In this article we propose a new multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model with time-varying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. Whereas each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analog. Our new model retains the intuition and interpretation of the univariate GARCH model and yet satis es the positive-de nite condition as found in the constant-correlation and Baba-Engle-Kraft-Kroner models. We report some Monte Carlo results on the nite-sample distributions of the maximum likelihood estimate of the varying-correlation MGARCH model. The new model is applied to some real data sets.

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Purpose – This paper aims to identify the antecedents of firm’s supply chain agility (SC agility) and how SC agility impacts on firm’s performance. Design/methodology/approach – Based on a comprehensive literature review, a conceptual model was proposed, in which the interrelated hypotheses were tested by structural equation modelling methodology using a dataset collected from 266 Chinese electronics firms. Findings – Initially, it was found that SC integration and external learning positively influenced SC agility. Second, the results indicated that firm’s performance is positively impacted by SC agility. Moreover, SC agility also fully mediated the effect of SC integration on firm’s performance and the effect of external learning on firm’s performance. Research limitations/implications – The generalizability of this research sample might be the major limitation of this study. Therefore, future research can adopt other industry sectors samples, such as automobile manufacturing, or other country samples to validate the research model. Practical implications – This research outlines strategies for better preparedness to achieve SCs to be agile which is a core competency of electronic firms in emerging market. Findings reveal that the external coordination practices – external learning and SC integration – are important factors of SC agility. In addition, the findings contribute to understanding the important role of SC agility in improving firm’s performance. Originality/value – This research examines the impact of two antecedents (i.e. SC integration and external learning) on SC agility and is the first empirical research to analyze the mediation effect of SC agility on the relationship between SC integration and firm performance and the relationship between external learning and firm performance.

In this paper we propose a new multivariate GARCH model with timevarying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. While each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analogue. By imposing some suitable restrictions on the conditional-correlation-matrix equation, we manage to construct a MGARCH model in which the conditional-correlation matrix is guaranteed to be positive de¯nite during the optimisation. Thus, our new model retains the intuition and interpretation of the univariate GARCH model and yet satis¯es the positive-de¯nite condition as found in the constant-correlation and BEKK models. We report some Monte Carlo results on the¯nite-sample distributions of the QMLE of the varying-correlation MGARCH model. The new model is applied to some real data sets. It is found that extending the constant-correlation model to allow for time-varying correlations provides some interesting time histories that are not available in a constant-correlation model.

This paper compares the performances of the hedge ratios estimated from the OLS (ordinary least squares) method and the constant-correlation VGARCH (vector generalized autoregressive conditional heteroscedasticity) model. These methods are evaluated based on the out-of-sample optimal hedge ratio forecasts. A systematic comparison is provided by examining ten spot and futures markets covering currency futures, commodity futures and stock index futures. Using a recently proposed test (Tse, 2000) for the constant-correlation assumption, it is found that the assumption cannot be rejected for eight of the ten series. To gain the maximum benefit of a time-varying hedging strategy the estimation data is kept up-to-date for the re-estimation of the hedge ratios. Both the constant hedge ratio (using OLS) and the timevarying hedge ratio (using constant-correlation VGARCH) are re-estimated on a day-by-day rollover, and the post-sample variances of the hedged portfolios are examined. It is found that the OLS hedge ratio performs better than the VGARCH hedge ratio. This result may be another indication that the forecasts generated by the VGARCH models are too variable.