黄金价格的波动特征和影响因素分析
发布时间：2018-08-31 18:09
【摘要】：黄金是一种特殊的商品,因为通常情况下它表现出历史赋予的货币属性,同时也具备由其货币和商品属性所衍生出的投资属性。很多和黄金相关价格波动的研究围绕着黄金的这三种属性展开。特别是2007年以黄金相关的论文大量涌现。据国外机构调查,在所有和黄金相关的论文中：约有15%发表于2010至2011年之间,而2008至2009年之间的文献数量则达到了21%。假使论文从思路的形成到最终发表需要半年至一年的时间,一个合理的估计显示,约一半的与黄金相关的论文发表于2007年,金融危机发端之后。大量的文献探讨了黄金市场的有效性、黄金对冲通货膨胀／美元的作用、黄金规避风险的效果、黄金和其他一些大宗商品价格的关系,甚至一些文献开始讨论黄金价格在近十年是否出现了泡沫。随着研究的不断深入,开始有研究者意识到是否应该更加综合的看待黄金的一系列分析研究。 按照大部分文献的研究思路,本文将影响黄金价格波动的因素划分为三块：黄金的商品属性对其价格波动的影响、黄金的货币属性对其价格波动的影响、黄金的避险属性对其价格波动的影响。另一个较为重要的问题是关于实证方法的选取。一种方法是通过事先选定时间节点,来对黄金价格波动加以分析。但这样做会产生两个问题：如果事先知道怎样选取时间节点,意味着在明确这个阶段内影响黄金价格波动的因素,进一步的意味着很多条件可以通过事先设定来做出是非判断；而失去了发现的探究。其次,若通过不断的增加时间节点来详尽的讨论问题,略显繁琐同时产生争议。 从研究方法上来看,时间序列是将统计指标按时间先后顺序排列而组成数据序列。直观的看,随着时间的推移经济变量在不断的变化；“一种容易看见但不容易把握的变化”也存在与时间序列当中。这种变化可以是时间序列内在机制的突然改变、也可以是外部环境状态发生变迁所导致。如果知道这种“突变”发生的时点,则可以很好的通过分段研究来探索想要回答的问题。但这种变化恰恰非常难以把握,这是由于时间序列本来的变化掩盖了这种内在机制的变化。这显示出时间序列数据所具有的非线性性。当前,在不能断定“突变”发生的时点的模型中,学术界中使用较为平凡的处理这种非线性时间序列数据的模型最早在1989年有Hamilton提出。马科夫机制转换模型通过纳入多个结构方程,将时间序列的非线性通过变来在不同状态下的转变刻画出来。马科夫机制转换模型刻画的这种复杂的动态演化过程,使得研究这能够更好的探讨其所需解答问题。用机制转化模型对宏观经济和金融市场进行分析研究是一个非常热门的领域。 上面的方法结合到本文对黄金价格波动的研究：可以分三个层次。第一层次是对一般波动水平的马科夫机制转模型分析,第二层是分别利用三大属性对黄金价格波动影响的马科夫机制转模型分析,最后采用马科夫机制转换向量自回归过程将三大属性和黄金价格作为一个系统研究变量之间的相互作用。本文希望能尝试着回答一下几个问题：黄金价格波动受那些主要因素影响?黄金价格波动有什么样的特征?所提到的这些因素能否解释黄金价格的波动、以及怎样解释?即,黄金的商品属性、避险属性、货币属性在什么情况下、什么时间内对黄金价格产生了什么样的影响。 因此本文主要划分为以下几个部分： 第一部分、主要介绍全文的背景、研究问题、相关文献综述评论、文章结构和主要创新点、主要研究方法和所使用数据简介。主要研究方法方面介绍了：向量自回归方法、马科夫机制转化模型、HP滤波、邹检验。其中对本文所使用的主要方法马科夫机制转化模型进行了详细介绍。数据选取以及处理的目的和原因也进行了详细的介绍。 第二部分、对黄金价格波动因素及特征的初步探讨。利用马科夫机制转化模型对其价格波动性的探讨,即黄金价格的波动水平是否发生了结构性的转变。在设定两种机制的前提下,研究表明：第一种机制下,每周GFP的收益在-0.02%,为小幅下跌；第二种机制下,GFP的每周收益在0.28%作为,为较大幅度上涨。最后,用较小的篇幅以传统的VAR方法和GARCH方法对黄金价格波动的影响因素、波动特征进行了探讨。 第三至六部分作为一个整体分别完成了以下讨论： 1、利用马科夫机制转换模型对黄金价格和波动指数之间关系的探讨。发现两者之间波动关系可以大致分为三种状态：状态1,VIX的一单位正向变动可以导致GFP下跌4.693美元,其标准差为16.2%；状态2,VIX的一单位正向变动可以导致GFP下跌15.24美元,标准差为18.4%；状态3,VIX的一单位正向变动可以导致GFP上涨25.825美元,标准差为250.8%. 2、利用马科夫机制转换模型对黄金价格和美元指数之间关系的探讨。发现两者之间波动关系可以大致分为五种状态：状态1,USDX的一单位正向变动可以导致GFP下跌26.945美元；状态2,USDX的一单位正向变动可以导致GFP下跌1.042美元；状态3,USDX的一单位正向变动可以导致GFP下跌42.183美元。 3、利用马科夫机制转换模型对黄金价格和大宗商品价格指数之间关系的探讨。发现两者之间波动关系可以大致分为三种状态：状态1,DJUBSSP的一单位正向变动可以导致GFP上涨3.001美元；状态2,USDX的一单位正向变动可以导致GFP上涨1.856美元；状态3,USDX的一单位正向变动可以导致GFP上涨3.723美元。 4、利用马科夫机制转换向量自回归模型综合讨论了黄金价格波动与其商品、货币、避险属性三者内在的系统性关系。发现在状态1、状态2、状态3中,黄金的月收益率分别为-2.186%、16.175%和-0.621%。因此可以认为状态1、状态2、状态3分别代表黄金价格小幅下跌阶段、大幅上涨阶段和几乎没有涨跌的阶段。 第七部分、对上述分析进行了总结,并分析了研究可以改进的地方。
[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.
【学位授予单位】：西南财经大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F224;F830.94
本文编号：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.
【学位授予单位】：西南财经大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F224;F830.94
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