Quantifying market risk with Value-at-Risk or Expected Shortfall? – Consequences for capital requirements and model risk

Kellner, Ralf; Rösch, Daniel

doi:10.1016/j.jedc.2016.05.002

Cited by 48 publications

(25 citation statements)

References 30 publications

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“…Among the changes in the regulatory treatment of financial institutions' trading book positions, the Basel Committee has proposed the replacement of 99% VaR by 97.5% ES. Kellner and Rösch (2016) provide evidence that under correctly specified models (i.e. models allowing for skewness and heavy tails) the level of capitalization would be higher when using 97.5% ES instead of the 99% VaR.…”

Section: {Insert Tables 7-8}mentioning

confidence: 91%

Multiple-Days-Ahead Value-At-Risk and Expected Shortfall Forecasting for Stock Indices, Commodities and Exchange Rates: Inter-Day Versus Intra-Day Data

Degiannakis

Potamia

2016

SSRN Journal

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Section: {Insert Tables 7-8}mentioning

confidence: 91%

Multiple-Days-Ahead Value-At-Risk and Expected Shortfall Forecasting for Stock Indices, Commodities and Exchange Rates: Inter-Day Versus Intra-Day Data

Degiannakis

Potamia

2016

SSRN Journal

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“…The academic response to this fact is not unanimous: while ES is coherent and takes into consideration the whole tail distribution, it lacks some nice statistical properties characteristic to V@R. See e.g. Cont et al (2010); Acerbi and Székely (2014); Ziegel (2016); Kellner and Rösch (2016); Yamai and Yoshiba (2005); Emmer et al (2015) for further details and interesting discussions. Also, the ES forecasts are believed to be much harder to backtest, a property essential from the regulator's point of view.…”

Section: Introductionmentioning

confidence: 99%

Unbiased Estimation of Risk

Pitera

Schmidt

2016

SSRN Journal

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Abstract. The estimation of risk measures recently gained a lot of attention, partly because of the backtesting issues of expected shortfall related to elicitability. In this work we shed a new and fundamental light on optimal estimation procedures of risk measures in terms of bias. We show that once the parameters of a model need to be estimated, one has to take additional care when estimating risks. The typical plug-in approach, for example, introduces a bias which leads to a systematic underestimation of risk.In this regard, we introduce a novel notion of unbiasedness to the estimation of risk which is motivated by economic principles. In general, the proposed concept does not coincide with the wellknown statistical notion of unbiasedness. We show that an appropriate bias correction is available for many well-known estimators. In particular, we consider value-at-risk and expected shortfall (tail value-at-risk). In the special case of normal distributions, closed-formed solutions for unbiased estimators can be obtained.We present a number of motivating examples which show the outperformance of unbiased estimators in many circumstances. The unbiasedness has a direct impact on backtesting and therefore adds a further viewpoint to established statistical properties.

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“…Confidence levels were chosen taking the Basel II Accord into consideration as well as the basic characteristics of the VaR calculation. Since VaR does not fulfil all the characteristics of the coherent risk measures (see Artzner et al 1999), Basel Committee recently proposed fundamental changes in the regulatory treatment of financial institutions‫׳‬ trading book positions (Kellner, Rösch 2016). Among others, a replacement of 99% VaR by 97.5% Expected Shortfall (ES) for the quantification of market risk is recommended.…”

Section: Empirical Research Of Applicability Of the Fhs Modelmentioning

confidence: 99%

Hull-White’s Value at Risk Model: Case Study of Baltic Equities Market

Radivojević

Ćurčić

Vukajlović

2017

Journal of Business Economics and Management

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Analysis of the applicability of the Hull and White (FHS) model on the Baltic equities market has not been the subject of significant research, especially not in the context of meeting the Basel Committee backtesting rules. The paper discusses the applicability of different variants of this model, in order to answer the question whether any variants (and which of them) of the model can be used in these markets in the context of the Basel II and III standards. The survey results show that 1) there isn’t an optimal variant of this model, but that risk managers have to keep in mind stylized facts of financial returns when they specify the FHS model; 2) according to different criteria of the validity of the model (Basel II and III standards) different variants of models are differently ranked, which suggests that selection of a suitable model implies the use of a large number of different criteria, the model validity and loss function, especially those who take care of the size of tail loss and ES.

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Quantifying market risk with Value-at-Risk or Expected Shortfall? – Consequences for capital requirements and model risk

Cited by 48 publications

References 30 publications

Multiple-Days-Ahead Value-At-Risk and Expected Shortfall Forecasting for Stock Indices, Commodities and Exchange Rates: Inter-Day Versus Intra-Day Data

Multiple-Days-Ahead Value-At-Risk and Expected Shortfall Forecasting for Stock Indices, Commodities and Exchange Rates: Inter-Day Versus Intra-Day Data

Unbiased Estimation of Risk

Hull-White’s Value at Risk Model: Case Study of Baltic Equities Market

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