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題名 隨機森林演算法於GARCH模型波動性預測之改進及關鍵指標分析 - 以比特幣為例
Improvement and importance indicator analysis on Volatility Forecasting of GARCH Model by Random Forest Algorithm - Case of Bitcoins作者 盧禹叡
Lu, Yu-Ruei貢獻者 林靖<br>蕭明福
Lin, Ching<br>Shiau, Ming-Fu
盧禹叡
Lu, Yu-Ruei關鍵詞 數位貨幣
比特幣
GARCH模型
隨機森林演算法
波動性預測
隨機森林重要性排序
關鍵指標分析
機器學習
Cryptocurrency
Bitcoin
Machine Learning
Random Forest Importance
Random Forest
Volatility Forecasting
Indicators analyzing
GARCH model日期 2022 上傳時間 1-Aug-2022 18:28:13 (UTC+8) 摘要 本研究以機器學習方法對比特幣報酬的波動率進行相關研究，並比 較一般化自回歸異質變異數模型(GARCH model)與機器學習模型對 比特幣報酬波動性的預測結果和所得出的重要影響指標探討。首先根 據過去文獻整理影響比特幣報酬波動性的外生指標並依據三個步驟進 行模型的建構和外生指標的分析。第一，運用日內資料進行實際波動 率的計算，並以隨機森林重要性排序(Random forest importance)的 方式對此實際波動率進行外生指標的挑選，依據此挑選結果進行模型 的建構和指標的分析;第二，使用 GARCH(1,1) 模型捕捉比特幣報酬 全樣本的波動性，並分別以 GARCH(1,1) 模型和機器學習模型對此波 動性進行樣本外的預測，並比較模型之間的預測結果，找出能夠最準 確對比特幣報酬波動性進行預測的模型;第三，依據具有最優預測結 果模型中的外生指標進行分析，了解影響比特幣報酬波動性預測之外 生指標及其原因。本研究實證結果顯發現，機器學習模型對預測結果 的改進可以達到預測誤差最小的效果，此外，在選擇預測比特幣報酬 波動性所使用的外生指標時，引入機器學習的相關方法可以找出具有 關鍵影響力的外生指標。
This study uses machine learning methods to study the volatility of bitcoin re- turns,compares the prediction results of the Generalized Autoregressive Heteroge- neous Variance model (GARCH model) and the machine learning model.The im- portant indicator will also be discussed.According to the past literature, the exoge- nous indicators that affect the volatility of Bitcoin’s return are sorted out. First, the realized volatility is calculated by the intraday data and sort the exogenous indica- tors of this actual volatility by Random forest importance selection; Second, use the GARCH(1,1) model and machine learning model to predict the volatility out of sample, and compare the prediction results between these models to find the model have the best prediction; Third, analyzing the exogenous indicators in models with optimal predictive outcomes to understand the affection of exogenous indicators . The empirical results shows that the improvement by machine learning method can obtain the minimize prediction error. In addition, when selecting the exogenous indicators used to predict the volatility of Bitcoin’s return, the related methods of machine learning can find the exogenous indicators with key influence.參考文獻 Aalborg, H. A., Molnár, P., and de Vries, J. E. (2019). What can explain the price, volatility and trading volume of bitcoin? Finance Research Letters, 29:255–265.Aharon, D. Y., Umar, Z., and Vo, X. V. (2021). Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies. Financial Innovation, 7(1):1–25.Alessandretti, L., ElBahrawy, A., Aiello, L. M., and Baronchelli, A. (2018). Anticipating cryptocurrency prices using machine learning. Complexity, 2018.Aras, S. (2021). Stacking hybrid garch models for forecasting bitcoin volatility. Expert Systems with Applications, 174:114747.Awartani, B. M. and Corradi, V. (2005). Predicting the volatility of the s&p-500 stock index via garch models: the role of asymmetries. International Journal of forecasting, 21(1):167–183.Baur, D. G. and Dimpfl, T. (2021). The volatility of bitcoin and its role as a medium of exchange and a store of value. Empirical Economics, 61(5):2663–2683.Bouoiyour, J., Selmi, R., and Wohar, M. E. (2019). Safe havens in the face of presidential election uncertainty: A comparison between bitcoin, oil and precious metals. Applied Economics, 51(57):6076–6088.Bouri, E., Gkillas, K., Gupta, R., and Pierdzioch, C. (2021). Forecasting realized volatility of bitcoin: The role of the trade war. Computational Economics, 57(1):29–53.Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.Chen, W., Xu, H., Jia, L., and Gao, Y. (2021). Machine learning model for bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting, 37(1):28–43.Conlon, T. and McGee, R. (2020). Safe haven or risky hazard? bitcoin during the covid-19 bear market. Finance Research Letters, 35:101607.Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2):174–196.Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–a garch volatility analysis. Finance Research Letters, 16:85–92.Elsayed, A. H., Gozgor, G., and Lau, C. K. M. (2022). Causality and dynamic spillovers among cryptocurrencies and currency markets. International Journal of Finance & Economics, 27(2):2026–2040.Fang, T., Su, Z., and Yin, L. (2020). Economic fundamentals or investor perceptions? the role of uncertainty in predicting long-term cryptocurrency volatility. International Review of Financial Analysis, 71:101566.Franses, P. H. and Van Dijk, D. (1996). Forecasting stock market volatility using (non- linear) garch models. Journal of forecasting, 15(3):229–235.Garcia-Jorcano, L. and Benito, S. (2020). Studying the properties of the bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying. Research in International Business and Finance, 54:101300.Görgen, K., Meirer, J., and Schienle, M. (2022). Predicting value at risk for cryptocurren- cies using generalized random forests. arXiv preprint arXiv:2203.08224.Gradojevic, N., Kukolj, D., Adcock, R., and Djakovic, V. (2021). Forecasting bitcoin with technical analysis: A not-so-random forest? International Journal of Forecasting.Huang, Y., Duan, K., and Mishra, T. (2021). Is bitcoin really more than a diversifier? a pre-and post-covid-19 analysis. Finance Research Letters, 43:102016.Huynh, T. L. D., Burggraf, T., and Wang, M. (2020). Gold, platinum, and expected bitcoin returns. Journal of Multinational Financial Management, 56:100628.Jaquart, P., Dann, D., and Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The journal of finance and data science, 7:45–66.Katsiampa, P. (2017). Volatility estimation for bitcoin: A comparison of garch models. Economics Letters, 158:3–6.Köchling, G., Schmidtke, P., and Posch, P. N. (2020). Volatility forecasting accuracy for bitcoin. Economics Letters, 191:108836.Kristjanpoller, W. and Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial neural network-garch model. Expert Systems with Applications, 65:233–241.Liang, C., Zhang, Y., Li, X., and Ma, F. (2022). Which predictor is more predictive for bit- coin volatility? and why? International Journal of Finance & Economics, 27(2):1947– 1961.López-Cabarcos, M. Á., Pérez-Pico, A. M., Piñeiro-Chousa, J., and Šević, A. (2021). Bitcoin volatility, stock market and investor sentiment. are they connected? Finance Research Letters, 38:101399.Luong, C. and Dokuchaev, N. (2018). Forecasting of realised volatility with the random forests algorithm. Journal of Risk and Financial Management, 11(4):61.Malladi, R. K. and Dheeriya, P. L. (2021). Time series analysis of cryptocurrency returns and volatilities. Journal of Economics and Finance, 45(1):75–94.Mensi, W., Sensoy, A., Aslan, A., and Kang, S. H. (2019). High-frequency asymmetric volatility connectedness between bitcoin and major precious metals markets. The North American Journal of Economics and Finance, 50:101031.Milunovich, G. and Lee, S. A. (2021). Cryptocurrency exchanges: Predicting which mar- kets will remain active. Journal of Forecasting.Moussa, W., Mgadmi, N., Béjaoui, A., and Regaieg, R. (2021). Exploring the dynamic relationship between bitcoin and commodities: New insights through stecm model. Re- sources Policy, 74:102416.Naimy, V., Haddad, O., Fernández-Avilés, G., and El Khoury, R. (2021). The predictive capacity of garch-type models in measuring the volatility of crypto and world curren- cies. PloS one, 16(1):e0245904.Nti, K. O., Adekoya, A., and Weyori, B. (2019). Random forest based feature selection of macroeconomic variables for stock market prediction. American Journal of Applied Sciences, 16(7):200–212.Pabuçcu, H., Ongan, S., and Ongan, A. (2020). Forecasting the movements of bitcoin prices: an application of machine learning algorithms. Quantitative Finance and Eco- nomics, 4(4):679–692.Qiu, Y., Wang, Z., Xie, T., and Zhang, X. (2021). Forecasting bitcoin realized volatility by exploiting measurement error under model uncertainty. Journal of Empirical Finance, 62:179–201.Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., and Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71:804–818.Su, F., Wang, X., and Yuan, Y. (2022). The intraday dynamics and intraday price discovery of bitcoin. Research in International Business and Finance, 60:101625.Tan, C.-Y., Koh, Y.-B., Ng, K.-H., and Ng, K.-H. (2021). Dynamic volatility modelling of bitcoin using time-varying transition probability markov-switching garch model. The North American Journal of Economics and Finance, 56:101377.Tiwari, A. K., Kumar, S., and Pathak, R. (2019). Modelling the dynamics of bitcoin and litecoin: Garch versus stochastic volatility models. Applied Economics, 51(37):4073– 4082.Trucíos, C. (2019). Forecasting bitcoin risk measures: A robust approach. International Journal of Forecasting, 35(3):836–847.Urquhart, A. and Zhang, H. (2019). Is bitcoin a hedge or safe haven for currencies? an intraday analysis. International Review of Financial Analysis, 63:49–57.Wakefield, K. (2019). A guide to machine learning algorithms and their applications.undated, SAS. com,< https://www. sas. com/en_gb/insights/articles/analytics/machine- learning-algorithms. html.Walther, T., Klein, T., and Bouri, E. (2019). Exogenous drivers of bitcoin and cryptocur- rency volatility–a mixed data sampling approach to forecasting. Journal of Interna- tional Financial Markets, Institutions and Money, 63:101133. 描述 碩士
國立政治大學
經濟學系
109258026資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109258026 資料類型 thesis dc.contributor.advisor 林靖<br>蕭明福 zh_TW dc.contributor.advisor Lin, Ching<br>Shiau, Ming-Fu en_US dc.contributor.author (Authors) 盧禹叡 zh_TW dc.contributor.author (Authors) Lu, Yu-Ruei en_US dc.creator (作者) 盧禹叡 zh_TW dc.creator (作者) Lu, Yu-Ruei en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 18:28:13 (UTC+8) - dc.date.available 1-Aug-2022 18:28:13 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 18:28:13 (UTC+8) - dc.identifier (Other Identifiers) G0109258026 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141251 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 109258026 zh_TW dc.description.abstract (摘要) 本研究以機器學習方法對比特幣報酬的波動率進行相關研究，並比 較一般化自回歸異質變異數模型(GARCH model)與機器學習模型對 比特幣報酬波動性的預測結果和所得出的重要影響指標探討。首先根 據過去文獻整理影響比特幣報酬波動性的外生指標並依據三個步驟進 行模型的建構和外生指標的分析。第一，運用日內資料進行實際波動 率的計算，並以隨機森林重要性排序(Random forest importance)的 方式對此實際波動率進行外生指標的挑選，依據此挑選結果進行模型 的建構和指標的分析;第二，使用 GARCH(1,1) 模型捕捉比特幣報酬 全樣本的波動性，並分別以 GARCH(1,1) 模型和機器學習模型對此波 動性進行樣本外的預測，並比較模型之間的預測結果，找出能夠最準 確對比特幣報酬波動性進行預測的模型;第三，依據具有最優預測結 果模型中的外生指標進行分析，了解影響比特幣報酬波動性預測之外 生指標及其原因。本研究實證結果顯發現，機器學習模型對預測結果 的改進可以達到預測誤差最小的效果，此外，在選擇預測比特幣報酬 波動性所使用的外生指標時，引入機器學習的相關方法可以找出具有 關鍵影響力的外生指標。 zh_TW dc.description.abstract (摘要) This study uses machine learning methods to study the volatility of bitcoin re- turns,compares the prediction results of the Generalized Autoregressive Heteroge- neous Variance model (GARCH model) and the machine learning model.The im- portant indicator will also be discussed.According to the past literature, the exoge- nous indicators that affect the volatility of Bitcoin’s return are sorted out. First, the realized volatility is calculated by the intraday data and sort the exogenous indica- tors of this actual volatility by Random forest importance selection; Second, use the GARCH(1,1) model and machine learning model to predict the volatility out of sample, and compare the prediction results between these models to find the model have the best prediction; Third, analyzing the exogenous indicators in models with optimal predictive outcomes to understand the affection of exogenous indicators . The empirical results shows that the improvement by machine learning method can obtain the minimize prediction error. In addition, when selecting the exogenous indicators used to predict the volatility of Bitcoin’s return, the related methods of machine learning can find the exogenous indicators with key influence. en_US dc.description.tableofcontents 誌謝.............................................. i摘要.............................................. ii Abstract............................................ iii目次.............................................. iv圖目錄 ............................................ vi表目錄 ............................................ vii第一章 緒論 ........................................ 1第一節 研究背景與動機.............................. 1第二節 研究目的.................................. 5第三節 研究方法與流程.............................. 7第四節 章節架構.................................. 10第二章 文獻回顧...................................... 11第一節 影響數位貨幣波動性關鍵指標之文獻回顧 ............... 11第二節 預測模型關鍵指標挑選方法及準則之文獻回顧 ............ 13第三節 使用GARCH模型預測波動性之文獻回顧 ............... 14第四節 使用機器學習演算法結合GARCH模型之文獻回顧 . . . . . . . . . . 17第三章 研究方法...................................... 20第一節 研究流程概述............................... 20第二節 資料衡量與資料集建構.......................... 22第三節 關鍵指標挑選與預測模型架構之建立.................. 24第四節 預測模型評估............................... 32第四章 實證結果...................................... 37第一節 資料搜集與預處理結果.......................... 37第二節 關鍵指標之選擇.............................. 43第三節 預測模型之假設與預測結果之取得 ................... 50第四節 預測結果及評估.............................. 56第五章 結論與建議 .................................... 65第一節 結論 .................................... 65第二節 研究限制.................................. 68第三節 未來建議.................................. 69參考文獻........................................... 71 zh_TW dc.format.extent 12114958 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109258026 en_US dc.subject (關鍵詞) 數位貨幣 zh_TW dc.subject (關鍵詞) 比特幣 zh_TW dc.subject (關鍵詞) GARCH模型 zh_TW dc.subject (關鍵詞) 隨機森林演算法 zh_TW dc.subject (關鍵詞) 波動性預測 zh_TW dc.subject (關鍵詞) 隨機森林重要性排序 zh_TW dc.subject (關鍵詞) 關鍵指標分析 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) Cryptocurrency en_US dc.subject (關鍵詞) Bitcoin en_US dc.subject (關鍵詞) Machine Learning en_US dc.subject (關鍵詞) Random Forest Importance en_US dc.subject (關鍵詞) Random Forest en_US dc.subject (關鍵詞) Volatility Forecasting en_US dc.subject (關鍵詞) Indicators analyzing en_US dc.subject (關鍵詞) GARCH model en_US dc.title (題名) 隨機森林演算法於GARCH模型波動性預測之改進及關鍵指標分析 - 以比特幣為例 zh_TW dc.title (題名) Improvement and importance indicator analysis on Volatility Forecasting of GARCH Model by Random Forest Algorithm - Case of Bitcoins en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Aalborg, H. A., Molnár, P., and de Vries, J. E. (2019). What can explain the price, volatility and trading volume of bitcoin? Finance Research Letters, 29:255–265.Aharon, D. Y., Umar, Z., and Vo, X. V. (2021). Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies. Financial Innovation, 7(1):1–25.Alessandretti, L., ElBahrawy, A., Aiello, L. M., and Baronchelli, A. (2018). Anticipating cryptocurrency prices using machine learning. Complexity, 2018.Aras, S. (2021). Stacking hybrid garch models for forecasting bitcoin volatility. Expert Systems with Applications, 174:114747.Awartani, B. M. and Corradi, V. (2005). Predicting the volatility of the s&p-500 stock index via garch models: the role of asymmetries. International Journal of forecasting, 21(1):167–183.Baur, D. G. and Dimpfl, T. (2021). The volatility of bitcoin and its role as a medium of exchange and a store of value. Empirical Economics, 61(5):2663–2683.Bouoiyour, J., Selmi, R., and Wohar, M. E. (2019). Safe havens in the face of presidential election uncertainty: A comparison between bitcoin, oil and precious metals. Applied Economics, 51(57):6076–6088.Bouri, E., Gkillas, K., Gupta, R., and Pierdzioch, C. (2021). Forecasting realized volatility of bitcoin: The role of the trade war. Computational Economics, 57(1):29–53.Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.Chen, W., Xu, H., Jia, L., and Gao, Y. (2021). Machine learning model for bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting, 37(1):28–43.Conlon, T. and McGee, R. (2020). Safe haven or risky hazard? bitcoin during the covid-19 bear market. Finance Research Letters, 35:101607.Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2):174–196.Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–a garch volatility analysis. Finance Research Letters, 16:85–92.Elsayed, A. H., Gozgor, G., and Lau, C. K. M. (2022). Causality and dynamic spillovers among cryptocurrencies and currency markets. International Journal of Finance & Economics, 27(2):2026–2040.Fang, T., Su, Z., and Yin, L. (2020). Economic fundamentals or investor perceptions? the role of uncertainty in predicting long-term cryptocurrency volatility. International Review of Financial Analysis, 71:101566.Franses, P. H. and Van Dijk, D. (1996). Forecasting stock market volatility using (non- linear) garch models. Journal of forecasting, 15(3):229–235.Garcia-Jorcano, L. and Benito, S. (2020). Studying the properties of the bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying. Research in International Business and Finance, 54:101300.Görgen, K., Meirer, J., and Schienle, M. (2022). Predicting value at risk for cryptocurren- cies using generalized random forests. arXiv preprint arXiv:2203.08224.Gradojevic, N., Kukolj, D., Adcock, R., and Djakovic, V. (2021). Forecasting bitcoin with technical analysis: A not-so-random forest? International Journal of Forecasting.Huang, Y., Duan, K., and Mishra, T. (2021). Is bitcoin really more than a diversifier? a pre-and post-covid-19 analysis. Finance Research Letters, 43:102016.Huynh, T. L. D., Burggraf, T., and Wang, M. (2020). Gold, platinum, and expected bitcoin returns. Journal of Multinational Financial Management, 56:100628.Jaquart, P., Dann, D., and Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The journal of finance and data science, 7:45–66.Katsiampa, P. (2017). Volatility estimation for bitcoin: A comparison of garch models. Economics Letters, 158:3–6.Köchling, G., Schmidtke, P., and Posch, P. N. (2020). Volatility forecasting accuracy for bitcoin. Economics Letters, 191:108836.Kristjanpoller, W. and Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial neural network-garch model. Expert Systems with Applications, 65:233–241.Liang, C., Zhang, Y., Li, X., and Ma, F. (2022). Which predictor is more predictive for bit- coin volatility? and why? International Journal of Finance & Economics, 27(2):1947– 1961.López-Cabarcos, M. Á., Pérez-Pico, A. M., Piñeiro-Chousa, J., and Šević, A. (2021). Bitcoin volatility, stock market and investor sentiment. are they connected? Finance Research Letters, 38:101399.Luong, C. and Dokuchaev, N. (2018). Forecasting of realised volatility with the random forests algorithm. Journal of Risk and Financial Management, 11(4):61.Malladi, R. K. and Dheeriya, P. L. (2021). Time series analysis of cryptocurrency returns and volatilities. Journal of Economics and Finance, 45(1):75–94.Mensi, W., Sensoy, A., Aslan, A., and Kang, S. H. (2019). High-frequency asymmetric volatility connectedness between bitcoin and major precious metals markets. The North American Journal of Economics and Finance, 50:101031.Milunovich, G. and Lee, S. A. (2021). Cryptocurrency exchanges: Predicting which mar- kets will remain active. Journal of Forecasting.Moussa, W., Mgadmi, N., Béjaoui, A., and Regaieg, R. (2021). Exploring the dynamic relationship between bitcoin and commodities: New insights through stecm model. Re- sources Policy, 74:102416.Naimy, V., Haddad, O., Fernández-Avilés, G., and El Khoury, R. (2021). The predictive capacity of garch-type models in measuring the volatility of crypto and world curren- cies. PloS one, 16(1):e0245904.Nti, K. O., Adekoya, A., and Weyori, B. (2019). Random forest based feature selection of macroeconomic variables for stock market prediction. American Journal of Applied Sciences, 16(7):200–212.Pabuçcu, H., Ongan, S., and Ongan, A. (2020). Forecasting the movements of bitcoin prices: an application of machine learning algorithms. Quantitative Finance and Eco- nomics, 4(4):679–692.Qiu, Y., Wang, Z., Xie, T., and Zhang, X. (2021). Forecasting bitcoin realized volatility by exploiting measurement error under model uncertainty. Journal of Empirical Finance, 62:179–201.Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., and Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71:804–818.Su, F., Wang, X., and Yuan, Y. (2022). The intraday dynamics and intraday price discovery of bitcoin. Research in International Business and Finance, 60:101625.Tan, C.-Y., Koh, Y.-B., Ng, K.-H., and Ng, K.-H. (2021). Dynamic volatility modelling of bitcoin using time-varying transition probability markov-switching garch model. The North American Journal of Economics and Finance, 56:101377.Tiwari, A. K., Kumar, S., and Pathak, R. (2019). Modelling the dynamics of bitcoin and litecoin: Garch versus stochastic volatility models. Applied Economics, 51(37):4073– 4082.Trucíos, C. (2019). Forecasting bitcoin risk measures: A robust approach. International Journal of Forecasting, 35(3):836–847.Urquhart, A. and Zhang, H. (2019). Is bitcoin a hedge or safe haven for currencies? an intraday analysis. International Review of Financial Analysis, 63:49–57.Wakefield, K. (2019). A guide to machine learning algorithms and their applications.undated, SAS. com,< https://www. sas. com/en_gb/insights/articles/analytics/machine- learning-algorithms. html.Walther, T., Klein, T., and Bouri, E. (2019). Exogenous drivers of bitcoin and cryptocur- rency volatility–a mixed data sampling approach to forecasting. Journal of Interna- tional Financial Markets, Institutions and Money, 63:101133. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200550 en_US
