This study investigates the forecasting performance of the GARCH(1,1) model by adding an effective covariate. Based on the assumption that many volatility predictors are available to help forecast the volatility of a target variable, this study shows how to construct a covariate from these predictors and plug it into the GARCH(1,1) model. This study presents a method of building a covariate such that the covariate contains the maximum possible amount of predictor information of the predictors for forecasting volatility. The loading of the covariate constructed by the proposed method is simply the eigenvector of a matrix. The proposed method enjoys the advantages of easy implementation and interpretation. Simulations and empirical analysis verify that the proposed method performs better than other methods for forecasting the volatility, and the results are quite robust to model misspecification. Specifically, the proposed method reduces the mean square error of the GARCH(1,1) model by 30% for forecasting the volatility of S&P 500 Index. The proposed method is also useful in improving the volatility forecasting of several GARCH‐family models and for forecasting the value‐at‐risk. Copyright © 2013 John Wiley & Sons, Ltd.

As science and technology policy-makers focus on understanding how industry networks evolve, this study discusses how environmental changes affect the evolution of industrial networks, especially in the flat panel display (FPD) sector. By investigating different environmental scenarios, it can be shown that changes of environmental munificence and environmental uncertainty simultaneously affect industrial network transition and reshape distinctive network formations. Data from 71 countries from 1976 to 2008 have been examined to empirically evaluate their network relationships. This study, thus, demonstrates a longitudinal evolutionary trajectory for the FPD industry and locates the essential transition of technological competition via international technological coevolution. The analytical results demonstrate that different environmental scenarios provide different opportunities for countries to develop their niche competence in the FPD industry. Our findings provide insightful implications for policy-makers and business practitioners involved in dynamic and competitive global industries similar to FPD.

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