本文改良了Shafiee-Topal(2010)所提出之跳躍模型之波動率，並歸納成三種模型:改良跳躍模型、改良平滑跳躍模型以及最佳化跳躍模型，並運用時間序列模型探討樣本期間內黃金價格。第一部份比較三種跳躍模型與Shafiee-Topal模型在訓練集及測試集的預測結果，並預測2012年至2018年之黃金價格走勢。第二部份探討黃金價格、原油價格以及美元加權指數之間的互動關係，建立多變數模型以預測黃金價格之長期趨勢。 首先，本文檢驗黃金價格、原油價格及美元加權指數樣本之恆定性，經由ADF 單根檢定法發現序列具有單根，進而使用TSP(Trend Stationary Process)估計模型參數。其次，黃金價格、原油價格及美元加權指數經共整合檢定發現，各模型變數間均具有共整合關係，即變數間具有長期均衡關係。黃金價格與原油價格呈正向反應，而黃金價格和原油價格與美元加權指數呈負向反應，除了受自身的預測解釋能力外，亦可以做為觀察其他變數的未來走勢方向及影響大小預估。最後，探討黃金價格受波動率的影響情形，本文改良Shafiee-Topal模型之波動率，並比較四種模型對黃金價格趨勢預測之結果，發現改良平滑跳躍模型在實際黃金價格波動率大時，其趨勢預測結果會優於Shafiee-Topal模型。 / This research advanced the volatility component (λ) of the jump and dip model (Shafiee and Topal,2010) on gold prices from 1968 to 2012 and estimated the gold price for the next 6 years. Based on the trend stationary process, we defined the three components and derived three new models: Adjusted Jump and Dip Model, Adjusted Smooth Jump and Dip Model and Optimized Jump and Dip Model. First part of the thesis compared the performance in prediction of the training data and the testing data for three different models and the jump and dip model. Second part of the thesis investigated the relationship among the gold price, crude oil price, and trade weighted U.S. dollar index of the concepts The result illustrated the long term trend of gold price described by a multivariate predictive model. We found evidence that different levels of volatility affect the prediction of gold price, and the adjusted jump and dip Model performs best when the true volatility is relatively high.

本研究主係探討以投資者持有本國股票作為基礎投資組合，利用台灣五十成份股投資組合及金融期貨指數之月報酬率資料形成效率前緣，然後將本研究所選取之黃金產業GOX指數或以黃金現貨價格兩種黃金商品投資組合分別加入基礎投資組合中，觀察每個投資組合標的加入前後之投資機會集合之變化，是否基礎投資組合之效率前緣進一步向左偏移，並比較其優劣。此外進一步探討在兩種黃金投資組合加入基礎投資組合，分別在股市多頭及空頭市場時會不會產生相同的效果；並且藉由中央大學台灣經濟發展研究中心所調查訂定的消費者信心指數，以中位數區分為信心指數高或信心指數低兩組情境，並將兩種黃金商品投資組合在不同的情境依序分別加入基礎投資組合中，其基礎投資組合之效率前緣是否皆仍進一步向左偏移。實證結果發現，黃金現貨帶給基礎資產投資機會集合的貢獻，明顯優於黃金產業GOX指數，當檢定資產為黃金產業GOX指數時，「切點投資組合」差異W_1統計值上皆不具統計顯著性，整體而言當黃金產業GOX指數在加入投資組合之後，我們認為大部份的效果是來自分散原有的投資組合風險；然而黃金現貨在改善投資機會集合的貢獻上，來自「最小變異數投資組合」及「切點投資組合」的改善皆有顯著的效果。

本文有兩個研究目標，第一個是比較政大用電量與氣溫之間的相關性，第二則是分析影響黃金價格波動的因素。本文使用到的研究方法有希爾柏特黃轉換（HHT）與一些統計值。 　　本研究使用的分析數據如下：政大逐時用電量、台北逐時氣溫以及倫敦金屬交易所（London Metal Exchange）的月平均黃金價格。透過經驗模態分解法（EMD），我們可以將分析數據拆解成數個互相獨立的分量，再藉由統計值選出較重要的分量並分析其意義。逐時用電量的重要分量為日分量、週分量與趨勢；逐時氣溫的重要分量為日分量與趨勢；月平均黃金價格的重要分量則是低頻分量與趨勢。 藉由這些重要分量，我們可以更加了解原始數據震盪的特性，並且選出合理的平均週期將所有的分量分組，做更進一步的分析。逐時用電量與逐時氣溫分成高頻、中頻、低頻與趨勢四組，其中低頻與趨勢相加的組合具有最高的相關性。月平均黃金價格則是分為高頻、低頻與趨勢三組，其中高頻表現出供需以及突發事件等短週期因素，低頻與歷史上對經濟有重大影響的事件相對應，趨勢則是反應出通貨膨脹的現象。 / There are two main separated researched purposes in this thesis. First one is comparing the correlation between electricity consumption and temperature in NCCU. Another one is analyzing the properties of gold price volatility. The methods used in the study are Hilbert-Huang transform (HHT) and some statistical measures. 　　The following original data: hourly electricity consumption in NCCU, hourly temperature in Taipei, and the LME monthly gold prices are decomposed into several components by empirical mode decomposition (EMD). We can ascertain the significant components and analyze their meanings or properties by statistical measures. The significant components of each data are shown as follows: daily component, weekly component and residue for hourly electricity consumption; daily component and residue for hourly temperature; low frequency components and residue for the LME monthly gold prices. 　　We can understand more properties about these data according to the significant components, and dividing the components into several terms based on reasonable mean period. The components of hourly electricity consumption and hourly temperature are divided into high, mid, low frequency terms and trends, and the composition of low frequency terms and trends have the highest correlation between them. The components of LME monthly gold prices are divided into high, low frequency term and trend. High frequency term reveals the supply-demand and abrupt events. The low frequency term represents the significant events affecting economy seriously, and trend shows the inflation in the long run.

本研究主要應用基於總體經驗模態分解法(EEMD)之倒傳遞類神經網路(BPNN)預測兩種不同的非線性時間序列數據，包括政大逐時用電量以及逐日歷史黃金價格。透過EEMD，這兩種資料會分別被拆解為數條具有不同物理意義的本徵模態函數(IMF)，而這讓我們可以將這些IMF視為各種影響資料的重要因子，並且可將拆解過後的IMF放入倒傳遞類神經網路中做訓練。 另外在本文中，我們也採用移動視窗法作為預測過程中的策略，另外也應用內插法和外插法於逐時用電量的預測。內插法主要是用於補點以及讓我們的數據變平滑，外插法則可以在某個範圍內準確預測後續的趨勢，此兩種方法皆對提升預測準確度占有重要的影響。 利用本文的方法，可在預測的結果上得到不錯的準確性，但為了進一步提升精確度，我們利用多次預測的結果加總平均，然後和只做一次預測的結果比較，結果發現多次加總平均後的精確度的確大幅提升，這是因為倒傳遞類神經網路訓練過程中其目標為尋找最小誤差函數的關係所致。 / In this paper, we applied the Ensemble Empirical Mode Decomposition (EEMD) based Back-propagation Neural Network (BPNN) learning paradigm to two different topics for forecasting: the hourly electricity consumption in NCCU and the historical daily gold price. The two data series are both non-linear and non-stationary. By applying EEMD, they were decomposed into a finite, small number of meaningful Intrinsic Mode Functions (IMFs). Depending on the physical meaning of IMFs, they can be regarded as important variables which are input into BPNN for training. We also use moving-window method in the prediction process. In addition, cubic spline interpolation as well as extrapolation as our strategy is applied to electricity consumption forecasting, these two methods are used for smoothing the data and finding local trend to improve accuracy of results. The prediction results using our methods and strategy resulted in good accuracy. However, for further accuracy, we used the ensemble average method, and compared the results with the data produced without applying the ensemble average method. By using the ensemble average, the outcome was more precise with a smaller error, it results from the procedure of finding minimum error function in the BPNN training.