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    题名: FAVAR模型與分量因子模型的應用: 油價衝擊對於股市表現的影響
    The Application of FAVAR and Quantile Factor Model: the Impact of Oil Price Shock on Stock Market Performance
    作者: 林煜軒
    Lin, Yu-Hsuan
    贡献者: 徐士勛
    Hsu, Shih-Hsun
    林煜軒
    Lin, Yu-Hsuan
    关键词: 油價衝擊
    股市表現
    因子擴充向量自我迴歸模型
    分量因子模型
    Oil price shock
    Stock market performance
    FAVAR
    QFM
    日期: 2023
    上传时间: 2023-07-10 11:52:02 (UTC+8)
    摘要: 本研究試圖檢視美國、台灣、日本、韓國等四個已開發國家的股市受
    到油價衝擊後的影響,樣本期間為 2000 年 1 月至 2022 年 2 月,共 266期月資料。我們將資料分成世界層級 (world-level) 與國家特定 (countryspecific) 兩類,總計整理 45 個變數。

    Chen, Dolado, and Gonzalo (2021) 的分量因子模型 (Quantile Factor model, QFM),使研究者透過不同分量的分析,獲得資料分配較完整特性。過去文獻發現,Bernanke, Boivin, and Eliasz (2005) 的因子擴充向量自我迴歸模型 (Factor Augmented Vector Autoregression model, FAVAR),雖使研究能納入高維度資料,並改善傳統向量自我迴歸模型 (VectorAutoregression model, VAR) 缺失,但實證卻顯示影響不對稱,資料存在異質性疑慮。我們擴張 FAVAR 模型,加入分量因子估計,並分析其衝擊反應。最後討論並比較原始及擴充模型。

    本文實證發現,原始模型結論與過去類似,油價上漲衝擊對於各國股
    市初期影響負面,之後反應漸不同。影響約於一年內較明顯,三年後幾近
    消失,經濟恢復均衡。另外,所考慮擴充模型的重要結論則大致與原始模
    型相同,但仍具備些許差異,顯示 QFM 確實捕捉更多資訊。
    This research intends to examine the impact of oil price shock on stock markets of US, Taiwan, Japan, and Korea from January 2000 to February 2022 (266 months). We separate our data into two categories: world-level and country specific. There are 45 variables.

    The Quantile Factor model (QFM) by Chen, Dolado, and Gonzalo
    (2021) lets researchers analyze data under different quantile and obtain more complete characteristics of the distribution of the data. From previous studies, the Factor Augmented Vector Autoregression model (FAVAR) by Bernanke, Boivin, and Eliasz (2005) is known that, although it can include high dimensional data in research and improve Vector Autoregression model (VAR), results show that the responses are asymmetric. Thus, data may exist heterogeneity. We expand FAVAR, add quantile factors to estimate together, and
    analyze the impulse response. Finally, we compare two FAVARs.

    Our empirical findings show that results of the original model are similar to past studies. The impact of oil price shock to four countries is negative at first, and responses are different after that. The impact is larger during first
    year and disappears after three years. The economy achieves stability. Besides, conclusions in both models are alike but have differences, pointing out QFM utilizes more information.
    參考文獻: Aastveit, K. A. (2014), “Oil Price Shocks in a Data-rich Environment,” Energy Economics, 45, 268-279.

    Aastveit, K. A., H. C. Bjørnland, and L. A. Thorsrud (2014), “What Drives Oil Prices? Emerging versus Developed Economies,” Journal of Applied Econometrics, 30, 1013-1028.

    Bai, J. , and S. Ng (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70(1), 191-221.

    Basher, S. A. and P. Sadorsky (2006), “Oil Price Risk and Emerging Stock Markets,” Global Finance Journal, 17(2), 224-251.

    Bernanke, B. S., J. Boivin, and P. Eliasz (2005), “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach,”The Quarterly Journal of Economics, 120(1), 387–422.

    Bohi, D. R. (1991), “On the Macroeconomic Effects of Energy Price Shocks,” Resources and Energy, 13(2), 145-162.

    Chen, L., J. J. Dolado, and J. Gonzalo (2020), “Quantile Factor Models,” Working Paper.

    Chen, L., J. J. Dolado, and J. Gonzalo (2021), “Quantile Factor Models,” Econmetrica, 89(2), 875-910.

    Choi, S., D. Furceri, P. Loungani, S. Mishra, and M. Poplawski-Ribeiro (2018),“Oil Prices and Inflation Dynamics: Evidence from Advanced and Developing Economies,” Journal of International Money and Finance, 82, 71-96.

    Cholette, P. A. (1984), “Adjusting Sub-annual Series to Yearly Benchmarks,” Survey Methodology, 10, 35-49.

    Cong, R. G., Y. M. Wei, J. L. Jiao, and Y. Fan (2008), “Relationships between Oil Price Shocks and Stock Market: An Empirical Analysis from China,” Energy Policy, 36, 3544-3553.

    Cunado, J. and F. Perez de Gracia (2005), “Oil Prices, Economic Activity and Inflation: Evidence for Some Asian Countries,” The Quarterly Review of Economics and Finance, 45(1), 65-83.

    Denton, F. T. (1971),“Adjustment of Monthly or Quarterly Series to Annual Totals:An Approach Based on Quadratic Minimization,” Journal of the American Statistical Association, 66, 99-102.

    Fayyad, A. and K. Daly (2011), “The Impact of Oil Price Shocks on Stock Market Returns: Comparing GCC Countries with the UK and USA,” Emerging Markets Review, 12(1), 61-78.

    Ferderer, J. P. (1996), “Oil Price Volatility and the Macroeconomy,” Journal of Macroeconomics, 18(1), 1-26.

    Griffin, J. M. and H. B. Steele (1986), Energy Economics and Policy, Orlando, Florida: Academic Press.

    Hamilton, J. D. (1983), “Oil and the Macroeconomy since World War II,” Journal of Political Economy, 91(2), 228-248.

    Hamilton, J. D. and A. M. Herrera (2004), “Oil Shocks and Aggregate Macroeconomic Behavior: The Role of Monetary Policy,” Journal of Money, Credit and Banking, 36(2), 265-286.

    Hwang, R. D., R. W. Masulis, and H. R. Stoll (1996),“Energy Shocks and Financial Markets,” Journal of Futures Markets, 16(1), 1-27.

    Jiménez-Rodríguez, R. and M. Sánchez (2005), “Oil Price Shocks and Real GDP Growth: Empirical Evidence for Some OECD Countries,” Applied Economics,37(2), 201-228.

    Jones, C. M. and G. Kaul (1996), “Oil and the Stock Markets,” The Journal of Finance, 51(2), 463-491.

    Kilian, L. and C. Park (2009), “The Impact of Oil Price Shocks on the U.S. Stock Market,” International Economic Review, 50(4), 1267-1287.

    Kim, I. M. and P. Loungani (1992), “The Role of Energy in Real Business Cycle Models,” Journal of Monetary Economics, 29(2), 173-189.

    Koenker, R. and G. Bassett (1978), “Regression Quantiles,” Econometrica, 46(1), 33-50.

    Lee, C. C. and J. H. Zeng (2011), “The Impact of Oil Price Shocks on Stock Market Activities: Asymmetric Effect with Quantile Regression,” Mathematics and Computers in Simulation, 81, 1910-1920.

    Mork, K. A. (1989), “Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton’s Results,” Journal of Political Economy, 97(3), 740-744.

    Mork, K. A., ∅. Olsen, and H. T. Mysen (1994), “Macroeconomic Responses to Oil Price Increases and Decreases in Seven OECD Countries,” The Energy
    Journal, 15(4), 19-35.

    Naser, H. and A. Rashid (2018), “Oil Price Shocks and Stock Market Performance in the BRICs: Some Evidence using FAVAR Models,” Economic Issues, 23(2), 85-108.

    Nusair, S. A. and J. A. Al-Khasawneh (2018), “Oil Price Shocks and Stock Market Returns of the GCC Countries: Empirical Evidence from Quantile Regression Analysis,” Economic Change and Restructuring, 51, 339-372.

    Papapetrou, E. (2001), “Oil Price Shocks, Stock Market, Economic Activity and Employment in Greece,” Energy Economics, 23(5), 511-532.

    Park, J. and R. A. Ratti (2008), “Oil Price Shocks and Stock Markets in the U.S. and 13 European Countries,” Energy Economics, 30(5), 2587-2608.

    Phillips, P. C. B. and P. Perron (1988), “Testing for a unit root in time series regression,” Biometrika, 75(2), 335-346.

    Sadorsky, P. (1999), “Oil Price Shocks and Stock Market Activity,” Energy Economics, 21(5), 449-469.

    Sax, C. and P. Steiner (2013), “Temporal Disaggregation of Time Series,” The R Journal, 5(2), 80-87.

    Sims, C. A. (1980), “Macroeconomics and Reality,” Econometrica, 48(1), 1-48.

    Stock, J. H. and M. W. Watson (2002), “Forecasting Using Principal Components From a Large Number of Predictors,” Journal of the American Statistical Association, 97, 1167-1179.

    Wang, Y., C. Wu, and L. Yang (2013), “Oil Price Shocks and Stock Market Activities: Evidence from Oil-importing and Oil-exporting Countries,” Journal of Comparative Economics, 41(4), 1220–1239.
    描述: 碩士
    國立政治大學
    經濟學系
    110258013
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110258013
    数据类型: thesis
    显示于类别:[經濟學系] 學位論文

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