黃金價格的波動特征和影響因素分析
發(fā)布時間：2018-08-31 18:09
【摘要】：黃金是一種特殊的商品,因為通常情況下它表現(xiàn)出歷史賦予的貨幣屬性,同時也具備由其貨幣和商品屬性所衍生出的投資屬性。很多和黃金相關價格波動的研究圍繞著黃金的這三種屬性展開。特別是2007年以黃金相關的論文大量涌現(xiàn)。據(jù)國外機構(gòu)調(diào)查,在所有和黃金相關的論文中：約有15%發(fā)表于2010至2011年之間,而2008至2009年之間的文獻數(shù)量則達到了21%。假使論文從思路的形成到最終發(fā)表需要半年至一年的時間,一個合理的估計顯示,約一半的與黃金相關的論文發(fā)表于2007年,金融危機發(fā)端之后。大量的文獻探討了黃金市場的有效性、黃金對沖通貨膨脹／美元的作用、黃金規(guī)避風險的效果、黃金和其他一些大宗商品價格的關系,甚至一些文獻開始討論黃金價格在近十年是否出現(xiàn)了泡沫。隨著研究的不斷深入,開始有研究者意識到是否應該更加綜合的看待黃金的一系列分析研究。 按照大部分文獻的研究思路,本文將影響黃金價格波動的因素劃分為三塊：黃金的商品屬性對其價格波動的影響、黃金的貨幣屬性對其價格波動的影響、黃金的避險屬性對其價格波動的影響。另一個較為重要的問題是關于實證方法的選取。一種方法是通過事先選定時間節(jié)點,來對黃金價格波動加以分析。但這樣做會產(chǎn)生兩個問題：如果事先知道怎樣選取時間節(jié)點,意味著在明確這個階段內(nèi)影響黃金價格波動的因素,進一步的意味著很多條件可以通過事先設定來做出是非判斷；而失去了發(fā)現(xiàn)的探究。其次,若通過不斷的增加時間節(jié)點來詳盡的討論問題,略顯繁瑣同時產(chǎn)生爭議。 從研究方法上來看,時間序列是將統(tǒng)計指標按時間先后順序排列而組成數(shù)據(jù)序列。直觀的看,隨著時間的推移經(jīng)濟變量在不斷的變化；“一種容易看見但不容易把握的變化”也存在與時間序列當中。這種變化可以是時間序列內(nèi)在機制的突然改變、也可以是外部環(huán)境狀態(tài)發(fā)生變遷所導致。如果知道這種“突變”發(fā)生的時點,則可以很好的通過分段研究來探索想要回答的問題。但這種變化恰恰非常難以把握,這是由于時間序列本來的變化掩蓋了這種內(nèi)在機制的變化。這顯示出時間序列數(shù)據(jù)所具有的非線性性。當前,在不能斷定“突變”發(fā)生的時點的模型中,學術界中使用較為平凡的處理這種非線性時間序列數(shù)據(jù)的模型最早在1989年有Hamilton提出。馬科夫機制轉(zhuǎn)換模型通過納入多個結(jié)構(gòu)方程,將時間序列的非線性通過變來在不同狀態(tài)下的轉(zhuǎn)變刻畫出來。馬科夫機制轉(zhuǎn)換模型刻畫的這種復雜的動態(tài)演化過程,使得研究這能夠更好的探討其所需解答問題。用機制轉(zhuǎn)化模型對宏觀經(jīng)濟和金融市場進行分析研究是一個非常熱門的領域。 上面的方法結(jié)合到本文對黃金價格波動的研究：可以分三個層次。第一層次是對一般波動水平的馬科夫機制轉(zhuǎn)模型分析,第二層是分別利用三大屬性對黃金價格波動影響的馬科夫機制轉(zhuǎn)模型分析,最后采用馬科夫機制轉(zhuǎn)換向量自回歸過程將三大屬性和黃金價格作為一個系統(tǒng)研究變量之間的相互作用。本文希望能嘗試著回答一下幾個問題：黃金價格波動受那些主要因素影響?黃金價格波動有什么樣的特征?所提到的這些因素能否解釋黃金價格的波動、以及怎樣解釋?即,黃金的商品屬性、避險屬性、貨幣屬性在什么情況下、什么時間內(nèi)對黃金價格產(chǎn)生了什么樣的影響。 因此本文主要劃分為以下幾個部分： 第一部分、主要介紹全文的背景、研究問題、相關文獻綜述評論、文章結(jié)構(gòu)和主要創(chuàng)新點、主要研究方法和所使用數(shù)據(jù)簡介。主要研究方法方面介紹了：向量自回歸方法、馬科夫機制轉(zhuǎn)化模型、HP濾波、鄒檢驗。其中對本文所使用的主要方法馬科夫機制轉(zhuǎn)化模型進行了詳細介紹。數(shù)據(jù)選取以及處理的目的和原因也進行了詳細的介紹。 第二部分、對黃金價格波動因素及特征的初步探討。利用馬科夫機制轉(zhuǎn)化模型對其價格波動性的探討,即黃金價格的波動水平是否發(fā)生了結(jié)構(gòu)性的轉(zhuǎn)變。在設定兩種機制的前提下,研究表明：第一種機制下,每周GFP的收益在-0.02%,為小幅下跌；第二種機制下,GFP的每周收益在0.28%作為,為較大幅度上漲。最后,用較小的篇幅以傳統(tǒng)的VAR方法和GARCH方法對黃金價格波動的影響因素、波動特征進行了探討。 第三至六部分作為一個整體分別完成了以下討論： 1、利用馬科夫機制轉(zhuǎn)換模型對黃金價格和波動指數(shù)之間關系的探討。發(fā)現(xiàn)兩者之間波動關系可以大致分為三種狀態(tài)：狀態(tài)1,VIX的一單位正向變動可以導致GFP下跌4.693美元,其標準差為16.2%；狀態(tài)2,VIX的一單位正向變動可以導致GFP下跌15.24美元,標準差為18.4%；狀態(tài)3,VIX的一單位正向變動可以導致GFP上漲25.825美元,標準差為250.8%. 2、利用馬科夫機制轉(zhuǎn)換模型對黃金價格和美元指數(shù)之間關系的探討。發(fā)現(xiàn)兩者之間波動關系可以大致分為五種狀態(tài)：狀態(tài)1,USDX的一單位正向變動可以導致GFP下跌26.945美元；狀態(tài)2,USDX的一單位正向變動可以導致GFP下跌1.042美元；狀態(tài)3,USDX的一單位正向變動可以導致GFP下跌42.183美元。 3、利用馬科夫機制轉(zhuǎn)換模型對黃金價格和大宗商品價格指數(shù)之間關系的探討。發(fā)現(xiàn)兩者之間波動關系可以大致分為三種狀態(tài)：狀態(tài)1,DJUBSSP的一單位正向變動可以導致GFP上漲3.001美元；狀態(tài)2,USDX的一單位正向變動可以導致GFP上漲1.856美元；狀態(tài)3,USDX的一單位正向變動可以導致GFP上漲3.723美元。 4、利用馬科夫機制轉(zhuǎn)換向量自回歸模型綜合討論了黃金價格波動與其商品、貨幣、避險屬性三者內(nèi)在的系統(tǒng)性關系。發(fā)現(xiàn)在狀態(tài)1、狀態(tài)2、狀態(tài)3中,黃金的月收益率分別為-2.186%、16.175%和-0.621%。因此可以認為狀態(tài)1、狀態(tài)2、狀態(tài)3分別代表黃金價格小幅下跌階段、大幅上漲階段和幾乎沒有漲跌的階段。 第七部分、對上述分析進行了總結(jié),并分析了研究可以改進的地方。
[Abstract]:Gold is a special commodity because it usually shows the monetary attributes given by history and also has the investment attributes derived from its monetary and commodity attributes. According to a survey conducted by foreign institutions, about 15% of all gold-related papers were published between 2010 and 2011, while the number of papers published between 2008 and 2009 reached 21%. In 2007, after the financial crisis began, a large number of papers discussed the effectiveness of the gold market, the role of gold in hedging inflation against the dollar, the risk aversion effect of gold, the relationship between gold and other commodity prices, and even some of the papers began to discuss whether gold prices had a bubble in the last decade. As time went on, researchers began to realize whether they should look at gold in a more comprehensive way.
According to the research ideas of most literatures, this paper divides the factors affecting gold price fluctuation into three parts: the influence of commodity property of gold on its price fluctuation, the influence of monetary property of gold on its price fluctuation, and the influence of hedging property of gold on its price fluctuation. One way is to analyze gold price volatility by selecting a time node beforehand. But doing so raises two questions: if you know how to select a time node beforehand, it means to identify the factors that affect gold price volatility at this stage, which further means that many conditions can be set beforehand. Judging right from wrong; losing the inquiry of discovery; secondly, discussing the problem in detail by increasing the time nodes, is slightly cumbersome and controversial.
As far as research methods are concerned, time series consist of statistical indexes arranged in chronological order to form a data sequence. Intuitively, with the passage of time, economic variables are constantly changing; "a change that is easy to see but not easy to grasp" also exists in the time series. This change can be the internal mechanism of time series. A sudden change in the state of the external environment can also be caused by a change in the state of the external environment. If you know when this "sudden change" occurs, you can do a good job of exploring the questions you want to answer through piecewise research. But this change is very difficult to grasp, because the original changes in the time series cover up the changes in this internal mechanism. This shows the nonlinearity of time series data. Nowadays, in the models which can not determine the time point of "catastrophe", the more trivial models for dealing with this kind of nonlinear time series data were first proposed by Hamilton in 1989. The non-linearity of the sequence is characterized by the change in different states. Markov's mechanism transformation model describes this complex dynamic evolution process, which makes it possible to better explore the problems it needs to answer.
The above method is combined with the study of gold price fluctuation in this paper, which can be divided into three levels. The first level is the analysis of Markov mechanism transition model of general fluctuation level. The second level is the analysis of Markov mechanism transition model with the influence of three attributes on gold price fluctuation respectively. Finally, the Markov mechanism transition vector is used to self-return. The regression process takes the three attributes and the price of gold as a systematic study variable. This paper attempts to answer several questions: What are the main factors that affect gold price volatility? What are the characteristics of gold price volatility? Can the factors mentioned explain gold price volatility and how Explanation? That is, under what circumstances and at what time the commodity attributes, hedging attributes and monetary attributes of gold have an impact on gold prices.
Therefore, this article is mainly divided into the following parts:
The first part mainly introduces the background, research issues, literature review, structure and main innovations of the paper, main research methods and the data used.The main research methods include vector autoregression, Markov mechanism transformation model, HP filtering, Zou test. The Markov mechanism transformation model is introduced in detail. The purpose and reason of data selection and processing are also introduced in detail.
The second part is a preliminary study on the factors and characteristics of gold price fluctuation. The Markov mechanism transformation model is used to study the price fluctuation, that is, whether the fluctuation level of gold price has undergone structural changes. In the second mechanism, the weekly return of GFP is 0.28% as a relatively large increase. Finally, the paper discusses the influencing factors and volatility characteristics of gold price volatility with the traditional VAR method and GARCH method.
The third to six parts respectively completed the following discussions as a whole:
1. Using Markov mechanism transformation model, the relationship between gold price and volatility index is discussed. It is found that the volatility relationship between the two can be divided into three states: state 1, a unit positive change of VIX can cause GFP to fall by $4.693, its standard deviation is 16.2%; state 2, a unit positive change of VIX can cause GFP to fall by 15%. At $24, the standard deviation is 18.4%; at Stage 3, a positive change in the VIX unit could lead to a $25.825 rise in GFP, with a standard deviation of 250.8%.
2. Using Markov mechanism transformation model, the relationship between gold price and US dollar index is discussed. It is found that the fluctuation relationship between them can be divided into five states: state 1, a forward change of USDX unit can lead to a decrease of US $26.945 in GFP; state 2, a forward change of USDX unit can lead to a decrease of US $1.042 in GFP; 3, a positive forward movement of USDX can cause GFP to fall by 42.183 US dollars.
3. Using Markov mechanism transition model, the relationship between gold price and commodity price index is discussed. It is found that the fluctuation relationship between them can be divided into three states: state 1, the forward movement of a unit of DJUBSSP can lead to the rise of $3.001 in GFP; state 2, the forward movement of a unit of USDX can lead to the rise of 1.856 in GFP. US dollar; state 3, a positive forward change of USDX can cause GFP to rise by US $3.723.
4. By using Markov mechanism transition vector autoregressive model, the intrinsic systematic relationship between gold price volatility and its commodities, currencies and hedging attributes is discussed. It is found that the monthly yields of gold in state 1, state 2 and state 3 are - 2.186%, 16.175% and - 0.621% respectively. Prices fell slightly, with a sharp rise and almost no rise or fall.
The seventh part summarizes the above analysis and analyzes the areas where research can be improved.
【學位授予單位】：西南財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F224;F830.94
本文編號：2215755
[Abstract]:Gold is a special commodity because it usually shows the monetary attributes given by history and also has the investment attributes derived from its monetary and commodity attributes. According to a survey conducted by foreign institutions, about 15% of all gold-related papers were published between 2010 and 2011, while the number of papers published between 2008 and 2009 reached 21%. In 2007, after the financial crisis began, a large number of papers discussed the effectiveness of the gold market, the role of gold in hedging inflation against the dollar, the risk aversion effect of gold, the relationship between gold and other commodity prices, and even some of the papers began to discuss whether gold prices had a bubble in the last decade. As time went on, researchers began to realize whether they should look at gold in a more comprehensive way.
According to the research ideas of most literatures, this paper divides the factors affecting gold price fluctuation into three parts: the influence of commodity property of gold on its price fluctuation, the influence of monetary property of gold on its price fluctuation, and the influence of hedging property of gold on its price fluctuation. One way is to analyze gold price volatility by selecting a time node beforehand. But doing so raises two questions: if you know how to select a time node beforehand, it means to identify the factors that affect gold price volatility at this stage, which further means that many conditions can be set beforehand. Judging right from wrong; losing the inquiry of discovery; secondly, discussing the problem in detail by increasing the time nodes, is slightly cumbersome and controversial.
As far as research methods are concerned, time series consist of statistical indexes arranged in chronological order to form a data sequence. Intuitively, with the passage of time, economic variables are constantly changing; "a change that is easy to see but not easy to grasp" also exists in the time series. This change can be the internal mechanism of time series. A sudden change in the state of the external environment can also be caused by a change in the state of the external environment. If you know when this "sudden change" occurs, you can do a good job of exploring the questions you want to answer through piecewise research. But this change is very difficult to grasp, because the original changes in the time series cover up the changes in this internal mechanism. This shows the nonlinearity of time series data. Nowadays, in the models which can not determine the time point of "catastrophe", the more trivial models for dealing with this kind of nonlinear time series data were first proposed by Hamilton in 1989. The non-linearity of the sequence is characterized by the change in different states. Markov's mechanism transformation model describes this complex dynamic evolution process, which makes it possible to better explore the problems it needs to answer.
The above method is combined with the study of gold price fluctuation in this paper, which can be divided into three levels. The first level is the analysis of Markov mechanism transition model of general fluctuation level. The second level is the analysis of Markov mechanism transition model with the influence of three attributes on gold price fluctuation respectively. Finally, the Markov mechanism transition vector is used to self-return. The regression process takes the three attributes and the price of gold as a systematic study variable. This paper attempts to answer several questions: What are the main factors that affect gold price volatility? What are the characteristics of gold price volatility? Can the factors mentioned explain gold price volatility and how Explanation? That is, under what circumstances and at what time the commodity attributes, hedging attributes and monetary attributes of gold have an impact on gold prices.
Therefore, this article is mainly divided into the following parts:
The first part mainly introduces the background, research issues, literature review, structure and main innovations of the paper, main research methods and the data used.The main research methods include vector autoregression, Markov mechanism transformation model, HP filtering, Zou test. The Markov mechanism transformation model is introduced in detail. The purpose and reason of data selection and processing are also introduced in detail.
The second part is a preliminary study on the factors and characteristics of gold price fluctuation. The Markov mechanism transformation model is used to study the price fluctuation, that is, whether the fluctuation level of gold price has undergone structural changes. In the second mechanism, the weekly return of GFP is 0.28% as a relatively large increase. Finally, the paper discusses the influencing factors and volatility characteristics of gold price volatility with the traditional VAR method and GARCH method.
The third to six parts respectively completed the following discussions as a whole:
1. Using Markov mechanism transformation model, the relationship between gold price and volatility index is discussed. It is found that the volatility relationship between the two can be divided into three states: state 1, a unit positive change of VIX can cause GFP to fall by $4.693, its standard deviation is 16.2%; state 2, a unit positive change of VIX can cause GFP to fall by 15%. At $24, the standard deviation is 18.4%; at Stage 3, a positive change in the VIX unit could lead to a $25.825 rise in GFP, with a standard deviation of 250.8%.
2. Using Markov mechanism transformation model, the relationship between gold price and US dollar index is discussed. It is found that the fluctuation relationship between them can be divided into five states: state 1, a forward change of USDX unit can lead to a decrease of US $26.945 in GFP; state 2, a forward change of USDX unit can lead to a decrease of US $1.042 in GFP; 3, a positive forward movement of USDX can cause GFP to fall by 42.183 US dollars.
3. Using Markov mechanism transition model, the relationship between gold price and commodity price index is discussed. It is found that the fluctuation relationship between them can be divided into three states: state 1, the forward movement of a unit of DJUBSSP can lead to the rise of $3.001 in GFP; state 2, the forward movement of a unit of USDX can lead to the rise of 1.856 in GFP. US dollar; state 3, a positive forward change of USDX can cause GFP to rise by US $3.723.
4. By using Markov mechanism transition vector autoregressive model, the intrinsic systematic relationship between gold price volatility and its commodities, currencies and hedging attributes is discussed. It is found that the monthly yields of gold in state 1, state 2 and state 3 are - 2.186%, 16.175% and - 0.621% respectively. Prices fell slightly, with a sharp rise and almost no rise or fall.
The seventh part summarizes the above analysis and analyzes the areas where research can be improved.
【學位授予單位】：西南財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F224;F830.94
【參考文獻】
相關期刊論文 前8條
1 傅瑜;近期黃金價格波動的實證研究[J];產(chǎn)業(yè)經(jīng)濟研究;2004年01期
2 楊葉;;黃金價格和石油價格的聯(lián)動分析[J];黃金;2007年02期
3 李治國;;從美元指數(shù)、黃金價格與原油價格關系看原油價格體制—微觀數(shù)據(jù)及政策含義[J];經(jīng)濟問題探索;2012年05期
4 張若欽;王剛;;美元指數(shù)和美國國債收益率對國際黃金價格的非線性影響——基于STR模型的研究[J];價格月刊;2013年06期
5 陳千里,周少甫;上證指數(shù)收益的波動性研究[J];數(shù)量經(jīng)濟技術經(jīng)濟研究;2002年06期
6 劉金全,劉志剛,于冬;我國經(jīng)濟周期波動性與階段性之間關聯(lián)的非對稱性檢驗——Plucking模型對中國經(jīng)濟的實證研究[J];統(tǒng)計研究;2005年08期
7 范為;房四海;;金融危機期間黃金價格的影響因素研究[J];管理評論;2012年03期
8 楊艷林;;黃金的資產(chǎn)屬性:對沖資產(chǎn)還是避險資產(chǎn)[J];武漢金融;2012年05期
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