Commodity volatility modelling and option pricing with a potential function approach
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DOI: 10.1080/13518470701773593
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Cited by:
- Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2022.
"Forecasting realized volatility of agricultural commodities,"
International Journal of Forecasting, Elsevier, vol. 38(1), pages 74-96.
- Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2019. "Forecasting Realized Volatility of Agricultural Commodities," MPRA Paper 96267, University Library of Munich, Germany.
- Matteo Bonato & Oğuzhan Çepni & Rangan Gupta & Christian Pierdzioch, 2023.
"El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 785-801, July.
- Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021. "El Nino, La Nina, and Forecastability of the Realized Variance of Agricultural Commodity Prices: Evidence from a Machine Learning Approach," Working Papers 202179, University of Pretoria, Department of Economics.
- John M. Fry & Baoying Lai & Mark Rhodes, 2011. "The interdependence of Coffee spot and futures market," Working Papers 2011.1, International Network for Economic Research - INFER.
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Keywords
commodity prices; multiple attraction regions; potential function; volatility esimation; option pricing; hedging costs;All these keywords.
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