This paper aims to model and forecast the volatility of gold price with the help of other precious metals. The data applied for application part in the article involves three financial time series which are gold, silver and platinum daily spot prices. The volatility is modeled by univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models including GARCH and EGARCH with different distributions such as normal distribution and student-t distribution. At the same time, comparisons of estimation and forecasting the volatility between GARCH family models have been done.

本文以2003至2009年的資料為研究區間，採用基本面分析指標、技術面分析指標及基因規畫法對倫敦黃金午後定盤價每季帄均塑造金價預測模型，同時歸納以基因規畫法塑造金價預測模型時，應使用何種投入指標與相關基因規畫法參數設定，較有機會獲得較佳預測力的金價預測模型。 最後發現對於黃金價格而言，各國股市大盤及黃金供需相關因素為使用基因規畫法塑造金價預測模型時較重要的指標種類，而於經濟狀況有劇烈變動時，加入技術分析指標將會改善模型的表現。而比較指標與基因規畫設定參數(如挑選函式、運算子集合、演化代數、染色體群大小)對模型預測力之影響，發現指標對模型預測力的影響遠大於基因規畫設定參數。 / The research uses the data between 2003 to 2009 to discuss the gold price forecastting model. Using fundamental analysis indices, technical analysis indices and Genetic Programming(GP) to modeling the gold price forecastting model. This paper also summarized that what kind of indexes and GP parameters should be set for getting better performance? Finally found that ,using the stock indices of important market and gold supply/demand factors to modeling usually get better performance. If there are drastic changes in economic conditions, using the technical analysis indices can improve the performance of model. The comparison of influence on model performance between indexes and GP parameters(ex. selecetio function, operator set, reproducting times, population size) show that, the indices have more influence to model performance than GP parameters.

Denna studies huvudsakliga syfte var att analysera sambandet mellan den svenska aktiemarknaden och guldpriset. Guldet har en viktig roll i finansmarknaden samtidigt som området saknar forskning i Sverige. Genom detta skapades ett intresse att studera aktiemarknadens samband med guldpriset i Sverige. Forskning kring aktiemarknadens samband till guldpriset är splittrad på global nivå där resultaten både kan vara negativa, positiva och en del där inget samband existerar. Studiens underliggande syfte var att analysera sambandet mellan den svenska aktiemarknaden och ränta, tillväxt och valutakurs.Studien är begränsad till att analysera kvartalsvis data inom 23 år mellan 1995 och 2018 i Sverige. Uppgifterna har sedan analyserats i en korrelationsanalys och en multipel linjär regressionsanalys. Resultaten visar att det finns ett negativt samband mellan guldpriset och aktiemarknaden. Resultatet visar också att det finns ett negativt samband mellan ränta och aktiemarknad. Studiens resultat visar också att det finns ett positivt samband mellan tillväxt och aktiemarknad. Slutligen visar resultatet att det inte finns något signifikant samband mellan valutakurs och aktiemarknad. / The main aim of this study was to analyze the relationship between gold price and the swedish stock market. Since gold has a major role in financial systems, the interest arose because of the lack of research on the gold price relationship to the stock market in Sweden. That as well as divided view of if gold price relationship is negative, positive or not related to the stock market, has created the interest. The underlying aim of the study was to analyze therelationship between interest rate, economic growth and exchange rate with the dependent variable stock market.This study was limited by analyzing quarterly data in 23 years between 1995 and 2018 on the swedish market. Data was collected and analyzed in statistical programs named Apple Numbers and SPSS. Data was analyzed in a correlation analysis and a regression analysis. The result showed that there is a negative relation between gold price and stock market. The result also shows that there is a negative relation between between interest rate and stock market. It also shows that there is a positive relation between economic growth and stockmarket. Lastly the result shows that there is no significant relation between exchange rate and stock market.

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.

Gold price hits record high more than ¢C1900 in 2011, so how to forecast gold price and whether the influence factor of gold price change over time become more interesting issues for people. The beginning of this paper tries to find out the reasonable gold price then cut the study period into 7 stages and examines the influence factor of gold price in each stage from 1972 to 2011. Finally, this research uses the recent influence factor to build a forecasting model and tests its performance. The empirical result has three parts. First, from the view of purchasing power at December 31, 1971, gold price is too high in the end of 2011. Secondly, influence factors of gold price will change over time. They usually alter with important economic events of the world. Thirdly, the forecasting model has good performance in both in-sample and out-of-sample backtesting, but if the influence factor had changed, the performance would be worse in out-of-sample backtesting.

This research paper revolves around the world’s oldest financial asset, gold, and whatdrives its price, which is of importance for all investors looking to be exposed to gold.The aim of this paper is to identify the main drivers behind the gold price, whichis done by performing a multiple linear regression analysis on the gold price and aset of explanatory variables. The results show that the real yield, measured as theTIPS-rate, has the largest impact on the gold price, followed by the inflation rate.The conclusion that is drawn in the paper is that it is reasonable that the real yield isthe main driver of the gold price, because the higher the real yield, the less attractiveit becomes for investors to own gold, as it is not an interest-bearing asset. / Den här uppsatsen handlar om väarldens äldsta finansiella tillgåang, guld, och vad som driver dess pris, vilket är till nytta för alla investerare som ämnar vara exponerade mot guld. Syftet med uppsatsen är att identifiera de huvudsakliga drivkrafterna bakom guldpriset, vilket görs genom att utföra en multipel linjär regressionsanalys på guldpriset och ett antal förklaringsvariabler. Resultatet visar att realräntan, mätt i form av TIPS-räntan, har störst påverkan på guldpriset, följt av inflationstakten. Slutsatsen som dras i uppsatsen är att det är rimligt att realräntan har störst påverkan på guldpriset, i och med att ju högre realränta, desto mindre attraktivtblir det för investerare att äga guld, då guld inte är en räntebärande tillgång.

The goal of this diploma thesis is to analyze the gold market and provide readers with the necessary information and context having an impact on the price of gold. The thesis consists of three chapters. First one deals in general with the commodity market and introduces the readers to commodity exchange issues such as trading commodities in commodity exchanges, motives of commodity trading as well as the specific characteristics of commodities. Second one concerns the detailed analysis of commodity investment tools that investors might use when they feel like getting an exposure to price movements of commodities. The last chapter gears towards an analysis of the gold market in today's super globalized world and depicts what fundamental factors have an impact on the price of gold. At last, I shall summarize existing pieces of knowledge and cast light on further gold price movements.

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.