Stock Market Volatility and Macroeconomic Fundamentals

Engle, Robert F.; Ghysels, Éric; Sohn, Bumjean

doi:10.1162/rest_a_00300

Cited by 820 publications

(689 citation statements)

References 38 publications

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“…Other researchers have used Mixed-data sampling methods (MIDAS), as in Ghysels et al (2006), Ghysels et al (2007). One example is Engle et al (2013) that analyses the relation between stock market volatility and macroeconomic activity since the 19th century, distinguishing short-run from secular movements. They use the MIDAS approach to link the monthly, quarterly, or bi-annual macroeconomic variables to the secular component and a mean reverting daily GARCH process for the short-run movements.…”

Section: Introductionmentioning

confidence: 99%

In search of the determinants of European asset market comovements

Gomes

Taamouti

2016

International Review of Economics & Finance

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. The information improves the portfolio performance compared to an equally weighted portfolio.JEL Classi…cation: C22, G12, G15, G17, E44.

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Section: Introductionmentioning

confidence: 99%

In search of the determinants of European asset market comovements

Gomes

Taamouti

2016

International Review of Economics & Finance

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“…In Section 2.1, we first introduce the GARCH-MIDAS specification of Engle et al (2013) and then discuss the null hypothesis of our test. We derive the likelihood function and the test indicator in Section 2.2 and present our main result on the asymptotic distribution of the test statistic in Section 2.3.…”

Section: Model and Test Statisticmentioning

confidence: 99%

“…The terminology of decomposing σ 2 0t into a short-and a long-term component follows Engle et al (2013). In our setting, the long-term component is the one that is driven by (exogeneous) explanatory variables and, typically, is smoother than the short-term component.…”

Section: The Garch-midas Modelmentioning

confidence: 99%

“…We develop a misspecification test for the multiplicative two-component GARCH-MIDAS model suggested in Engle et al (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly timevarying long-term component which is driven by the dynamics of an explanatory variable.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, an increasing amount of empirical studies has employed the GARCH-MIDAS framework introduced by Engle et al (2013) (see, e.g., Asgharian et al, 2013, Conrad and Loch, 2014, 2015, Dorion, 2013, Opschoor et al, 2014. In a GARCH-MIDAS specification, the conditional variance consists of two multiplicative components, whereby economic conditions enter through the smooth long-term component around which a short-term unit variance GARCH component fluctuates.…”

Section: Introductionmentioning

confidence: 99%

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Misspecification Testing in GARCH-MIDAS Models

Conrad

Schienle

2015

SSRN Journal

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We develop a misspecification test for the multiplicative two-component GARCH-MIDAS model suggested in Engle et al. (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly timevarying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange Multiplier statistic for testing the null hypothesis that the variable has no explanatory power. Hence, under the null hypothesis the long-term component is constant and the GARCH-MIDAS reduces to the simple GARCH model. We derive the asymptotic theory for our test statistic and investigate its finite sample properties by Monte-Carlo simulation. The usefulness of our procedure is illustrated by an empirical application to S&P 500 return data.

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2019

GARCH Models

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Stock Market Volatility and Macroeconomic Fundamentals

Cited by 820 publications

References 38 publications

In search of the determinants of European asset market comovements

In search of the determinants of European asset market comovements

Misspecification Testing in GARCH-MIDAS Models

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