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Authors = Martin Odening

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17 pages, 2017 KiB  
Article
Price Discovery and Market Reflexivity in Agricultural Futures Contracts with Different Maturities
by Steffen Volkenand, Günther Filler and Martin Odening
Risks 2020, 8(3), 75; https://doi.org/10.3390/risks8030075 - 11 Jul 2020
Cited by 2 | Viewed by 4110
Abstract
The purpose of this paper is to analyze market reflexivity in agricultural futures contracts with different maturities. To this end, we apply a four-dimensional Hawkes model to storable and non-storable agricultural commodities. We find market reflexivity for both storable and non-storable commodities. Reflexivity [...] Read more.
The purpose of this paper is to analyze market reflexivity in agricultural futures contracts with different maturities. To this end, we apply a four-dimensional Hawkes model to storable and non-storable agricultural commodities. We find market reflexivity for both storable and non-storable commodities. Reflexivity accounts for about 50 to 70% of the total trading activity. Differences between nearby and deferred contracts are less pronounced for non-storable than for storable commodities. We conclude that the co-existence of exogenous and endogenous price dynamics does not change qualitative characteristics of the price discovery process that have been observed earlier without the consideration of market reflexivity. Full article
(This article belongs to the Special Issue Risks: Feature Papers 2020)
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14 pages, 821 KiB  
Article
The Influence of Wind Energy and Biogas on Farmland Prices
by Olena Myrna, Martin Odening and Matthias Ritter
Land 2019, 8(1), 19; https://doi.org/10.3390/land8010019 - 15 Jan 2019
Cited by 23 | Viewed by 5902
Abstract
In the context of the rapid development of renewable energy in Germany in the last decade, and increased concerns regarding its potential impacts on farmland prices, this paper investigates the impact of wind energy and biogas production on agricultural land purchasing prices. To [...] Read more.
In the context of the rapid development of renewable energy in Germany in the last decade, and increased concerns regarding its potential impacts on farmland prices, this paper investigates the impact of wind energy and biogas production on agricultural land purchasing prices. To quantify the possible impact of the cumulative capacity of wind turbines and biogas plants on arable land prices in Saxony-Anhalt, we estimate a community-based and a transaction-based model using spatial econometrics and ordinary least squares. Based on data from 2007 to 2016, our analysis shows that a higher cumulative capacity of wind turbines in communities leads to higher farmland transaction prices, though the effect is very small: if the average cumulative capacity of wind turbines per community doubles, we expect that farmland prices per hectare increase by 0.4%. Plots that are directly affected by a wind turbine or part of a regional development plan, however, experience strong price increases. Full article
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16 pages, 9522 KiB  
Article
Neighborhood Effects in Wind Farm Performance: A Regression Approach
by Matthias Ritter, Simone Pieralli and Martin Odening
Energies 2017, 10(3), 365; https://doi.org/10.3390/en10030365 - 16 Mar 2017
Cited by 6 | Viewed by 4329
Abstract
The optimization of turbine density in wind farms entails a trade-off between the usage of scarce, expensive land and power losses through turbine wake effects. A quantification and prediction of the wake effect, however, is challenging because of the complex aerodynamic nature of [...] Read more.
The optimization of turbine density in wind farms entails a trade-off between the usage of scarce, expensive land and power losses through turbine wake effects. A quantification and prediction of the wake effect, however, is challenging because of the complex aerodynamic nature of the interdependencies of turbines. In this paper, we propose a parsimonious data driven regression wake model that can be used to predict production losses of existing and potential wind farms. Motivated by simple engineering wake models, the predicting variables are wind speed, the turbine alignment angle, and distance. By utilizing data from two wind farms in Germany, we show that our models can compete with the standard Jensen model in predicting wake effect losses. A scenario analysis reveals that a distance between turbines can be reduced by up to three times the rotor size, without entailing substantial production losses. In contrast, an unfavorable configuration of turbines with respect to the main wind direction can result in production losses that are much higher than in an optimal case. Full article
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