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

By Gabriele Fiorentini, Enrique Sentana, Giorgio Calzolari. 2003

155 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5b7194d56d2b9f92746ffb7fd96a51c0b9cecfa4

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Citations in the corpus

Estimation and testing of dynamic models with generalised hyperbolic innovations (2005, methodology): “…it would be interesting to develop sequential estimators of the asymmetry and kurtosis parameters introduced by the GH assumption, which would keep constant the conditional mean and variance parameters at their Gaussian PML estimators along the lines of Fiorentini, Sentana, and Calzolari (2003b).”
Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications (2017, unknown): “6 Forbes and Rigobon (2002) define the contagion as a significant increase in crossmarket linkages after a shock to one country (or a group of countries).”
A Comparison of Mean-Variance Efficiency Tests (2008, methodology): “Finally, to draw asymmetric t innovations we first generate a univariate Gamma and N independent standard Gaussian variates, and then use the decomposition presented in (14).”
Conditional Volatility and Correlations of Weekly Returns and the VaR Analysis of 2008 Stock Market Crash (2010, methodology): “In the non-mean reverting case these intercept coe¢ cients disappear, but for the initialization of the recursive relations (2) and (12) it is still advisable to use unconditional estimates of the correlation matrix and asset returns volatilities.”
Exchange return co-movements and volatility spillovers before and after the introduction of euro (2012, methodology): “The DCC model is estimated using the Quasi-Maximum Likelihood (QML) estimator under a multivariate Student distribution (see Harvey et al., 1992; Fiorentini et al., 2003).”
Analytical quasi maximum likelihood inference in multivariate volatility models (2003, methodology): “Fiorentini et al. (2003) provide a general framework for maximum likelihood estimation using the Student-t distribution.”
Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries (2011, methodology): “The multivariate Student-t distribution was first proposed in the estimation of multivariate ARCH models by Harvey et al. (1992) and Fiorentini et al. (2003).”