本文是分析自2000年初到2020年底發生了總計7次的護盤政策，這項政策對於臺灣加權指數的影響。透過不同的方法來檢測護盤政策的效果，首先使用OLS分析護盤政策的平均效果，以及是否具有外溢效果，再來是使用ARIMA來檢測政策是否能有顯著的正面衝擊，也就是能否讓指數止跌回升，最後則是使用隨機優越來檢測護盤期間的風險是否有變小。本文發現護盤期間的平均日報酬有顯著高於護盤前0.49%，而政策也具有外溢效果，並不是在國安基金所購買的股票才會有影響，在ARIMA的結果，本文發現政策除了能夠緩止住市場的快速下跌，有時候能夠有顯著的正面衝擊，讓市場止跌回升，最後在隨機優越上的結果也是發現大部分的時候護盤期間的風險有比護盤前期間還要小。總結所有結果，本文認為當重大負面事件發生時，政府的護盤政策是能夠有效穩定金融市場。

This paper analyzes the effect of stabilization policy on Taiwan capitalization weighted stock index which has occurred 7 times from the beginning of 2000 to the end of 2020. By using different methods to detect the effect of the stabilization policy. First, we use OLS to analyze the average effect of the stabilization policy and whether it has spillover effects. Then, using ARIMA to detect whether the policy can have a significant positive impact, that is, whether the policy can have a significant positive impact which can let the index stop falling and rebound, and finally use the stochastic dominance to detect whether the risk during the support period has decreased. We find that the average daily return during the policy period is significantly higher than 0.49% before the policy started, and the policy do have a spillover effect. It is not only the stocks purchased by the stabilization fund that will have the positive effect. According to the results of ARIMA, we find that the policy can not only slow decline, sometimes there can be a significant positive impact, allowing the market to stop falling and rebound. Finally, the result of the stochastic dominance is that most of the time the risk during the policy period is smaller than the period before the government’s intervention. Summarizing all the results, there are some evidences when a significant negative event occurs, the government's policy to intervene the market can effectively stabilize the financial market.

中文文獻
1. 馬黛、詹傑仲、胡德中，「政府干預股市的理論與實證分析：台灣股市的護盤實例」，財務金融學刊 ,10(3)，107-145頁，2002。

2. 黃柏凱、臧大年、何加政與張元晨，「國安基金以期貨維護現貨策略之分析」，管理學報，23 (1)，125-147頁，2006。

3. 詹傑仲 (2006)。「國家金融安定基金的價值及其對市場的影響:界限選擇權的應用。」 國立中山大學財務管理學系研究所博士論文，高雄市。

4. 張政閔 (2011)。「國家金融安定基金護盤股市短期宣示效果與長期實質效果之研究。」，銘傳大學企業管理學研究所碩士論文，臺北市。

5. 林宣文 (2016)。「護誰的盤？政商關係與國安基金護盤之關係。」，國立臺灣大學經 濟學研究所碩士論文，臺北市。

英文文獻

6. Acemoglu, Daron, and Thierry Verdier, 2000, The Choice between Market Failures and Corruption. American Economic Review, 90(1), 194-211.

7. Brown, S. J. and Warner, J. B., 1980, Measuring security price performance. Journal of Financial Economics, 8(3), 205– 258.

8. Box, G. E. P. and G. C. Tiao, 1975, Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association 70, 70– 79.

9. Box, G. E. P. and G. M. Jenkins, Time Series Analysis Forecasting and Control, Third Edition, Holden-Day, San Francisco, 1994.

10. Hami Levy, 1992, Stochastic dominance and expected utility: survey and analysis.Management science, 38(4), 555-593.

11. Horvath R, Petrovski D, 2012, International stock market integration: Central and South Eastern Europe compared. Economic Systems, 37(1), 81-91.

12. Kyungjoo L., Sehwan Y. and John J., 2007, Neural Network Model vs. SARIMA Model In Forecasting Korean Stock Price Index (KOSPI). Information System, 8(2), 372-378.

13. Liu, Y. J. & Yu, C. H., 2002, On The Effect of Stock Stabilization Fund: A Case of Taiwan., Review of Pacific Basin Financial Markets and Policies. 5(1), 93-109.

14. Nitin Merh, Vinod P. Saxena and Kamal Raj Pardasani, 2010, A Comparison Between Hybrid Approaches of ANN and ARIMA For Indian Stock Trend Forecasting. Journal of Business Intelligence, 3(2), 23-43.

15. Newey, W. K. & West, K. D., 1986, A simple, positive semi-definite, heteroskedasticity and autocorrelationconsistent covariance matrix. Econometrica 55, 703–708.

16. Rhee, S Ghon and Rosita P. Chang, 1993, The Microstructure of Asian Equity Markets. Journal of Financial Services Research 6, 437–454.

17. Sterba J. and Hilovska ,2010, The Implementation of Hybrid ARIMA Neural Network Prediction Model for Aggregate Water Consumption Prediction. Journal of Applied Mathematics, 3(3), 123-131.

18. William Schwert, 2011, Stock Volatility during the Recent Financial Crisis. European Financial Management, 17(5), 789–805.

19. Whaley, Robert E, 2009, Understanding the VIX. Journal of Portfolio Management, 35, 98– 105.