Multivariate GARCH Models: A Survey

Bauwens, Luc; Laurent, Sébastien; Rombouts, Jeroen V.K.

doi:10.2139/ssrn.411062

Cited by 465 publications

(512 citation statements)

References 96 publications

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“…The covariance stationary condition is checked for both models and all eigenvalues are found to be less than one in modulus (see tables 1 and 2). Further, the standardized residuals of the models are examined for autocorrelation by using the multivariate Portmanteau test (Bauwens et al 2005). Results indicate that both models are correctly specified (see tables 1 and 2).…”

Section: Methodological Approachmentioning

confidence: 91%

Price volatility in ethanol markets

Serra¹,

Zilberman²,

Gil³

2010

European Review of Agricultural Economics

153

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Our paper looks at how price volatility in the Brazilian ethanol industry changes over time and across markets. Demand and supply forces in the energy and food markets are likely to ensure that crude oil, ethanol and feedstock prices co-move in the long-run. Hence, when assessing price volatility changes and spillovers in the ethanol industry, one should also pay attention to the notion of cointegration. Until recently, the methods proposed to estimate cointegration relationships, have not explicitly considered time varying volatility in the data. Seo (2007) suggests an estimator of the cointegration vector that explicitly models conditional heteroskedasticity. More specifically, he proposes a maximum likelihood estimator that estimates the error correction model and the multivariate GARCH process jointly. We follow this proposal.World ethanol production is dominated by the US and Brazil. In 2006 worldwide production totaled 13,489 million gallons, with the US and Brazil producing, respectively, a 36 and a 33% of this quantity. Although both countries produce a conspicuous part of worldwide ethanol, their industries are not equally developed: while the Brazilian market is starting to

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Section: Methodological Approachmentioning

confidence: 91%

Price volatility in ethanol markets

Serra¹,

Zilberman²,

Gil³

2010

European Review of Agricultural Economics

153

View full text Add to dashboard Cite

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“…7 See Bauwens et al (2006) and Silvennoinen and Teräsvirta (2009) for an overview of (multivariate) GARCH models. Hansen and Lunde (2005); Laurent et al (2012) show that relatively simple models are…”

Section: Choosing the Time-series Modelmentioning

confidence: 99%

Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

Kole

Markwat²,

Opschoor

et al. 2017

Journal of Financial 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 December 2015 AbstractWe examine the impact of temporal and portfolio aggregation on the quality of Valueat-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly returns of all constituent assets separately, gathered into portfolios based on asset class, or into a single portfolio. We compare the impact of aggregation to that of choosing a model for the conditional volatilities and correlations, the distribution for the innovations and the method of forecast construction. We find that the degree of temporal aggregation is most important. Daily returns form the best basis for VaR forecasts. Modelling the portfolio at the asset or asset class level works better than complete portfolio aggregation, but differences are smaller. The differences from the model, distribution and forecast choices are also smaller compared to temporal aggregation.

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“…For this density, and based on the well-known orthogonality properties given in equations (5), (6) and (7), 4 Z…”

Section: Multivariate Gram-charlier Densitiesmentioning

confidence: 99%