An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives
S Cramer, M Kampouridis, AA Freitas… - Expert Systems with …, 2017 - Elsevier
Regression problems provide some of the most challenging research opportunities in the
area of machine learning, and more broadly intelligent systems, where the predictions of …
area of machine learning, and more broadly intelligent systems, where the predictions of …
Valuation of energy storage: An optimal switching approach
R Carmona, M Ludkovski - Quantitative finance, 2010 - Taylor & Francis
We consider the valuation of energy storage facilities within the framework of stochastic
control. Our two main examples are natural gas dome storage and hydroelectric pumped …
control. Our two main examples are natural gas dome storage and hydroelectric pumped …
Development of an irradiance-based weather derivative to hedge cloud risk for solar energy systems
For large energy consumers transitioning to high shares of solar energy, irradiance
variability causes volatility in power generation and energy expenditures. Volatility in an end …
variability causes volatility in power generation and energy expenditures. Volatility in an end …
Mitigating hydrologic financial risk in hydropower generation using index-based financial instruments
BT Foster, JD Kern, GW Characklis - Water Resources and Economics, 2015 - Elsevier
Variability in streamflows can lead to reduced generation from hydropower producers and
result in reductions in revenues that can be financially disruptive. This link between …
result in reductions in revenues that can be financially disruptive. This link between …
Minimizing geographical basis risk of weather derivatives using a multi-site rainfall model
It is well known that the hedging effectiveness of weather derivatives is interfered by the
existence of geographical basis risk, ie, the deviation of weather conditions at different …
existence of geographical basis risk, ie, the deviation of weather conditions at different …
Pricing rainfall futures at the CME
Many business people such as farmers and financial investors are affected by indirect
losses caused by scarce or abundant rainfall. Because of the high potential of insuring …
losses caused by scarce or abundant rainfall. Because of the high potential of insuring …
[HTML][HTML] A poisson-gamma model for zero inflated rainfall data
Rainfall modeling is significant for prediction and forecasting purposes in agriculture,
weather derivatives, hydrology, and risk and disaster preparedness. Normally two models …
weather derivatives, hydrology, and risk and disaster preparedness. Normally two models …
Temperature stochastic modeling and weather derivatives pricing: empirical study with Moroccan data
M Mraoua - Afrika Statistika, 2007 - ajol.info
The main objective of this paper is to present a technique for pricing weather derivatives with
payout depending on temperature. We start by using the Principle Component Analysis …
payout depending on temperature. We start by using the Principle Component Analysis …
Precipitation or water capacity indices? An analysis of the benefits of alternative underlyings for index insurance
U Kellner, O Musshoff - Agricultural Systems, 2011 - Elsevier
Eastern Germany is often hit by drought causing income risk for crop farmers. Index-based
risk management instruments could help crop farmers to reduce their farm income risk. Such …
risk management instruments could help crop farmers to reduce their farm income risk. Such …
Decomposition genetic programming: An extensive evaluation on rainfall prediction in the context of weather derivatives
S Cramer, M Kampouridis, AA Freitas - Applied Soft Computing, 2018 - Elsevier
Regression problems provide some of the most challenging research opportunities in the
area of machine learning, where the predictions of some target variables are critical to a …
area of machine learning, where the predictions of some target variables are critical to a …