Price Volatility Forecast for Agricultural Commodity Futures: The Role of High Frequency Data
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Cited by:
- Dejan Živkov & Marijana Joksimović & Suzana Balaban, 2021. "Measuring parametric and semiparametric downside risks of selected agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(8), pages 305-315.
- Dejan Živkov & Boris Kuzman & Jonel Subić, 2020. "What Bayesian quantiles can tell about volatility transmission between the major agricultural futures?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(5), pages 215-225.
- Sanusi, Olajide I. & Safi, Samir K. & Adeeko, Omotara & Tabash, Mosab I., 2022. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(2), June.
- Dejan Živkov & Suzana Balaban & Marijana Joksimović, 2022. "Making a Markowitz portfolio with agricultural commodity futures," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(6), pages 219-229.
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More about this item
Keywords
High Frequency Data; Fat-tail; Skewness; Realized Volatility; Agricultural Futures;All these keywords.
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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