Robust conditional variance estimation and value-at-risk

Guermat, Cherif; Harris, Richard

doi:10.21314/jor.2002.060

Cited by 58 publications

(72 citation statements)

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“…Note that for the normal distribution, the SD-EWMA scheme coincides with the standard EWMA for volatility modeling. As explained in Section 2, the SD-EWMA updating schemes (11) and (15) based on the Laplace and asymmetric Laplace distribution, respectively, are very close to the robust EWMA scheme (9) of Guermat and Harris [2002], and the skewed EWMA scheme(13) of Gerlach et al [2013], respectively. For the dynamic asymmetric Laplace, we use the same dynamics for p t in (14) as used in Gerlach et al…”

Section: Data and Descriptive Statisticsmentioning

confidence: 53%

“…We also show that the SD-EWMA approach encompasses other proposals from the literature to model time-varying parameters, such as the normal based standard EWMA, the robust EWMA of Guermat and Harris [2002] based on the Laplace distribution, and the skewed EWMA of Gerlach et al [2013] based on the asymmetric Laplace distribution. Given that we are interested in modeling the time variation in financial risk measures, we explicitly develop an SD-EWMA model based on the fat-tailed skewed Student's t distribution; see for example Poon and Granger [2003] for stylized facts about financial returns.…”

Section: Introductionmentioning

confidence: 81%

“…Compared to previous methods, such as Jensen andLunde [2001] andWilhelmsson [2009], the SD-EWMA approach has the distinct advantage that it provides a unifying framework that embeds previous proposals from the literature, such as Guermat and Harris [2002] and Gerlach et al [2013]. In addition, the generality of the SD-EWMA approach also allows for a straightforward generalization to higher dimensions, estimating score-driven versions of both volatilities, covariances and correlations, and other higherorder moments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting

Lucas

Zhang

2014

SSRN Journal

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Section: Data and Descriptive Statisticsmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting

Lucas

Zhang

2014

SSRN Journal

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“…Therefore, the calculation based on the normal distribution assumption may underestimate the risk. Hull & White (1998) and Guermat & Harris (2001) use non-normal distributions and resolve the problem of fat tail.…”

Section: Literature Reviewmentioning

confidence: 99%

Does the VaR Measurement Using Monte-Carlo Simulation Work in China?—Evidence from Chinese Listed Banks

Wang¹,

Song²,

Lin³

2017

JFRM

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There are usually great demands for risk control in the banking industry. Value at risk (VaR) is an important risk measurement in the Basel Accords, and Monte-Carlo simulation is a common method for VaR measurement. We conduct a series of Monte-Carlo simulation for VaR measurement based on the banks listed in the China stock market. Our study thinks that it is reliable to use Monte-Carlo simulation to measure VaR in Chinese banks. Therefore, we think that such VaR measurement works in China.

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“…This indicates a possible inadequacy of an assumed normal distribution. Guermat and Harris (2002) have shown that the EM A-based V aR forecasts are excessively volatile and unnecessarily high, when returns are not conditionally normal.…”

Section: Introductionmentioning

confidence: 99%

Value-at-Risk and Expected Shortfall When There Is Long Range Dependence

Härdle

Mungo

2008

SSRN Journal

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Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and forecasting of market volatility. The implication is that models that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long-term contracts. On the other hand, recent focus is on whether long memory can affect the measurement of market risk in the context of Value-atRisk (V aR). In this paper, we evaluate the Value-at-Risk (V aR) and Expected Shortfall (ESF ) in financial markets under such conditions. We examine one equity portfolio, the British F T SE100 and three stocks of the German DAX index portfolio (Bayer, Siemens and Volkswagen). Classical V aR estimation methodology such as exponential moving average (EM A) as well as extension to cases where long memory is an inherent characteristics of the system are investigated. In particular, we estimate two long memory models, the Fractional Integrated Asymmetric Power-ARCH and the Hyperbolic-GARCH with different error distribution assumptions. Our results show that models that account for asymmetries in the volatility specifications as well as fractional integrated parametrization of the volatility process, perform better in predicting the one-step as well as five-step ahead V aR and ESF for short and long positions than short memory models. This suggests that for proper risk valuation of options, the degree of persistence should be investigated and appropriate models that incorporate the existence of such characteristic be taken into account.JEL classification: C14, C32, C52, C53, G12

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Robust conditional variance estimation and value-at-risk

Cited by 58 publications

References 0 publications

Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting

Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting

Does the VaR Measurement Using Monte-Carlo Simulation Work in China?—Evidence from Chinese Listed Banks

Value-at-Risk and Expected Shortfall When There Is Long Range Dependence

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