Exploring the Limitations of Value at Risk: How Good Is It in Practice?

Krause, Andreas

doi:10.1108/eb022958

Cited by 39 publications

(31 citation statements)

References 5 publications

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“…These properties include monotonicity, homogeneity, sub-additivity and translational invariance. Krause (2003) explains that these qualities essentially ensure that investments that are more risky are given a higher value of a risk measure.…”

Section: Limitations Of Varmentioning

confidence: 99%

Emerging markets and portfolio foreign exchange risk: An empirical investigation using a value-at-risk decomposition technique

Sirr

Garvey

Gallagher

2011

Journal of International Money and Finance

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Section: Limitations Of Varmentioning

confidence: 99%

Emerging markets and portfolio foreign exchange risk: An empirical investigation using a value-at-risk decomposition technique

Sirr

Garvey

Gallagher

2011

Journal of International Money and Finance

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“…Description about creating D-Vine copula ARMA-GARCH(1,1) model from Hofmann & Czado (2010) and Krause (2003) …”

Section: Representations Of Volatility Modelsmentioning

confidence: 99%

Assessing Efficiency of D-Vine Copula ARMA-GARCH Method in Value at Risk Forecasting: Evidence from PSE Listed Companies

Klepáč¹,

Hampel²

2015

Acta Univ. Agric. Silvic. Mendelianae Brun.

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The article points out the possibilities of using static D-Vine copula ARMA-GARCH model for estimation of 1 day ahead market Value at Risk. For the illustration we use data of the four companies listed on Prague Stock Exchange in range from 2010 to 2014. Vine copula approach allows us to construct high-dimensional copula from both elliptical and Archimedean bivariate copulas, i.e. multivariate probability distribution, created from process innovations. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we backtested D-Vine copula ARMA-GARCH model against the VaR rolling out of sample forecast from October 2012 to April 2014 of chosen benchmark models, e.g. multivariate VAR-GO-GARCH, VAR-DCC-GARCH and univariate ARMA-GARCH type models. Common backtesting via Kupiec and Christoff ersen procedures off er generalization that technological superiority of model supports accuracy only in case of an univariate modeling -working with non-basic GARCH models and innovations with leptokurtic distributions. Multivariate VAR governed type models and static Copula Vines performed in stated backtesting comparison worse than selected univariate ARMA-GARCH, i.e. it have overestimated the level of actual market risk, probably due to hardly tractable time-varying dependence structure.

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“…Ju and Pearson (1998). See also Krause (2002). 28 Under assumptions specified in the parametric method, the scaling law enables high-frequency data to be translated into a lower frequency by means of multiplication with the Square-Root-of-Time horizon.…”

Section: Amplification Of Impact Due To Harmonized Capital Requirementsmentioning

confidence: 99%

“…Embrechts et al (1999). 42 Among others, Ahn and Boudukh (1999) and Krause (2002) show how VaR can be manipulated using options. Compared with VaR, ES offers the regulator an alternative that is at least theoretically superior.…”

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confidence: 99%

Stochastic Modelling – Boon or Bane for Insurance Industry Capital Regulation?

Bate

Plato

Thallinger

2006

Geneva Pap Risk Insur Issues Pract

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Regulation of the financial industry should pursue three key objectives: consumer protection, market stability, and competitive efficiency. This article discusses core elements of a capital regime that could be used to develop regulation that meets these objectives while fostering an industry-wide enhancement of risk management. The authors argue that a pre-commitment approach can have considerable advantages over regulation based on (stochastic) risk models, as the latter can have adverse effects, especially on market stability and competitive efficiency, while consumer protection would have to be supplemented by additional requirements (such as scenario tests) in any case. Academic studies on capital regulation based on stochastic models have focused more on banking and less on insurance, while work by insurance practitioners has concentrated on the implications for management. The authors, therefore, wish to contribute to a more fundamental discussion of the design of capital and risk management regulation of the insurance industry.

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Exploring the Limitations of Value at Risk: How Good Is It in Practice?

Cited by 39 publications

References 5 publications

Emerging markets and portfolio foreign exchange risk: An empirical investigation using a value-at-risk decomposition technique

Emerging markets and portfolio foreign exchange risk: An empirical investigation using a value-at-risk decomposition technique

Assessing Efficiency of D-Vine Copula ARMA-GARCH Method in Value at Risk Forecasting: Evidence from PSE Listed Companies

Stochastic Modelling – Boon or Bane for Insurance Industry Capital Regulation?

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