A neural network model for hedging crude oil is introduced. The NYMEX futures prices is used to investigate the effectiveness of this model. Empirical results show that the neural network model reduces price risk more than other approaches
When the literature regarding applications of neural networks is investigated, it appears that a sub...
Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, cru...
When the literature regarding applications of neural networks is investigated, it appears that a sub...
Despite the growing share of renewable energy sources, most of the world energy supply is still base...
This paper describes the profitability of technical trading rules which are enhanced by the use of n...
In this paper we use a kernel-based approach to Crude Oil price prediction which should allow us to ...
This paper presents short-term forecasting model for crude oil prices based on three layer feedforwa...
We study neural networks as nonparametric estimation tools for the hedging of options. To this end, ...
We study neural networks as nonparametric estimation tools for the hedging of options. To this end, ...
During the COVID-19 pandemic, uncertainty has increased in many areas of both business supply and de...
This paper focuses on oil hedging using near month crude oil futures. Hedging may allow a firm to re...
The Black-Scholes formula is a well-known model for pricing and hedging derivative securities. It re...
This paper focuses on oil hedging using near month crude oil futures. Hedging may allow a firm to re...
As the oil demand continues to surge ahead and production continues to decline, it is believed that ...
Prediction of oil prices is an implausible task due to the multifaceted nature of oil markets. This ...
When the literature regarding applications of neural networks is investigated, it appears that a sub...
Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, cru...
When the literature regarding applications of neural networks is investigated, it appears that a sub...
Despite the growing share of renewable energy sources, most of the world energy supply is still base...
This paper describes the profitability of technical trading rules which are enhanced by the use of n...
In this paper we use a kernel-based approach to Crude Oil price prediction which should allow us to ...
This paper presents short-term forecasting model for crude oil prices based on three layer feedforwa...
We study neural networks as nonparametric estimation tools for the hedging of options. To this end, ...
We study neural networks as nonparametric estimation tools for the hedging of options. To this end, ...
During the COVID-19 pandemic, uncertainty has increased in many areas of both business supply and de...
This paper focuses on oil hedging using near month crude oil futures. Hedging may allow a firm to re...
The Black-Scholes formula is a well-known model for pricing and hedging derivative securities. It re...
This paper focuses on oil hedging using near month crude oil futures. Hedging may allow a firm to re...
As the oil demand continues to surge ahead and production continues to decline, it is believed that ...
Prediction of oil prices is an implausible task due to the multifaceted nature of oil markets. This ...
When the literature regarding applications of neural networks is investigated, it appears that a sub...
Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, cru...
When the literature regarding applications of neural networks is investigated, it appears that a sub...
DOI
Add to ORKG