在利率自由化的過程中，貨幣市場利率變化情形較以前劇烈，因此近年來 使得一些需要運用貨幣市場來融通短期資金的廠商與個人較以往面臨更大 的利率變動的風險。本文的主要目的在探討以芝加哥期貨交易所(CBOT)之 美國長期公債期貨合約、十年期公債期貨合約及五年期公債期貨合約及芝 加哥商品期貨交易所(CME) 的美國國庫券期貨、Eurodollar期貨之組合交 叉規避國內商業本票30天期、90天期、 180天期之次級市場的利率風險， 以了解利用國外利率期貨交叉規國內商業本票現貨利率風險的績效及不同 的避險期間與不同的避險比例對避險績效的影響。本研究之採樣期間 自1989年 1月至1992年10月底，並分為兩部份進行實證，一為整體樣本測 試避險模式、另一為樣本外交叉避險模式，且修正自身相關現象。 根據 實證結果，可以得到以下的結論與發現：1.在整體樣本測試交叉避模式之 自身相關迴歸分析中，當避險期間愈長時，則避險績效愈好。2.在樣本外 測試交叉避險模式--最適避險模式之價差迴歸分析與自身相關迴歸分析中 ，可以發現三種商業本票的交叉避險績效均以避險期間較短者擁有較好的 交叉避險績效。3.在樣本外測試交叉避險模式中，所有商業本票不論何種 避險期間，自然避險模式的交叉避險績效均比最適避險模式為差。4.在樣 本外測試交叉避 險模式--最適避險模式之價差迴歸分析與自身相關迴歸 分析中，可以發現所有商業本票，在單一期貨組合的交叉避險績效大致上 皆高於其他期貨組合的交叉避險績效，因此，在從事避險操作時，基於時 間及交易成本的考量，以單一期貨組合從事避險操作較為有利。

In this dissertation, generalized additive neural networks (GANNs) and multilayer perceptrons (MLPs) are studied and compared as prediction techniques. MLPs are the most widely used type of artificial neural network (ANN), but are considered black boxes with regard to interpretability. There is currently no simple a priori method to determine the number of hidden neurons in each of the hidden layers of ANNs. Guidelines exist that are either heuristic or based on simulations that are derived from limited experiments. A modified version of the neural network construction with cross–validation samples (N2C2S) algorithm is therefore implemented and utilized to construct good MLP models. This algorithm enables the comparison with GANN models. GANNs are a relatively new type of ANN, based on the generalized additive model. The architecture of a GANN is less complex compared to MLPs and results can be interpreted with a graphical method, called the partial residual plot. A GANN consists of an input layer where each of the input nodes has its own MLP with one hidden layer. Originally, GANNs were constructed by interpreting partial residual plots. This method is time consuming and subjective, which may lead to the creation of suboptimal models. Consequently, an automated construction algorithm for GANNs was created and implemented in the SAS R statistical language. This system was called AutoGANN and is used to create good GANN models. A number of experiments are conducted on five publicly available data sets to gain insight into the similarities and differences between GANN and MLP models. The data sets include regression and classification tasks. In–sample model selection with the SBC model selection criterion and out–of–sample model selection with the average validation error as model selection criterion are performed. The models created are compared in terms of predictive accuracy, model complexity, comprehensibility, ease of construction and utility. The results show that the choice of model is highly dependent on the problem, as no single model always outperforms the other in terms of predictive accuracy. GANNs may be suggested for problems where interpretability of the results is important. The time taken to construct good MLP models by the modified N2C2S algorithm may be shorter than the time to build good GANN models by the automated construction algorithm / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2011.

In this dissertation, generalized additive neural networks (GANNs) and multilayer perceptrons (MLPs) are studied and compared as prediction techniques. MLPs are the most widely used type of artificial neural network (ANN), but are considered black boxes with regard to interpretability. There is currently no simple a priori method to determine the number of hidden neurons in each of the hidden layers of ANNs. Guidelines exist that are either heuristic or based on simulations that are derived from limited experiments. A modified version of the neural network construction with cross–validation samples (N2C2S) algorithm is therefore implemented and utilized to construct good MLP models. This algorithm enables the comparison with GANN models. GANNs are a relatively new type of ANN, based on the generalized additive model. The architecture of a GANN is less complex compared to MLPs and results can be interpreted with a graphical method, called the partial residual plot. A GANN consists of an input layer where each of the input nodes has its own MLP with one hidden layer. Originally, GANNs were constructed by interpreting partial residual plots. This method is time consuming and subjective, which may lead to the creation of suboptimal models. Consequently, an automated construction algorithm for GANNs was created and implemented in the SAS R statistical language. This system was called AutoGANN and is used to create good GANN models. A number of experiments are conducted on five publicly available data sets to gain insight into the similarities and differences between GANN and MLP models. The data sets include regression and classification tasks. In–sample model selection with the SBC model selection criterion and out–of–sample model selection with the average validation error as model selection criterion are performed. The models created are compared in terms of predictive accuracy, model complexity, comprehensibility, ease of construction and utility. The results show that the choice of model is highly dependent on the problem, as no single model always outperforms the other in terms of predictive accuracy. GANNs may be suggested for problems where interpretability of the results is important. The time taken to construct good MLP models by the modified N2C2S algorithm may be shorter than the time to build good GANN models by the automated construction algorithm / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2011.