Value-at-Risk for long and short trading positions

By P. Giot, Sébastien-Yves Laurent. 2003

389 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/64cdbbf8bc17f68fdb93e925d9c52b1ec489ab0f

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Citations in the corpus

Market risk in commodity markets: a VaR approach (2003, background): “Recent examples are the books by Dowd(1998) and1 Jorion (2000) or the papers by van den Goorbergh and Vlaar(1999), Danielsson and de Vries(2000), Vlaar (2000) and Giot and Laurent(2003).”
Developing a stress testing framework based on market risk models (2008, methodology): “Giot and Laurent (2003) apply a similar approach in equity markets and Angelidis et al. (2004) apply the simpler tEGARCH model for estimating VaR in equity markets.”
Frontiers in VaR forecasting and backtesting (2016, background): “…distributions for standardized returns are the Gaussian, the Student and the Skewed-Student distribution of Hansen (1994); see, for example, Giot and Laurent (2003), Kuester et al. (2006), Bali and Theodosiou (2007), Sajjad et al. (2008), Pérignon and Smith (2010a), Halbleib and Pohlmeier…”
The Hidden Dangers of Historical Simulation (2006, neutral): “5 The Skewed Student Asymmetric Power ARCH (Skewed Student APARCH) model is probably better than the GARCH(1,1) model for fitting the conditional heteroskedasticity of exchange rates (Mittnik and Paolella, 2000) or equity indices (Giot and Laurent, 2003), but it is less tractable.”
The Use of GARCH Models in VaR Estimation (2004, methodology): “[41] P. Lambert, S. Laurent, Modelling financial time series using GARCH-type models and a skewed student density, Universite de Liege, (2001) Mimeo.”
Forecasting Value-at-Risk under Different Distributional Assumptions (2016, background): “Specifically, we consider three symmetric distributions, i.e., normal, Student and Multivariate Exponential Power (MEP), and their corresponding skewed counterparts obtained by applying the transformation of Bauwens and Laurent [13] 1.”
Modelling and forecasting volatility in the gold market (2012, methodology): “Giot and Laurent (2001, 2003) investigate volatility models for both negative and positive returns, with the latter representing risk for short position holders. They find that skewed asymmetric ARCH models using the Student t distribution perform best with respect to risk quantification. Sadorsky (”
A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models (2005, background): “…that because the distribution assumption is crucial to obtaining accurate VaR forecasts in a univariate framework (see Mittnik and Paolella 2000; Giot and Laurent 2003, among others), one can conjecture that this will also be true in the multivariate case, which calls for the use of a flexible…”
Estimation and Decomposition of Downside Risk for Portfolios with Non-Normal Returns (2008, positive): “This statement has been empirically verified by Giot and Laurent (2003).”
Global financial crisis and spillover effects among the U.S. and BRICS stock markets (2016, methodology): “Following Giot and Laurent (2003), testing the accuracy of the model is equivalent to testing the hypothesis fH fH : : 1 0 .”
Value at Risk with time varying variance, skewness and kurtosis--the NIG-ACD model (2009, positive): “Since all the models have a variance equation equal to or nested by the APARCH model we can use the weak stationarity condition aE [(j tj t) ]+b < 1 from Giot and Laurent (2003). All models are found to be covariance stationary.”
Power arch modelling of the volatility of emerging equity markets (2007, positive): “Giot and Laurent (2003) use a skewed Student-t version of the APARCH model to estimate value at risk for three US stocks and three international markets (US, UK, Japan).”