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| Title: | 長期追蹤資料之分位數迴歸分析：探討股票波動性與流動性的影響因素
Quantile Regression Analysis of Longitudinal Data: Investigating the Determinants of Stock Volatility and Liquidity |
| Authors: | 涂筱宜
Tu, Shiau-Yi |
| Contributors: | 鄭宗記
Cheng, Tsung-Chi
涂筱宜
Tu, Shiau-Yi |
| Keywords: | 長期追蹤資料
分位數迴歸
線性混合效應模型
有限混合模型
赤池資訊準則
貝氏資訊準則
波動性
流動性
Longitudinal Data
Quantile Regression
Linear Mixed Effects Model
Finite Mixture Model
Akaike Information Criterion
Bayesian Information Criterion
Volatility
Liquidity |
| Date: | 2025 |
| Issue Date: | 2025-08-04 15:11:21 (UTC+8) |
| Abstract: | 本研究以2014年第一季至2024年第二季台灣 1003 家上市公司的縱向資料為基礎，旨在探討影響個股波動性與流動性的因素，並捕捉在不同市場風險情境下的異質性反應。傳統迴歸方法多聚焦於平均效應，難以掌握極端情境下的非對稱性與潛在結構變異。本研究採用分位數迴歸模型作為主要分析工具，涵蓋 0.1 至 0.9 分位，全面剖析 ETF 持股比例、市值、股價等因素在不同分位下的影響力。研究建構了四種模型架構進行比較，包括傳統分位數迴歸（QR）、時間固定效應模型（TC）、時間變動效應模型（TV）以及結合兩者特性的 TCTV模型，並透過赤池資訊準則（AIC）與貝氏資訊準則（BIC）評估其適配性。實證結果顯示，多項變數在市場極端情境下對個股波動性與流動性具有顯著影響，部分變數呈現顯著非對稱效應，強調分位數模型在捕捉市場風險異質性及提升政策敏感度上的重要性。總體而言，TCTV 模型結合個股間的結構性異質性與時間序列上的動態轉換，能更全面刻劃市場波動性與流動性的潛在異質性與演變機制，展現優異的解釋力與擬合度，亦為後續跨市場比較與模型應用提供重要依據。
This study is based on longitudinal data from 1,003 listed companies in Taiwan spanning from the first quarter of 2014 to the second quarter of 2024. It aims to investigate the factors influencing individual stock volatility and liquidity, and to capture heterogeneous responses under different market risk scenarios. Traditional regression methods often focus on mean effects, making it difficult to grasp asymmetries and latent structural variations under extreme conditions. Therefore, this study adopts the quantile regression as the main analytical tool, covering quantiles from 0.1 to 0.9, to comprehensively analyze the influence of factors such as ETF ownership ratio, market capitalization and stock price across different quantiles. The study constructs and compares four model frameworks, including the traditional quantile regression (QR), the Time-constant model (TC), the Time-varying model (TV), and the TCTV model that combines features of both. Model fit is assessed using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The empirical results show that several variables have significant effects on stock volatility and liquidity under extreme market conditions, with some exhibiting sigificant asymmetric effects, highlighting the importance of quantile models in capturing market risk heterogeneity and enhancing the sensitivity of policy supervision. Overall, the TCTV model combines structural heterogeneity across individual stocks and dynamic transitions in the time series dimension, enabling a more comprehensive depiction of the underlying heterogeneity and evolving mechanisms of market volatility and liquidity. It demonstrates excellent explanatory power and model fit, and also provides an important basis for future cross-market comparisons and model applications. |
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| Description: | 碩士
國立政治大學
統計學系
112354019 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112354019 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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