本文關(guān)鍵詞：風(fēng)險(xiǎn)度量中的信息熵方法研究　出處：《北京交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

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【摘要】：受經(jīng)濟(jì)全球化和金融一體化的影響,各國(guó)的投資市場(chǎng)迅猛發(fā)展的同時(shí),投資風(fēng)險(xiǎn)也在隨著悄然增長(zhǎng)。投資者總是期望獲取穩(wěn)定的收益,規(guī)避不必要的風(fēng)險(xiǎn),因此如何利用合理科學(xué)的方法來(lái)準(zhǔn)確地度量投資中的風(fēng)險(xiǎn),不僅是投資者關(guān)心的問(wèn)題,而且已經(jīng)是現(xiàn)代金融學(xué)研究的一項(xiàng)重要的課題。 在此背景下本文綜述了一系列度量風(fēng)險(xiǎn)的方法,尤其強(qiáng)調(diào)近幾年來(lái)興起的利用信息熵理論來(lái)度量風(fēng)險(xiǎn),分章介紹如下： 第一章首先介紹了風(fēng)險(xiǎn)度量方法產(chǎn)生的背景、國(guó)內(nèi)外研究現(xiàn)狀以及本文主要內(nèi)容和安排。 第二章綜述了幾種傳統(tǒng)經(jīng)典的風(fēng)險(xiǎn)度量方法,分別回顧了其發(fā)展歷程,給出了模型并對(duì)其分析了各自的優(yōu)缺點(diǎn)。 第三章介紹了熵的起源與發(fā)展、信息熵的定義與性質(zhì)以及最大熵與最小相對(duì)熵原理,這為第四章提供了必要的理論支持。 第四章是本文的重點(diǎn)。在第二章的基礎(chǔ)上,我們介紹了幾種用信息熵來(lái)度量風(fēng)險(xiǎn)的模型,從理論上給出了模型的具體形式并分析了模型的特點(diǎn)與適用范圍。 第五章采用實(shí)證分析的方法,選取了50只較好的股票作為分析的樣本,通過(guò)Matlab編程計(jì)算比較了熵模型和傳統(tǒng)的風(fēng)險(xiǎn)度量模型的異同,最后得出了熵模型在風(fēng)險(xiǎn)度量中的優(yōu)勢(shì)。最后一章是本文的總結(jié)以及對(duì)后續(xù)工作的展望。
[Abstract]:Under the influence of economic globalization and financial integration, with the rapid development of investment markets in various countries, the investment risk is also increasing quietly. Investors always expect to obtain stable returns and avoid unnecessary risks. Therefore, how to use reasonable and scientific methods to accurately measure the risks in investment is not only an issue that investors are concerned about, but also an important subject in modern finance research. In this context, this paper summarizes a series of risk measurement methods, especially emphasizes the use of information entropy theory to measure risk in recent years. The first chapter introduces the background of risk measurement, the current research situation at home and abroad, and the main content and arrangement of this paper. In the second chapter, several classical risk measurement methods are reviewed, their development process is reviewed, models are given and their advantages and disadvantages are analyzed. The third chapter introduces the origin and development of entropy, the definition and properties of information entropy and the principle of maximum entropy and minimum relative entropy, which provides the necessary theoretical support for Chapter 4th. Chapter 4th is the focus of this paper. On the basis of the second chapter, we introduce several models to measure risk by information entropy, give the concrete form of the model theoretically and analyze the characteristics and application scope of the model. Chapter 5th uses the empirical analysis method, selects 50 good stock as the analysis sample, through the Matlab programming computation has compared the entropy model and the traditional risk measurement model similarities and differences. Finally, the advantage of entropy model in risk measurement is obtained. The last chapter is the summary of this paper and the prospect of future work.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：F830.59

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本文編號(hào)：1420244