall (ES) are methods often used to measure market risk. Inaccurate and unreliable Value at Risk and Expected Shortfall models can lead to underestimation of the market risk that a firm or financial institution is exposed to, and therefore may jeopardize the well-being or survival of the firm or financial institution during adverse markets. The objective of this study is therefore to examine various Value at Risk and Expected Shortfall models, including fatter tail models, in order to analyze the accuracy and reliability of these models.
Thirteen VaR and ES models under three main approaches (Parametric, Non-Parametric and Semi-Parametric) are examined in this study. The results of this study show that the proposed model (ARMA(1,1)-GJR-GARCH(1,1)-SGED) gives the most balanced Value at Risk results. The semi-parametric model (Extreme Value Theory, EVT) is the most accurate Value at Risk model in this study for S&P 500. / October 2014

Identiferoai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/23875
Date25 August 2014
CreatorsHuang, Xinxin
ContributorsBoyd, Milton (Agribusiness and Agricultural Economics) Pai, Jeffrey (Warren Center for Acturial Studies and Research), Coyle, Barry (Agribusiness and Agricultural Economics) Porth, Lysa (Warren Center for Acturial Studies and Research)
Source SetsUniversity of Manitoba Canada
Detected LanguageEnglish

Page generated in 0.0016 seconds