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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/111090

    Title: 極值分配在投資組合風險值估計上之實證分析
    Other Titles: The empirical analysis of portfolio Value-at-Risk estimations incorporating extreme value distribution
    Authors: 程偉倫;Cheng, Wei-Lun
    Contributors: 淡江大學統計學系碩士班
    林志娟;Lin, Jyh-Jiuan
    Keywords: 風險值;極值分配;等加權移動平均;指數加權移動平均;蒙地卡羅模擬法;Value-at-Risk;Extreme value distribution;Equal Weights Moving Average;Exponentially Weighted Moving Average;Monte Carlo Simulation Method
    Date: 2016
    Issue Date: 2017-08-24 23:43:43 (UTC+8)
    Abstract: 由於全球金融貿易國際化以及經濟迅速成長,整個金融市場快速的發展,許多衍生性金融商品興起,資產的流動性提高,但市場風險也相對增加,然而近年全球性的金融危機事件頻傳,導致金融市場遭受重創,使得風險控制逐漸地開始被重視,如何正確地衡量風險,成為了重要的課題之一。
    Thanks to the internationalization of global financial trading and the rapid growth of economics, the whole financial market has also expanded rapidly. Many financial derivatives have been aroused. Although the assets liquidity has increased, the market risk has also increased relatively. In recent years, the global financial crisis occurred frequently. This led to the serious hit of financial market. Moreover, people have paid more attention on the risk control. How to evaluate the investment risk has become an important issue.

    In this thesis, we used historical simulation method, variance-covariance method and Monte Carlo simulation method to evaluate the investment risk. Among them, equal weights moving average and exponentially weighted moving average are used to estimate the variance-covariance matrix. In addition, the simulation of assets correlation and the heavy-tailed distribution of return on assets are considered by Monte Carlo simulation method. Through the abovementioned method combined with the empirical data, we constructed three evaluation models of Value-at-Risk. Finally, we made a comparison between these three models. The empirical results show two important points. First, according to the efficiency of funds use and back-testing, if the risk control tends to conservatism, historical simulation method is recommended. Second, if investors hope to have a high efficiency of funds use and they are able to take high risk, they can choose to use Monte Carlo method to simulate the heavy-tailed distribution.
    Appears in Collections:[Graduate Institute & Department of Statistics] Thesis

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