產業指數上下行波動預測 -可解釋性多元因子

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：38

、訪客IP：18.219.131.216

姓名

廖武靖(LIAO,WU-CHING) 查詢紙本館藏

畢業系所

財務金融學系

論文名稱

產業指數上下行波動預測 -可解釋性多元因子
(Industry Index UP and Down Volatility Forecast: An Interpretable Multivariate Factor Model)

相關論文

★ 國內股票型共同基金異常報酬之特徵研究	★ 台灣境外高收益債券型基金績效分析
★ 財富管理客戶選擇銀行之因素探討	★ 境外匯回專法實施前後境外資金解決方案比較－以個案分析為例
★ 利用隨機優勢方法探究商品指數之投資績效	★ 承銷關係是否會影響未來承銷業務?
★ 併購動能:以台灣市場為例	★ 機構法人對股票報酬與公司價值之影響
★ 投資者情緒與期貨價格關聯性	★ 避險基金指數是否能夠提供風險分散效果？- 利用均異擴張檢定
★ Model-Free隱含波動度價差之遠期資訊	★ 公開市場購回股票之研究
★ Modeling Long Run Risk with Macroeconomic Fundamentals	★ Exploration of Jumps and Cojumps in Financial Markets
★ 社會責任指數與環境、社會及公司治理之關聯性分析-以FTSE4Good系列指數為例	★ 運用檢定資產價格泡沫模型建構動態財務危機預警之驗證

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2026-7-18以後開放)

摘要(中)

本文分別以美國耐久財、能源、科技與製造業的上市公司波動作為預測目標，並以 Inter‑Quantile‑Range‑based Volatility （IQRBV）作為波動代理並進行預測，樣本期間為 1990 年 1 月至 2021 年 12 月的月頻率資料，變數種類包含公司基本面的財務、經營、技術、供應、企業社會責任類別，以及代表其他未來資訊的情緒和總體經濟等類別共 414 個變數，並藉由不同的降維演算法萃取類別因子，並輸入分量迴歸預測分位數(10%,50%,90%)並依此建構 IQRBV。首先在四個產業中科技業波動預測表現最佳能源業次之，再者，形成波動的可能原因隨同時間變化，因此不同時空背景與經濟結構下，選擇使用何種變數因子與模型預測波動至關重要。最重要的是，本研究的模型不僅能預測產業的上行和下行波動，還能將這些波動與經濟因素結合，深入探討波動背後的影響因素。這種方法有助於市場參與者在決策時提高依據性，從而增強其資產配置和風險管理決策之間的因果關係。

摘要(英)

This article targets the volatility of publicly listed companies in the U.S. durable
goods, energy, technology, and manufacturing sectors, utilizing the Inter-QuantileRange-based Volatility (IQRBV) as a proxy for volatility forecasting. The data spans
from January 1990 to December 2021 with a monthly frequency, incorporating 414
variables across categories including financial fundamentals, operations, technology,
supply, corporate social responsibility, as well as sentiment and macroeconomic
indicators that represent additional future information. Dimensionality reduction
algorithms are employed to extract categorical factors, which are then input into
quantile regression to predict volatility at the 10%, 50%, and 90% quantiles for the
construction of IQRBV.
Among the four sectors, technology exhibits the best performance in volatility
forecasting, followed by energy. Moreover, the factors contributing to volatility evolve
over time, indicating the critical importance of selecting appropriate variables and
models for volatility forecasting under different temporal and economic contexts. Most
importantly, the models developed in this study are capable not only of forecasting
industry-specific upward and downward volatility but also of integrating these
volatilities with economic factors to delve deeper into the underlying drivers of
volatility. This approach aids market participants in enhancing the substantiation of
their decision-making processes, thereby improving the causal relationship between
asset allocation and risk management decisions.

關鍵字(中)

★ 波動
★ 半波動
★ 降維
★ 解構
★ 樣本外預測

關鍵字(英)

★ Volatility
★ Semi-Volatility
★ Dimensionality Reduction
★ Out-of-sample Forecasting

論文目次

摘要.................................................................................................................................i
Abstract..........................................................................................................................ii
目錄.............................................................................................................................. iii
一、緒論........................................................................................................................1
二、資料........................................................................................................................4
2.1 資料來源..........................................................................................................4
2.2 資料清理..........................................................................................................4
2.3 建立產業投組..................................................................................................4
2.4 區分類別變數..................................................................................................5
三、研究方法................................................................................................................8
3.1 第一階段-降維方法 ......................................................................................10
3.1.1 主成分分析 PCA...............................................................................10
3.1.2 偏最小平方迴歸 PLS........................................................................10
3.1.3 偏分量迴歸 PQR...............................................................................10
3.2 第二階段-遞迴式迴歸 ..................................................................................11
3.3 波動代理與基準模型....................................................................................12
3.1.1 預測目標:已實現波動 .......................................................................12
3.3.2 基於分位數範圍的波動性.................................................................13
3.3.3 基礎模型:異質性自我迴歸模型 .......................................................13
3.4 模型預測能力衡量方法................................................................................14
3.5 重要類別衡量方法........................................................................................14
四、實證結果..............................................................................................................15
4.1 耐久財產業....................................................................................................15
4.2 能源業............................................................................................................16
4.3 科技業............................................................................................................17
4.4 製造業............................................................................................................18
4.5 小節................................................................................................................19
4.6 關鍵類別因子................................................................................................20
4.6.1 能源業與科技業下行波動重要因子比較.........................................20
4.6.2 科技業上下行波動重要因子比較.....................................................21
五、模型經濟價值—交易策略應用..........................................................................23
5.1 科技業指數 Ratio 擇時策略........................................................................23
5.2 重要因子選股策略........................................................................................25
六、結論......................................................................................................................27
參考文獻......................................................................................................................28
附錄..............................................................................................................................32

參考文獻

Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P., 2003, Modeling and
Forecasting Realized Volatility, Econometrica, 71(2), 579-625.
Andersen, T. G., and Benzoni, L., 2008, Realized Volatility, Working Paper 2008-14.
Barndorff-Nielsen, O. E., Kinnebrock, S., and Shephard, N., 2008, Measuring
Downside Risk-Realised Semivariance, CREATES Research Paper, 2008-42.
Bollerslev, T., Hood, B., Huss, J., and Pedersen, L. H., 2018, Risk Everywhere:
Modeling and Managing Volatility, The Review of Financial Studies, 31(7), 2729-
2773.
Bond, S., 2007, An Econometric Model of Downside Risk, Forecasting Volatility in the
Financial Markets, 301-331.
Brownlees, C. T., Engle, R. F., and Kelly, B. T, 2009, A Practical Guide to Volatility
Forecasting Through Calm and Storm, In URL http://ssrn.com/abstract, 1502915.
Chen, Andrew Y., and Tom Zimmermann, 2021, Finance and Economics Discussion
Series Divisions of Research and Statistics and Monetary Affairs Federal Reserve
Board, Washington, D.C., Open Source Cross-Sectional Asset Pricing.
Christoffersen, P. F., and Diebold, F. X., 2000, How Relevant Is Volatility Forecasting
For Financial Risk Management, Review of Economics and Statistics, 82(1), 12-
22.
Chou, R. Y., Chou, H., and Liu, N., 2010, Range Volatility Models and Their
Applications In Finance, Springer US, 1273-1281.
Chun, D., Cho, H., and Ryu, D., 2022, Forecasting Stock Market Volatility and
Application To Volatility Timing Portfolios, Available at SSRN 4167561.
Dodge, Y., and Whittaker, J., 2009, Partial Quantile Regression, Biometrica Metrika,
70, 35-57.
Decomposed Parts, Journal of Empirical Finance, 45, 228-242.
Feltham, G. A., and Ohlson, J. A., 1995, Valuation and Clean Surplus Accounting For
Operating and Financial Activities, Contemporary Accounting Research, 11(2),
689-731.
Giglio, S., Kelly, B., and Pruitt, S., 2016, Systemic Risk and The Macroeconomy: An
empirical evaluation, Journal of Financial Economics, 119(3), 457-471.
Giglio, S., Kelly, B., and Pruitt, S., 2016, Systemic Risk and The Macroeconomy: An
Empirical Evaluation, Journal of Financial Economics, 119(3), 457-471.
Gkillas, K., Gupta, R., and Pierdzioch, C., 2019, Forecasting (Downside and Upside)
Realized Exchange-Rate Volatility: Is There a Role For Realized Skewness and
Kurtosis, Physica A: Statistical Mechanics and its Applications, 532, 121867.
Granger, C. W., and Poon, S. H., 2001, Forecasting Financial Market Volatility: A
review, Available at SSRN 268866.
Harvey, C. R., Liu, Y., and Zhu, H., 2016, … and The Cross-Section of Expected
Returns, The Review of Financial Studies, 29(1), 5-68.
Hol Uspensky, E., and Koopman, S. J., 2002, Stock Index Volatility Forecasting With
High Frequency Data, Tinbergen Institute Discussion Paper, No. 02-068/4
Hou, K., Xue, C., and Zhang, L., 2020, Replicating Anomalies, The Review of Financial
Studies, 33(5), 2019-2133.
Méndez-Civieta, Á., Aguilera-Morillo, M. C., and Lillo, R. E., 2022, Fast Partial
Quantile Regression, Chemometrics and Intelligent Laboratory Systems, 223,
104533.
Mittnik, S., Robinzonov, N., and Spindler, M., 2015, Stock Market Volatility:
Identifying Major Drivers and The Nature of Their Impact, Journal of Banking
and Finance, 58, 1-14.
Nawrocki, D. N., 1999, A Brief History of Downside Risk Measures, The Journal of
Investing, 8(3), 9-25.
Nelson, D. B., 1991, Conditional Heteroskedasticity In Asset Returns: A New Approach,
Econometrica: Journal of the Econometric Society, 347-370.
Pedro Brito, R., Sebastião, H., and Godinho, P., 2016, Efficient Skewness/Semivariance
Portfolios, Journal of Asset Management, 17, 331-346.
Pierdzioch, C., Döpke, J., and Hartmann, D., 2008, Forecasting Stock Market Volatility
With Macroeconomic Variables In Real Time, Journal of Economics and Business,
60(3), 256-276.
Salah, H. B., Gannoun, A., and Ribatet, M., 2016, Conditional Mean-Variance and
Mean-Semivariance Models In Portfolio Optimization, Journal of Statistics and
Management Systems, 23(8), 1333-1356
Silva, D. B. D., 2021, Semivolatility Managed Portfolios, Doctoral dissertation,
Fundação Getulio Vargas
Stock, J. H., and Watson, M. W., 2002, Forecasting Using Principal Components From
a Large Number of Predictors, Journal of the American Statistical Association,
97(460), 1167-1179.
Taylor, S. J., 1994, Modeling Stochastic Volatility: A Review and Comparative Study,
Mathematical Finance, 4(2), 183-204.
West, K. D., and Cho, D., 1995, The Predictive Ability of Several Models of Exchange
Rate Volatility, Journal of Econometrics, 69(2), 367-391.
Yeh, J. H., Wang, J. N., and Kuan, C. M., 2014, A Noise-Robust Estimator of Volatility
Based On Interquantile Ranges, Review of Quantitative Finance and Accounting,
43, 751-779.

指導教授

葉錦徽(Jin‑Huei Yeh)

審核日期

2024-7-22

推文