Multivariate realized volatility forecasts of agricultural commodity futures |
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Authors: | Jiawen Luo Langnan Chen |
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Affiliation: | 1. School of Business Administration, South China University of Technology, Guangzhou, China;2. Lingnan (University) College, Sun Yat-sen University, Guangzhou, China |
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Abstract: | We forecast the multivariate realized volatility of agricultural commodity futures by constructing multivariate heterogeneous autoregressive (MHAR) models with flexible heteroscedastic error structures that allow for non-Gaussian distribution, stochastic volatility, and heteroscedastic and serial dependence. We evaluate the forecast performances of various models based on both statistical and economic criteria. The in-sample and out-of-sample results suggest that the proposed MHAR models allowing for flexible heteroscedastic covariance structures outperform the benchmark MHAR models. In addition, the proposed Bayesian MHAR models allowing for t innovations improve both in-sample and out-of-sample forecast performance of the corresponding MHAR models with Gaussian innovations. |
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Keywords: | flexible covariance structure MHAR models multivariate volatility forecasts performance evaluations |
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