In this study,we aim at developing a model for option pricing to reduce the risks associated with Ethiopian coffee price fluctuations.We used daily closed Washed Sidama class A Grade3(WSDA3)coffee price recorded in th...In this study,we aim at developing a model for option pricing to reduce the risks associated with Ethiopian coffee price fluctuations.We used daily closed Washed Sidama class A Grade3(WSDA3)coffee price recorded in the period 31 May 2011 to 30 March 2018 obtained from Ethiopia commodity exchange(ECX)market to analyse the price fluctuation.The nature of log-returns of the price is asymmetric(negatively skewed)and exhibits high kurtosis.We used jump diffusion models for modeling and option pricing the coffee price.The method of maximum likelihood is applied to estimate the parameters of the models.We used the root mean square error(RMSE)to test the validation of the models.The values of RMSE for Merton’s and double exponential jump diffusion models are 0.1093 and 0.0783,respectively.These results indicate that the models fit the data very well.We used analytical and Monte Carlo technique to find the call option pricing of WSDA3 price.Based on the empirical results,we concluded that double exponential jump diffusion model is more efficient than Merton’s model for modeling and option pricing of this coffee price.展开更多
A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples ...A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples in the feature space to boost the performance of each weak classifier in the asymmetric boosting framework. Then, the weak classifier based on a novel linear discriminate analysis （LDA） algorithm which is learned from the subspace of heterogeneous features is integrated into the framework. Finally, the proposed method deals with each bit of the code sequentially, which utilizes the samples misclassified in each round in order to learn compact and balanced code. The heterogeneous information from different modalities can be effectively complementary to each other, which leads to much higher performance. The experimental results based on the two public benchmarks demonstrate that this method is superior to many of the state- of-the-art methods. In conclusion, the performance of the retrieval system can be improved with the help of multiple heterogeneous features and the compact hash codes which can be learned by the imbalanced learning method.展开更多
文摘In this study,we aim at developing a model for option pricing to reduce the risks associated with Ethiopian coffee price fluctuations.We used daily closed Washed Sidama class A Grade3(WSDA3)coffee price recorded in the period 31 May 2011 to 30 March 2018 obtained from Ethiopia commodity exchange(ECX)market to analyse the price fluctuation.The nature of log-returns of the price is asymmetric(negatively skewed)and exhibits high kurtosis.We used jump diffusion models for modeling and option pricing the coffee price.The method of maximum likelihood is applied to estimate the parameters of the models.We used the root mean square error(RMSE)to test the validation of the models.The values of RMSE for Merton’s and double exponential jump diffusion models are 0.1093 and 0.0783,respectively.These results indicate that the models fit the data very well.We used analytical and Monte Carlo technique to find the call option pricing of WSDA3 price.Based on the empirical results,we concluded that double exponential jump diffusion model is more efficient than Merton’s model for modeling and option pricing of this coffee price.
基金The National Natural Science Foundation of China(No.61305058)the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.12KJB520003)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20130471)the Scientific Research Foundation for Advanced Talents by Jiangsu University(No.13JDG093)
文摘A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples in the feature space to boost the performance of each weak classifier in the asymmetric boosting framework. Then, the weak classifier based on a novel linear discriminate analysis （LDA） algorithm which is learned from the subspace of heterogeneous features is integrated into the framework. Finally, the proposed method deals with each bit of the code sequentially, which utilizes the samples misclassified in each round in order to learn compact and balanced code. The heterogeneous information from different modalities can be effectively complementary to each other, which leads to much higher performance. The experimental results based on the two public benchmarks demonstrate that this method is superior to many of the state- of-the-art methods. In conclusion, the performance of the retrieval system can be improved with the help of multiple heterogeneous features and the compact hash codes which can be learned by the imbalanced learning method.
