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Backtesting value-at-risk based on tail losses
Journal of Empirical Finance, 2008Extreme losses caused by leverage and financial derivatives highlight the need to backtest Value-at-Risk (VaR) based on the sizes of tail losses, because the risk measure currently used disregards losses beyond the VaR boundary. While Basel II backtests VaR by counting the number of exceptions, this paper proposes to use the saddlepoint technique by ...
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Value at Risk Estimation for Heavy Tailed Distributions [PDF]
The International Journal of Business and Finance Research, 2014 The aim of this paper is to derive a coherent risk measure for heavy tailed GARCH processes using extreme value theory. For the proposed measure, the risk associated to a given portfolio is less than the sum of the stand-alone risks of its components. This measure which is value at risk (VaR), is the limiting result of an infinity shift of location and
Imed Gammoudi +2 more
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Efficient Computation of Value at Risk with Heavy-Tailed Risk Factors
SSRN Electronic Journal, 2009The probabilities considered in value-at-risk (VaR) are typically of moderate deviations. However, the variance reduction techniques developed in the literature for VaR computation are based on large deviations methods. Modeling heavy-tailed risk factors using multivariate $t$ distributions, we develop a new moderate-deviations method for VaR ...
Cheng-der Fuh +3 more
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Risk measurement for insurance sector with credible tail value-at-risk
AIP Conference Proceedings, 2019Providing protection against probability of losses is important issue in insurance company. Insurance company must certainly estimate all the risks which can be done by using risk measures. Value-at-Risk (VaR) is one of risk measures that is widely used in insurance industry.
Ferren Alwie +2 more
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Efficient Simulation of Value at Risk with Heavy-Tailed Risk Factors
Operations Research, 2011Simulation of small probabilities has important applications in many disciplines. The probabilities considered in value-at-risk (VaR) are moderately small. However, the variance reduction techniques developed in the literature for VaR computation are based on large-deviations methods, which are good for very small probabilities.
Fuh, Chengder +3 more
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Value-at-Risk, Tail Value-at-Risk und Schadenverteilung in der Personenversicherung
Blätter der DGVFM, 2006Zur Herleitung der Gesamtschadenverteilung werden die drei Vorgehensweisen Faltung, Poisson-Approximation und der zentrale Grenzwertsatz vorgestellt. Es werden die Risikomase Value-at-risk und Tail Value-at-risk fur die vorliegende Fragestellung definiert und fur die Normalverteilung allgemein angegeben.
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Value-at-Risk Diversification of $��$-stable Risks: The Tail-Dependence Puzzle
2017We consider the problem of risk diversification of $ $-stable heavy tailed risks. We study the behaviour of the aggregated Value-at-Risk, with particular reference to the impact of different tail dependence structures on the limits to diversification.
Cherubini, Umberto, Neri, Paolo
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Fat Tails, Value at Risk, and the Daily Palladium Returns
SSRN Electronic Journal, 2017The past decade has witnessed the rapid growing of the world palladium market. Thus, it is even more important to develop effective quantitative tools for risk management of palladium assets at this moment. In this paper, we investigate five different types of widely-used statistical distributions and employ the industry standard risk measurement ...
Jianhua Ding, Turen Guo, Bin Guo
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A predictive approach to quantiles: Application to Value at Risk and Tail Value at Risk
Probability and Mathematical StatisticsSummary: We prove that quantiles are best predictors in a special metric. The best predictor turns out to coincide with the notions of generalized arithmetic mean, exponential barycenter and certainty equivalent. We also show that the computation of tail value at risk (TVaR) reduces to the computation of a quantile with a higher level of confidence ...
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Estimation of tail-related value-at-risk measures: range-based extreme value approach
Quantitative Finance, 2013This study proposes a new approach for estimating value-at-risk (VaR). This approach combines quasi-maximum-likelihood fitting of asymmetric conditional autoregressive range (ACARR) models to estimate the current volatility and classical extreme value theory (EVT) to estimate the tail of the innovation distribution of the ACARR model.
Heng-Chih Chou, David K. Wang
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