Estimating stock market volatility using asymmetric GARCH models

By Dima Alberg, Haim Shalit, Rami Yosef. 2008

318 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/eb131903b65b741798af77f77c8ad26e3af04127

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. John W. Galbraith
2. G. Ali
3. Dongming Zhu

In Finance

1. John W. Galbraith
2. G. Ali
3. Dongming Zhu

In Management Science and Operations Research

1. John W. Galbraith
2. G. Ali

Citations in the corpus

• EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites (2013, methodology): “Alberg, Shalit and Yosef [19] showed that the GARCH models with fat-tail distributions are relatively better suited for analyzing returns on stocks.”
• Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions (2011, background): “…data sets has allowed the estimation and exploitation of these asymmetrical and heavy-tailed models, and empirical investigation has found that such extensions can be useful in characterizing the pattern of financial returns; see for example Mittnik and Paolella (2003) and Alberg et al. (2008).”
• Corporate social responsibility and stock market performance (2006, unknown): “…(LM) test on model residuals does not reject the hypothesis of conditional heteroskedasticity.12 This finding coupled with the excess kurtosis of portfolio returns (Table 4) leads us to estimate a GARCH (1, 1) model 11 See Choi and Kim (1991), Salin et al. (2002) and Alberg et al. (2008).”