English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51483/86598 (59%)
Visitors : 8243896      Online Users : 47
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/51541

    Title: 波動預測績效比較 : 變幅為基礎 vs. 報酬率為基礎
    Other Titles: Comparison of volatility forecasting performance : range-based method vs. return-based method
    Authors: 章育瑄;Chang, Yu-hsuan
    Contributors: 淡江大學財務金融學系碩士班
    邱建良;Chiu, Chien-liang
    Keywords: GARCH模型;變幅;變幅波動;SPA test;GARCH models;Range;Range-Based Volatility;SPA test
    Date: 2010
    Issue Date: 2010-09-23 15:24:55 (UTC+8)
    Abstract: 本研究主要探討九個不同國家的股價指數:KOSPI(韓國KOSPI 股價指數)、NKI225(日經225 股價指數)、TAIEX(台灣加權股價指數)、DJIA(美國道瓊工業股價指數)、NDX(美國那史達克股價指數)、SPX(美國S&P500 股價指數)、CAC(法國CAC 40 股價指數)、FTSE(英國FTSE 100 股價指數)及 DAX(德國DAX 30 股價指數)波動度的特性,除了運用變幅單一變數來預測外,還將其拆成最高價及最低價二個變數,且分別利用 ARMA 模型、GARCH 模型、CARR 模型與 VECM 模型等不同波動度模型中配適出較適合各國股價指數波動度的模型。再者,本研究採用 Parkinson (1980)之變幅波動(range volatility)及報酬平方(squared return)作為真實波動度代理變數,並利用MSE、MAE、LLE、GMLE 等四種統計損失函數(loss function)及 VaR 財務績效評估,分別作為預測能力衡量指標,最後以 SPA 檢定各模型預測能力之優劣。實證結果為:當決策者使用 MAE 與 LLE 為損失函數時,則用 CARR 模型有較佳的預測能力;當決策者使用 MSE 與 GMLE 為損失函數時,則用 不對稱 GARCH 模型有較佳的預測能力。當決策者使用 VaR 財務績效評估時,除了 KOSPI、NKI225 和 TAIEX 是以不對稱 GARCH 模型有較佳的預測能力外,其餘股價指數皆是以 CARR 模型有較佳的預測能力。整體而言,由統計之觀點與財務之觀點來看波動度預測能力會得到相同的結論,各國股價指數不是以 CARR 模型預測較佳,就是以不對稱 GARCH 模型預測較佳。
    This article selects the appropriate model to match volatility of nine stock markets from ARMA, GARCH, CARR and VECM models and use range, high and low variables to match the models. In the meantime, we use Parkinson (1980) proposed ranged-based estimator and squared return to be the proxy of true volatility. This study not only uses statistic loss functions, including MAE, MSE, LLE, GMLE and the VaR performance assessments are based on the range of measures that address the accuracy and efficiency, but also employ more robust SPA test to compare forecasting performance of models. The empirical result indicates that, for MAE and LLE, CARR model is preferred.In addition, for MSE and GMLE, asymmetric GARCH models are preferred.For VaR based loss function, except for KOSPI, NKI225 and TAIEX, CARR model is preferred.In a word, for statistic and financial loss functions, there are high performance to forecast volatility of nine stock markets which is CARR model or asymmetric GARCH model be used. Therefore, alternative stock markets and loss functions are important for volatility forecasting.
    Appears in Collections:[財務金融學系暨研究所] 學位論文

    Files in This Item:

    File SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback