Price Volatility Forecast for Agricultural Commodity Futures： The Role of High Frequency Data
AbstractRealized measures of volatility based on high frequency data contain valuable information about the unobserved conditional volatility. In this paper, we use the Realized GARCH model developed by Hansen, Huang and Shek (2012) to estimate and forecast price volatility for four agricultural commodity futures. Empirical evidences, both in-sample and out-of-sample, show that the Realized GARCH model and its variants outperform the conventional volatility models that only use daily price data, such as GARCH and EGARCH. We also consider skewed student’s t-distribution to account for the skewness and fat-tail in the agricultural futures prices. The empirical performances are relatively close for models using three different realized measures, as the measurement equation in the Realized GARCH model can adjust to the different realized measures to some extent.
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Bibliographic InfoArticle provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.
Volume (Year): (2012)
Issue (Month): 4 (December)
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More information through EDIRC
High Frequency Data; Fat-tail; Skewness; Realized Volatility; Agricultural Futures;
Find related papers by JEL classification:
- C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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