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題名：金融指數型基金調查：溢出效應、交易量、 和預測的實證模型
作者：胡柏森
作者(外文)：Sabbor Hussain
校院名稱：中原大學
系所名稱：商學博士學位學程
指導教授：陳若暉
學位類別：博士
出版日期：2023
主題關鍵詞：溢出效應；槓桿效應；交易量；指數型基金；波動性不對稱；灰關聯分析；隨機森林；機器學習；Spillover Effect；Leverage Effect；Trading Volume；Exchange Traded-Funds；ARMA；GARCH；EGARCH；MGARCH-BEKK；MGARCH-ADCC；Volatility Asymmetry；Grey Relational Analysis；Random Forest；Machine Learning.
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中文摘要
本研究包括三篇論文，使用各種模型研究金融指數型基金 (ETF) 的動態和績效表現。第一篇文章使用 GARCH-ARMA 和 EGARCH-ARMA 模型以及交易量動態，研究金融、金融科技和科技 ETFs 的溢出和槓桿效應。研究聚焦於各產業ETF與股市指數之間的顯著關聯性和相互依賴性，結果發現衝擊和波動性的傳遞。研究指出負面衝擊對波動性的影響更為明顯，而較高的波動性則對應著交易量的增加。分析顯示，金融科技和科技 ETF 在溢出效應、槓桿效應以及波動動態方面表現較佳。這些行業表現具有較強的抗衝擊能力。正面消息對市場的影響大於負面消息，表明科技類資產投資的潛在偏好。
第二篇論文利用 MGARCH-ADCC 和 MGARCH-BEKK 模型研究醫藥、保險和醫療保健 ETF 之間的動態關係。研究結果表明這些部門之間存在顯著的相互聯繫和共同的潛在因素。不對稱波動性溢出效應的存在，顯示一個行業的衝擊對其他行業的波動性影響更大。研究顯示隨著時間的推移，會發生的大規模衝擊的聚集，並提供對各行業內平均波動和波動性波動的現象。此發現對ETF 行業的投資策略、風險管理和投資組合多元化具有重要影響。此外，基於更大的對數似然估計，BEKK 模型優於 ADCC 模型。
第三項研究採用灰色關聯分析 (GRA) 和機器學習模型來預測銀行、保險和金融科技 ETF 的波動性。藉由特徵重要性和灰色關聯等級 (GRG) 衡量的影響分析，確定這些產業 ETF表現的重要變量，例如納斯達克保險指數、納斯達克銀行指數、和波動性指數。通過使用高 GRG 變量，根據 RMSE 和 MAE 測量結果，顯示隨機森林 (RF) 模型在預測所有三個部門的 ETF 表現方面均優於其他模型。本研究提供對有影響力的金融變量的見解，使金融 ETF 市場的政策制定者、基金經理和投資者能夠做出明智的決策。
總的來說，本文有助於更深入地了解各個 ETF 行業的動態和表現。研究結果增強投資組合管理策略、風險評估和預測能力，支持利益相關者在金融市場做出明智的決策。
Abstract
This research encompasses three essays that investigate the dynamics and performance of Financial Exchange-Traded Funds (ETFs) using various models. The first essay examines the spillover and leverage effects of Financial, FinTech, and Technology ETFs, using GARCH-ARMA and EGARCH-ARMA models as well as trading volume dynamics. The findings highlight significant interconnectedness and interdependencies between these ETF sectors and the stock market index. The results indicated the transmission of shocks and volatility. The study also found that negative shocks have a more pronounced impact on volatility, while higher volatility corresponds to increased trading volume. The analysis showed that FinTech and Technology ETFs exhibited more favorable outcomes in terms of spillover and leverage effects, as well as volatility dynamics. These sectors displayed stronger resilience to shocks, and positive news had a greater impact on the market than negative news, indicating a potential preference for technology-related assets investments.
The second paper utilizes MGARCH-ADCC and MGARCH-BEKK models to investigate the dynamics relationship among Pharmaceutical, Insurance, and Healthcare ETFs. The findings indicate significant interconnections and shared underlying factors among these sectors. The presence of asymmetric volatility spillovers suggests that shocks in one sector have a greater impact on the volatility of the others. The study also highlights the clustering of large shocks over time and provides insights into mean and volatility fluctuations within the sectors. These findings have important implications for investment strategies, risk management, and portfolio diversification in these ETFs sector. Based on the greater log likelihood estimations, the BEKK model outperforms the ADCC model.
The third study employs Grey Relational Analysis (GRA) and machine learning models to forecast the volatility of Bank, Insurance, and FinTech ETFs. The analysis identifies significant variables measured by feature importance and Grey Relational Grades (GRGs) that influence the performance of these ETF sectors, such as the Nasdaq Insurance Index, Nasdaq Bank Index, and volatility index. By using high GRGs variables, the study demonstrates that the Random Forest (RF) model consistently outperforms other models in forecasting the performance of ETFs across all three sectors, as measured by RMSE and MAE measurements. The research provides insights into influential financial variables, enabling informed decision-making for policymakers, fund managers, and investors in the financial ETF market.
Collectively, these essays contribute to a deeper understanding of the dynamics and performance of various ETF sectors. The findings enhance portfolio management strategies, risk assessment, and forecasting capabilities, supporting stakeholders in making informed decisions in the financial markets.
References
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