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https://ah.lib.nccu.edu.tw/handle/140.119/146859| 題名: | 基於產業類別之S&P500與美國十年期公債DCC動態條件相關性分析 The analysis of Dynamic Conditional Correlations between S&P500 and US 10 Year Treasury based on different industries |
作者: | 何建志 He, Chien-Chih |
貢獻者: | 張興華 Chang, Hsing-Hua 何建志 He,Chien-Chih |
關鍵詞: | 股債動態條件相關性 產業別 資料頻率 DCC MV-GARCH Model |
日期: | 2023 | 上傳時間: | 1-Sep-2023 | 摘要: | 股票與債券為市場上最為普遍的金融資產，深入了解股債相關性可以增進資產配置效益、提升股債再平衡效率，活化避險策略。而美國又是全球發展最蓬勃的金融市場，因此本研究以美國十年期公債與S & P 500及S & P 500不同產業為標的，評估其股債動態條件相關係數。\n本研究利用DCC MV-GARCH模型計算出之股債動態條件相關係數，而第一個研究目標，即為了解股債動態條件相關係數在日資料與月資料中，隨時間的走勢變化。實證顯示，股債動態條件相關係數在2022年聯準會快速升息下，有明顯的上升趨勢，且日資料較月資料存在更明顯的波動持續性與叢聚性。\n第二與第三個研究目標，分別為利用OLS迴歸分析評估VIX、MOVE、美元指數、黃金現貨價格、產業股價指數交易量對股債動態條件相關係數於不同產業與不同資料頻率之影響。實證結果顯示，面對VIX指數改變，股債動態條件相關係數在不同資料頻率下會有相反之變化，可能原因包含投資人情緒、再平衡、資金調配、風險控制、安全性資產與風險性資產間的轉移等行為。而面對MOVE指數、美元指數變動，股債動態條件相關係數同樣在不同資料頻率下容易有相反之變化，可能原因包含安全性資產與風險性資產間的轉移、總體經濟、貨幣政策等因素。產業之特性、資料頻率的差異都會造成股債動態條件相關係數在變化幅度上的異同。股價指數交易量變動，則容易在短期內造成股債動態條件相關係數正向變化。 Stocks and bonds are the most common financial assets in the market. Understanding the correlation between them can enhance asset allocation efficiency, improve stock-bond rebalancing, and activate hedging strategies. This study focuses on the dynamic conditional correlation between U.S. ten-year Treasury bonds, the S&P 500 index, and its various sectors.\n\nUsing the DCC MV-GARCH model, we calculate the dynamic conditional correlation. The first goal is to track how this correlation changes over time in daily and monthly data. Empirical evidence shows that amid the 2022 Federal Reserve interest rate hikes, the correlation exhibited a notable upward trend. Daily data also displayed more pronounced volatility and clustering compared to monthly data.\n\nThe second and third objectives involve analyzing the impact of VIX, MOVE, the U.S. dollar index, gold prices, and industry stock index trading volumes on the dynamic conditional correlation in different industries and data frequencies. Results indicate that changes in the VIX index lead to opposite effects at different data frequencies, possibly due to investor sentiment, rebalancing, capital allocation, risk control, and asset transfers between safety and risk categories. Similar effects are observed with changes in the MOVE index and the U.S. dollar index. Differences in industry characteristics and data frequency contribute to variations in the dynamic conditional correlation. Changes in stock index trading volumes can induce short-term positive dynamic conditional correlations. |
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資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0110352005 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
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