Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models With Student<i>t</i>Innovations

Fiorentini, Gabriele; Sentana, Enrique; Calzolari, Giorgio

doi:10.1198/073500103288619232

Cited by 133 publications

(98 citation statements)

References 24 publications

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“…Alternatively, the p-value discrepancy plot (i.e. plotting b P p it (y % ) y % against y % ) can be more revealing when it is necessary to discriminate among speci…cations that perform similarly in terms of the p-value plot (see Fiorentini et al, 2003). Consequently, under correct density speci…cation, the variable b P p it (y % ) y % must converge to zero.…”

Section: =2mentioning

confidence: 99%

“…Furthermore, the MGCII cdf can be easily worked out as shown in equation (19) (see Proof 5 in the Appendix), and consequently, they can be used easily for risk management purposes, either for modelling and forecasting credit risk, portfolio VaR or short-fall probabilities. The multivariate cdf of the MGCI can be obtained analogously in terms of the cdf of the univariate N(0,1) and univariate SNP distributions, see León et al (2007) for further details.…”

Section: Multivariate Gram-charlier Densitiesmentioning

confidence: 99%

See 1 more Smart Citation

Gram–Charlier densities: a multivariate approach

Brío

Ñíguez²,

Perote

2009

Quantitative Finance

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Section: =2mentioning

confidence: 99%

Section: Multivariate Gram-charlier Densitiesmentioning

confidence: 99%

Gram–Charlier densities: a multivariate approach

Brío

Ñíguez²,

Perote

2009

Quantitative Finance

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“…distribution for the standardized innovations. For details on the T-student density estimation for MGARCH models, see Fiorentini, Sentana, and Calzolari (2003). Table 2 also presents the sample autocorrelation functions for the returns and squared-returns series up to two lags and the Ljung-Box (LB) statistics up to 6 and 12 lags.…”

Section: Datamentioning

confidence: 99%

How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets

Hernandez¹,

Ibarra²,

Trupkin³

2013

European Review of Agricultural Economics

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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. Abstract: This paper examines the level of interdependence and volatility transmission in global agricultural futures markets. We follow a multivariate GARCH approach to explore the dynamics and cross-dynamics of volatility across major exchanges of corn, wheat, and soybeans between the United States, Europe, and Asia. We account for the potential bias that may arise when considering exchanges with different closing times. The results indicate that agricultural markets are highly interrelated and there are both own-and cross-volatility spillovers and dependence among most of the exchanges. The results also show the major role Chicago plays in terms of spillover effects over the other markets, particularly for corn and wheat. Additionally, the level of interdependence between exchanges has only increased in recent years for some of the commodities. Keywords: Volatility transmission, agricultural commodities, futures markets, Multivariate GARCH. JEL Classification: Q02, G15, Q11, C32. Terms of use: Documents inResumen: En este trabajo se analiza el nivel de interdependencia y transmisión de volatilidad en los mercados internacionales de futuros agrícolas. Se sigue un enfoque de GARCH multivariado para explorar la dinámica y la dinámica cruzada de la volatilidad para los principales mercados de maíz, trigo y soya en Estados Unidos, Europa y Asia. Se toma en cuenta el posible sesgo que puede surgir cuando se consideran mercados con diferentes horarios de cierre. Los resultados indican que los mercados agrícolas se encuentran altamente interrelacionados y que existen derrames propios y cruzados de volatilidad y dependencias entre la mayoría de los mercados. Los resultados indican además el importante papel que juega Chicago en términos de efectos de derrame de volatilidad sobre los otros mercados, particularmente para maíz y trigo. Adicionalmente, el nivel de interdependencia entre los mercados sólo se ha incrementado en años recientes para algunos de los productos primarios. Palabras Clave: Transmisión de volatilidad, productos primarios agrícolas, mercados de futuros, GARCH multivariado.

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“…According to Fiorentini et al (2003), it follows from the fact that the estimator ξ i cannot be negative that, under the null, √ T N i=1 ξ i will converge in distribution to a normal variable which is truncated at zero. As a result, the probability that (21) exceeds a given critical value under H 0 will be just half of the probability that a χ 2 (1) distributed variable does so.…”

Section: Data and Model Estimatesmentioning

confidence: 99%