博碩士論文 etd-0623112-002029 詳細資訊


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姓名 楊韓緻 (Han-Chih Yang) 電子郵件信箱 E-mail 資料不公開
畢業系所 財務管理學系研究所(Finance)
畢業學位 博士(Ph.D.) 畢業時期 100學年第2學期
論文名稱(中) VAR模型-股票市場危機的預測   
論文名稱(英) Predicting Stock Market Crises by VAR Model
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    摘要(中) 在現在的學術上,有許多研究方法來預測金融危機。而許多金融機構也採用了代表性的指標來預測危機。這些方法和指標雖然不可能直接評估,但還是以不同的估計方式估來呈現,並朝各方面的發展。儘管,至今仍無法證明哪一方法或指標最為適當,我們仍然試圖找到具有某一特性的產業能幫助我們在股票危機發生前提前預警做出適當地應變措施。
    在本文中,我們採用的資料為S&P100,時間期間為1977年1月至2008年12的月資料。我們先將資料透過Fama-French三因子加上動能因子和投資人情緒做資料上的處理,接著透過集群分析分成四個組群。最後,我們採用GARCH-SGT模型,並運用VAR來預測股市危機。
    本文中,我們發現預測股市危機的關鍵因素是峰態值,越高的峰態值越具有預測的能力而非高的波動性。此外,當產業具有較大規模這項特性時,也較有可以預測股市危機的能力。另一方面,我們也可以透過此模型來做到確認的目的,藉此,我們可以在危機發前做適當地風險控管以降低損失。
    摘要(英) There are several methods to predict financial crises. There are also several types of indicators used by financial institutions. These indicators, which are estimated in different ways, often show various developments, although it is not possible to directly assess which is the most suitable. Here, we still try to find what characteristics that industry group has and forecast financial crises
    In this paper, our data started from monthly of 1977 January to 2008 December in S&P100. We consider Fama-French and Cluster Analysis to process data to make data with same characteristic within a group. Then, we use GARCH type models and apply it to VaR predicting stock turmoil.
    In conclusion, we found that the group which has high kurtosis value is the key factor for predicting stock crises instead of volatility. Moreover, the characteristics of this industry which can predict stock crises is a great scale. On the other hand, we can through this model to double check the reaction for anticipating. Therefore, people can do some actions to control risk to reduce the loss.
    關鍵字(中)
  • SGT分配
  • VaR模型
  • GARCH模型
  • 股票危機
  • 危機預測
  • 預警系統
  • 關鍵字(英)
  • stock crises
  • GARCH
  • VaR
  • SGT
  • early warning system
  • predicting crises
  • 論文目次 論文審定書 i
    摘要 ii
    Abstract iii
    Tables v
    Figures vi
    1. Introduction 1
    2. Method 6
    2.1 GARCH (1, 1) model with skewed generalized t distribution (GARCH-SGT) 6
    2.2 skewed general t density 6
    2.3 Measurement and evaluation for distribution-based VaR models 8
    2.3.1 Definition and estimation 8
    2.3.2 Conditional-SGT-VaR approach 8
    2.4 Evaluating VaR performance 8
    2.4.1 Unconditional coverage test (LRuc) 9
    2.4.2 Conditional coverage test (LRcc) 9
    3. Data 11
    4. Empirical result 12
    4.1 Result of stock market 12
    4.2 Evaluting the performance of VaR 17
    4.3 Analysis of clustering 17
    5. Conclusion 19
    Reference 21
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    口試委員
  • 郭修仁 - 召集委員
  • 李建強 - 委員
  • 王昭文 - 指導教授
  • 黃振聰 - 指導教授
  • 口試日期 2012-06-12 繳交日期 2012-06-23

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