查詢結果分析
相關文獻
- 油價對股價預測能力之研究
- 臺灣股價之預測
- 股價預測模式的建構與投資決策
- 差分需求體系模型之建構與選擇--以臺灣地區總和食品需求為例
- 在股利宣告事件效應下股價預測模式之研究
- Spatial Model Selection Using Bayes Factor and Ratio of Variabilities for Asthma Mortality Data
- 臺灣肉品需求的函數擇選及弱分割性的檢定
- 臺灣證券市場股價預測模型之建構--以總體經濟因素與公司財務指標為基礎
- Choosing the Best Competition Models for the Margarine Industry in the United States
- 國際油價變動下主要國家之因應政策
頁籤選單縮合
題 名 | 油價對股價預測能力之研究=The Impact of Oil Price on Stock Return Predictability |
---|---|
作 者 | 胡育豪; | 書刊名 | 全球商業經營管理學報 |
卷 期 | 3 2011.09[民100.09] |
頁 次 | 頁131-142 |
分類號 | 563.54 |
關鍵詞 | 油價變動; 股價預測; 樣本外; 模型選擇; Oil price change; Stock return predictability; Out-of-sample; Model selection; |
語 文 | 中文(Chinese) |
中文摘要 | 本篇文章的主要目的是以多國的觀點,檢視油價變動對於股價樣本外的預測能力,研究對象包含美國、日本、澳洲、加拿大、英國、法國等先進國家及香港、新加坡、韓國及台灣等亞洲國家,研究期間為1974:1到2009:12的月資料。過去關於股價預測的文章大多採取財務變數或總體變數的方法,如本利比(Price-Dividend ratio)或利率。文獻上雖然有些變數被認為是預測股價的較佳解釋變數,但對於利用油價預測股價的文章相對討論較少,本文依據Driesprong Jacobsen and Matt(2008)的方法,建立不同期間的股價預測方程式,利用CW(Clark and West(2007))樣本外預測的統計量檢驗,並以bootstrap 的方法建立分配,進行不同預測期間油價變動對股價報酬之樣本外預測能力的推論。另外,為了比較油價在預測能力上是否較目前所使用的預測變數,如本利比(Price dividend ratio)或利率較好,本文在第二部分也將利用Giacomini and Rossi(2010)模型選擇的方法進行油價、本益比與利率在解釋股價變動能力上之比較。 |
英文摘要 | The purpose of this article is to examine the influence of the change in oil price on the stock market return. The paper focuses on eleven countries, including G7 countries and four Asian emerging countries. We start our analysis at 1974:1, when oil prices started to fluctuate. All series end in 2009:12, so we construct the result on the 36 years of monthly observation. Numerous empirical studies have investigated the predictability of stock returns using financial ratio and macroeconomics variable, like Price-Dividend ratio and various interest rates. Although some variables have been suggested by the academic literature to be good predictors of stock return, there is very little research on how the stock market reacts to oil price changes. Following the predicting equation in Driesprong Jacobsen and Matt (2008), we try to evaluate the strong presumption in the financial press that oil drives the stock market in terms of the out-of-sample predicting performance. We employ CW (Clark and West (2007)) statistics, recently developed inference procedure for testing equal predictability of two nested models. The inference of significance based on the critical values from a bootstrap procedure. Furthermore, for comparing the predictive ability of oil price and other variable, we use the methods of model selection in Giacomini and Rossi(2010) in effort to find whether the consideration of oil price changes can enhance the evidence of stock return predictability. |
本系統中英文摘要資訊取自各篇刊載內容。