Structural GARCH: The Volatility-Leverage Connection

Engle, Robert F.; Siriwardane, Emil

doi:10.2139/ssrn.2596622

Cited by 10 publications

(6 citation statements)

References 44 publications

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“…Engle and Siriwardane (2014) discuss how the leverage adjustment is increased if the asset-price distribution has a fat tail, which is consistent with our modification of the Merton model (using a local volatility which reflects the size of the fat tail).19 Schaefer and Strebulaev (2008) find that Merton's model generates plausible hedge ratios for corporate bonds. That would require that changes in risk-neutral volatilities (Q domain) are of similar magnitude to changes in physical volatilities (P domain), as we have confirmed here.…”

supporting

confidence: 67%

Explaining CDS prices with Merton’s model before and after the Lehman default

Gemmill

Marra

2019

Journal of Banking & Finance

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We examine whether CDS prices around the Credit Crisis can be explained with Merton's model. First we invert the model with market prices to reveal skewed volatility smiles over the whole 2005-2012 period. Then we calibrate the model to pre-Crisis data in two novel ways that allow for skewness, one based on equity-index options (MEIV) and the other on the sensitivity of CDS prices to equity volatility (MSKEW). In out-of-sample forecasts both calibrations match the in-Crisis peak of prices, but the second is better at capturing the systematic component of prices thereafter. Average CDS prices remain at twice their pre-Crisis level long after that event; the MSKEW calibration demonstrates that this is due to extra idiosyncratic risks, which are important for some firms but have negligible impact on others.

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confidence: 67%

Explaining CDS prices with Merton’s model before and after the Lehman default

Gemmill

Marra

2019

Journal of Banking & Finance

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“…The author shows that the approach used by software packages is not consistent with the Nelson-Cao inequality constraints for ensuring positivity of conditional variance. More recently, Engle and Siriwardane (2018) have introduced the Structural GARCH model where leverage is referred to the capital structure of a firm, a totally different concept with respect to the volatility asymmetry effect. In this note we argument that the asymmetry and leverage effects within a GARCH model are not identical.…”

Section: Asymmetry and Leverage In Garch Modelsmentioning

confidence: 99%

Asymmetry and leverage in GARCH models: a News Impact Curve perspective

Caporin

Costola

2019

Applied Economics

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Models for conditional heteroskedasticity belonging to the GARCH class are now common tools in many economics and finance applications. Among the many possible competing univariate GARCH models, one of the most interesting groups allows for the presence of the so-called asymmetry or leverage effect. In our view, asymmetry and leverage are two distinct phenomena, both inspired by the seminal work of Black (1976). We propose definitions of leverage and asymmetry that build on the News Impact Curve, allowing to easily and coherently verify if they are both present. We show that several GARCH models are asymmetric but none is allowing for a proper leverage effect. Finally, we extend the leverage definition to a local leverage effect and show that the AGARCH model of Engle ( 1990) is coherent with the presence of local leverage. An empirical analysis completes the paper.

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“…( 2010 ), Culp et al. ( 2018 ) and Engle and Siriwardane ( 2018 ) our paper is also capable of capturing volatility clustering. Engle and Siriwardane ( 2018 ) develops a structural GARCH model by combining the Merton structural credit risk framework and the Glosten et al.…”

Section: Introductionmentioning

confidence: 60%

“…Like the papers by Balachandran et al (2010), Culp et al (2018 and Engle and Siriwardane (2018) our paper is also capable of capturing volatility clustering. Engle and Siriwardane (2018) develops a structural GARCH model by combining the Merton structural credit risk framework and the Glosten et al (1993) GARCH specification to capture equity volatility asymmetry-the well-known negative correlation between equity returns and equity volatility. Culp et al (2018) introduces a novel, model-free benchmarking methodology to the empirical Merton model.…”

Section: Introductionmentioning

confidence: 82%

“…To this end, the literature has incorporated new features into the Merton model such as jumps in asset values, stochastic volatility, endogenous default. Several papers (Jessen and Lando 2015 ; Engle and Siriwardane 2018 ) have highlighted the power of volatility dynamics in explaining default probability. To capture the volatility dynamics observed during market downturns adequately, we introduce time-varying volatility via an appropriate GARCH model.…”

Section: Introductionmentioning

confidence: 99%

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Estimating volatility clustering and variance risk premium effects on bank default indicators

Kenç

Çevik

2021

Rev Quant Finan Acc

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Default risk increases substantially during financial stress times due to mainly the two reasons: volatility clustering and investors’ desire to protect themselves from such increases in volatility. It manifested in the aftermath of the Global Financial Crisis of 2008–2009 with unpleasant outcomes of many bankruptcies and severe financial distress. To account for these features, we adapted the structural credit risk approach to include both time-varying (return) volatility and risk premium about the return volatility itself. By applying the model to US banks, we obtain better bank default indicators in comparison to the benchmark models.

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Structural GARCH: The Volatility-Leverage Connection

Cited by 10 publications

References 44 publications

Explaining CDS prices with Merton’s model before and after the Lehman default

Explaining CDS prices with Merton’s model before and after the Lehman default

Asymmetry and leverage in GARCH models: a News Impact Curve perspective

Estimating volatility clustering and variance risk premium effects on bank default indicators

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