Forecasting Volatility with Many Predictors
學年 101
學期 2
出版(發表)日期 2013-07-19
作品名稱 Forecasting Volatility with Many Predictors
著者 Ke, Tsung-han
著錄名稱、卷期、頁數 Journal of Forecasting 32(8),  p.743-754
摘要 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.
關鍵字 conditional heteroskedasticity;dimension reduction;GARCH model;risk management;S&P 500 Index
語言 en
ISSN 1099-131X
期刊性質 國外
收錄於 SSCI
國別 GBR
出版型式 ,電子版

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