The Effect of Asymmetries on Stock Index Return Value‐at‐Risk Estimates

Brooks, Chris; Persand, Gita

doi:10.1108/eb022959

Cited by 71 publications

(46 citation statements)

References 44 publications

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“…APARCH) performed better overall. Brooks and Persand (2003) concluded that the models which do not allow for asymmetries underestimate the true VaR.…”

Section: Introduction and Review Of The Literaturementioning

confidence: 99%

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

2017

International Review of Financial Analysis

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“…APARCH) performed better overall. Brooks and Persand (2003) concluded that the models which do not allow for asymmetries underestimate the true VaR.…”

Section: Introduction and Review Of The Literaturementioning

confidence: 99%

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

2017

International Review of Financial Analysis

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“…Brooks and Persand (2003) pointed out that models, which do not allow for asymmetries either in the unconditional return distribution or in the volatility specification, underestimate the "true" VaR. Giot and Laurent (2003a) proposed the skewed Student's-t distribution and argued that it performed better than the pure symmetric one, as it reproduced the characteristics of the empirical distribution more accurately.…”

Section: E M P I R I C a L R E S U L T Smentioning

confidence: 99%

“…Furthermore, Brooks and Persand (2003) also concluded that the asymmetry is an important issue in the VaR framework and therefore it must be modeled either in the unconditional mean return distribution or in the volatility specification.…”

Section: Introductionmentioning

confidence: 99%

Modeling risk for long and short trading positions

Angelidis¹,

Degiannakis

2005

The Journal of Risk Finance

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Purpose -Aims to investigate the accuracy of parametric, nonparametric, and semiparametric methods in predicting the one-day-ahead value-at-risk (VaR) measure in three types of markets (stock exchanges, commodities, and exchange rates), both for long and short trading positions. Design/methodology/approach -The risk management techniques are designed to capture the main characteristics of asset returns, such as leptokurtosis and asymmetric distribution, volatility clustering, asymmetric relationship between stock returns and conditional variance, and power transformation of conditional variance. Findings -Based on back-testing measures and a loss function evaluation method, finds that the modeling of the main characteristics of asset returns produces the most accurate VaR forecasts. Especially for the high confidence levels, a risk manager must employ different volatility techniques in order to forecast accurately the VaR for the two trading positions. Practical implications -Different models achieve accurate VaR forecasts for long and short trading positions, indicating to portfolio managers the significance of modeling separately the left and the right side of the distribution of returns. Originality/value -The behavior of the risk management techniques is examined for both long and short VaR trading positions; to the best of one's knowledge, this is the first study that investigates the risk characteristics of three different financial markets simultaneously. Moreover, a two-stage model selection is implemented in contrast with the most commonly used back-testing procedures to identify a unique model. Finally, parametric, nonparametric, and semiparametric techniques are employed to investigate their performance in a unified environment.

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“…A number of applications of Value-at-risk (V aR) on financial asset returns have shown that due to excess kurtosis, estimation under the assumption of normally distributed residuals tend to underestimate or overestimate the true V aR. Brooks and Persand (2003a) showed that models that do not allow for asymmetries in the unconditional distribution or volatility specification, underestimate the true V aR and are most likely to generate inaccurate forecasts. Giot and Laurent (2004) pointed out that the V aR for daily stock indexes with a skewed student-t distribution performed better than with pure symmetric distribution.…”

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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The Effect of Asymmetries on Stock Index Return Value‐at‐Risk Estimates

Cited by 71 publications

References 44 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

Modeling risk for long and short trading positions

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

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