Revisiting Several Popular GARCH Models with Leverage Effect: Differences and Similarities

Rodríguez, María José Blanco; Ruiz, Esther

doi:10.1093/jjfinec/nbs003

Cited by 64 publications

(29 citation statements)

References 38 publications

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“…Duffee (1995) also observed that using a broader set of sample size will make the results in Christie (1982) disappear. Other tools used in testing Black's hypothesis include conditional heteroscedasticity models (Bollerslev, Litvinova, & Tauchen, 2006;Cheung & Ng, 1992;Engle & Ng, 1993;Linton & Mammen, 2005;Long et al, 2014;Nelson, 1991;Rodriguez & Ruiz, 2012), a nonparametric measure of conditional distributional dominance (Linton et al, 2016), a panel vector autoregression model (Ericsson et al, 2016), and using Fama and French risk factors in the EGARCH process to estimate the leverage effect parameter (Adami, Gough, Muradoglu, & Sivaprasad, 2010;Smith, 2015). Adami et al (2010) have also confirmed the existence of an inverse relationship between stock returns and leverage but became weaker when Fama-French-Carhart's risk factors were used to estimate returns.…”

Section: Review Of Literaturementioning

confidence: 99%

Investigating the sources of Black’s leverage effect in oil and gas stocks

Sanusi

2017

Cogent Economics & Finance

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The Black's leverage effect hypothesis postulates that a negative stock return innovation increases the financial leverage of a firm since the value of equity decreases at a given level of debt, which, in turn, creates a higher equity return volatility in the future. The paper is aimed at investigating the authenticity of the Black's leverage effect hypothesis and the relationship between negative stock returns and the financial leverage of the UK oil and gas stocks from 2004 to 2015. For each stock, exponential generalised autoregressive conditional heteroscedasticity model was estimated using Fama-French-Carhart 4-factor asset pricing model to extract the difference between the effects of negative and positive stock return innovations, regarded as leverage effect. The leverage effect parameter was further regressed on the financial leverage ratios of the book value of long-term debt to total assets, interest expenses to total assets and long-term debt to market value of equity to examine whether variation in the leverage parameter was as a result of variation in the firm's financial leverage. The findings of the study show that Fama-FrenchCarhart four risk factors of market, size effect, value and momentum were significant in the stock returns of most of the oil and gas companies. The mixed results in the significance level of the factors were attributed to the differences in individual firm characteristics. An evidence of leverage effect was also found in all the oil and gas stock returns but no evidence to suggest it was derived from the changes in the

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Section: Review Of Literaturementioning

confidence: 99%

Investigating the sources of Black’s leverage effect in oil and gas stocks

Sanusi

2017

Cogent Economics & Finance

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“…We also experiment with the GJR-GARCH conditional variance specification of Glosten et al [11] containing leverage effects (see Rodríguez and Ruiz [16] and McAleer [17]). The GJR(1,1,1) equation is:…”

Section: Modelsmentioning

confidence: 99%

“…In the GJR specification, the value of the additional (leverage) parameter equals γ = 0.1, and persistence is maintained at ρ = 0.99. For such parameter combinations, the fourth moment does not exist even if ε t is mesokurtic (see Rodríguez and Ruiz [16]). …”

Section: Data Generationmentioning

confidence: 99%

Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting

Anatolyev

Khrapov

2015

Econometrics

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Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the return distribution affect the quality of estimation of the volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions are more precisely estimated when no variance targeting is employed. Bias properties are exacerbated for a heavier-tailed distribution of standardized returns, while the distributional asymmetry has little or moderate impact, these phenomena tending to be more pronounced under variance targeting. Some effects further intensify if one uses ML based on a leptokurtic distribution in place of normal QML. The sample size has also a more favorable effect on estimation precision when no variance targeting is used. Thus, if computational costs are not prohibitive, variance targeting should probably be avoided.

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“…These typically involve fitting of a general parametric or semiparametric model to conditional volatility and then testing the implied restrictions on parameters or curves, see for example Nelson (1991), Engle and Ng (1993), Linton and Mammen (2005), and Rodriguez and Ruiz (2012). Most authors have found that the parameters governing asymmetric volatility response in daily individual stock returns and in indexes to be statistically significant.…”

Section: Introductionmentioning

confidence: 99%

A nonparametric test of a strong leverage hypothesis

Linton

Whang

Yen

2016

Journal of Econometrics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractThe so-called leverage hypothesis is that negative shocks to prices/returns affect volatility more than equal positive shocks. Whether this is attributable to changing financial leverage is still subject to dispute but the terminology is in wide use. There are many tests of the leverage hypothesis using discrete time data. These typically involve fitting of a general parametric or semiparametric model to conditional volatility and then testing the implied restrictions on parameters or curves. We propose an alternative way of testing this hypothesis using realized volatility as an alternative direct nonparametric measure. Our null hypothesis is of conditional distributional dominance and so is much stronger than the usual hypotheses considered previously. We implement our test on a number of stock return datasets using intraday data over a long span. We find powerful evidence in favour of our hypothesis.

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Revisiting Several Popular GARCH Models with Leverage Effect: Differences and Similarities

Cited by 64 publications

References 38 publications

Investigating the sources of Black’s leverage effect in oil and gas stocks

Investigating the sources of Black’s leverage effect in oil and gas stocks

Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting

A nonparametric test of a strong leverage hypothesis

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