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1Academic Journal
المؤلفون: 陳宗岡, 蔡宗勝, 黃美玲, Chen, Tsung-Kang, Tsai, Tsung-Sheng, Huang, Mei-Ling
المساهمون: 會計評論
مصطلحات موضوعية: 指數股票型基金持股比例, 持股波動度, 信用風險, 價格發現假說, ETF ownership level, ETF ownership volatility, Credit Risk, Price discovery hypothesis
وصف الملف: 588868 bytes; application/pdf
Relation: 會計評論, 76, 95-143; https://nccur.lib.nccu.edu.tw//handle/140.119/143248; https://nccur.lib.nccu.edu.tw/bitstream/140.119/143248/1/index.html
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2Dissertation/ Thesis
المؤلفون: 張祐誠, Chang, Yu-Cheng
المساهمون: 王儷玲, Wang, Li-Ling
مصطلحات موضوعية: 永續投資, 永續評級, 投資績效, 波動度, ESG ETFs, sustainable investing, sustainability ratings, nvestment performance, volatility
وصف الملف: 1975762 bytes; application/pdf
Relation: 王儷玲、黃泓智、楊曉文、彭金隆(2023)。全方位退休理財與保險規劃。台北:台灣金融 研訓院 Gillan, Stuart L., Andrew Koch and Laura T. Starks (2021), “ Firms and social responsibility: A review of ESG and CSR research in corporate finance,” Journal of Corporate Finance, 66, 101889. Albuquerque, Rui, Yrjö Koskinen and Chendi Zhang (2019), “Corporate social responsibility and firm risk: Theory and empirical evidence, ” Management Science, 65(10), 4451- 4469. Buchanan, Bonnie, Cathy Xuying Cao and Chongyang Chen (2018), “Corporate social responsibility, firm value, and influential institutional ownership, ” Journal of Corporate Finance, 52, 73-95. Humphrey, Jacquelyn E., Darren D. Lee and Yaokan Shen (2012), “Does it cost to be sustainable?, ” Journal of Corporate Finance, 18(3), 626-639. DiBartolomeo, D., & Kurtz, L. (1996). Socially screened portfolios: An attribution analysis of relative performance. The Journal of Investing, 5(3), 59-64. Statman, M. (2000). Socially responsible mutual funds. Financial Analysts Journal, 56(3), 30-39. Gerasimos G. Rompotis. (2023). ESG ETF Performance Analysis. Journal of Financial Markets, 45, 123-145. Kanuri, S. (2020). ESG ETF Performance and Risk Analysis. Journal of Sustainable Finance & Investment, 10(4), 385-407. Thinking Ahead Institute. (2024). Global Pension Assets Study. Thinking Ahead Institute. Lee, L.-E. (2023). Measuring ESG impact and integration: Challenges and insights. Journal of Sustainable Finance & Investment, 13(1), 45-60. Morningstar, Inc. (2021). Morningstar Sustainability Rating Methodology.; G0111358027; https://nccur.lib.nccu.edu.tw//handle/140.119/153065; https://nccur.lib.nccu.edu.tw/bitstream/140.119/153065/1/802701.pdf
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3Dissertation/ Thesis
المؤلفون: 洪詩涵, Hung, Shih-Han
المساهمون: 郭維裕, Guo, Wei-Yu
مصطلحات موضوعية: 總體經濟指標, 股市報酬, 報酬率波動度, 綜合指數, Macroeconomic Indicators, Stock Return, Return Volatility, Composite Index
وصف الملف: 1253335 bytes; application/pdf
Relation: Boyd, John H., Jian Hu, and Ravi Jagannathan, 2005, The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks, The Journal of Finance 60(2), 649-672. Boyarchenko, Nina, Richard K. Crump, Anna Kovner, and Or Shachar, 2022, Measuring Corporate Bond Market Dislocations, FRB of New York Staff Report 957. Bruno, Valentina, and Hyun Song Shin, 2012, Capital Flows and the Risk-Taking Channel of Monetary Policy, Journal of Monetary Economics 71, 119-132. Chen, Nai-Fu, Richard Roll, and Stephen A. Ross, 1986, Economic Forces and the Stock Market, The Journal of Business 59(3), 383-403. Clewell, David, Chris Faulkner-Macdonagh, David Giroux, Sébastien Page, and Charles Shriver, 2017, Macroeconomic Dashboards for Tactical Asset Allocation, The Journal of Portfolio Management 44(2), 50-61. Gourinchas, Pierre-Olivier, and Maurice Obstfeld, 2012, Stories of the Twentieth Century for the Twenty-First, American Economic Journal 4(1), 226-265. Greenwood, Robin, Samuel G. Hanson, Andrei Shleifer, and Jakob Ahm Sørensen, 2022, Predictable Financial Crises, Journal of Finance 77(2), 863-921. Homa, Kenneth E., and Dwight M. Jaffee, 1971, The Supply of Money and Common Stock Prices, Journal of Finance 26(5), 1045-1066. Kose, M. Ayhan, Christopher Otrok, and Charles H. Whiteman, 2003, International Business Cycles: World, Region, and Country-Specific Factors, The American Economic Review 93(4), 1216-1239. Miranda-Agrippino, Silvia, and Hélène Rey, 2015, World asset markets and the global financial cycle, CEPR Discussion Papers, No. 10936. Petkova, Ralitsa, 2006, Do the Fama-French Factors Proxy for Innovations in Predictive Variables?, Journal of Finance 61(2), 581-612 . Rapach, David E., Jesper Rangvid, and Mark E. Wohar, 2005, Macro variables and international stock return predictability, International Journal of Forecasting 21 (1), 137-166. Rey, Hélène, 2015, Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence, NBER Working Paper 21162. 黃雅楨, 2018, VIX指數、總體經濟變數與台灣加權股價指數關聯性之分析, 碩士論文, 國立政治大學. 楊士漢, 2008, 台灣股市與美國那斯達克的動態相關性分析, 碩士論文, 國立交通大學. 劉照群, 2008, 美國費城半導體股、台灣股市、台積電股價的關聯性之實證研究, 碩士論文, 國立臺北大學.; G0111351034; https://nccur.lib.nccu.edu.tw//handle/140.119/152402; https://nccur.lib.nccu.edu.tw/bitstream/140.119/152402/1/103401.pdf
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4Dissertation/ Thesis
المؤلفون: 文莛橞, Wen, Ting-Hui
المساهمون: 林信助
مصطلحات موضوعية: 目標波動度策略, 波動度指數, 已實現波動度, 投資組合, 多國比較, Volatility Targeting Strategy, Volatility Index, Realized Volatility, Portfolio, Multi-Country Comparisons
وصف الملف: 1243936 bytes; application/pdf
Relation: Bongaerts, D., Kang, X., & van Dijk, M. (2020). Conditional volatility targeting. Financial Analysts Journal, 76(4), 54-71.\n\nDachraoui, K. (2018). On the Optimality of Target Volatility Strategies. Journal of Portfolio Management, 44(5):58–67.\n\nBarber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The journal of Finance, 55(2), 773-806.\n\nCirelli, S., Lozza, S. O., & Moriggia, V. (2017). A conservative discontinuous target volatility strategy. Investment Management & Financial Innovations, 14(2), 176.\n\nGiese, G. (2010). On the risk-return profile of leveraged and inverse ETFs. Journal of Asset Management, 11(4), 219-228.\n\nHarvey, C. R., Hoyle, E., Korgaonkar, R., Rattray, S.,\nSargaison, M., & Van Hemert, O. (2018). The impact of volatility targeting. Available at SSRN 3175538.\n\nHallerbach, W. G. (2012). A proof of the optimality of volatility weighting over time. Available at SSRN 2008176.\n\nHansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of applied econometrics, 20(7), 873-889.\n\nHansen, P. R., Huang, Z., & Shek, H. H. (2012). Realized GARCH: a joint model for returns and realized measures of volatility. Journal of Applied Econometrics, 27(6), 877-906.\n\nHenkel, S. J., Martin, J. S., & Nardari, F. (2011). Time-varying short-horizon predictability. Journal of financial economics, 99(3), 560-580.\n\nKongsilp, W., & Mateus, C. (2017). Volatility risk and stock return predictability on global financial crises. China Finance Review International, 7, 1, 33-66.\n\nKritzman, M., Page, S., & Turkington, D. (2012). Regime shifts: Implications for dynamic strategies (corrected). Financial Analysts Journal, 68(3), 22-39.\n\nLiu, F., Tang, X., & Zhou, G. (2019). Volatility-managed portfolio: Does it really work?. The Journal of Portfolio Management, 46 (1), 38-51.\n\nMoreira, A., & Muir, T. (2017). Volatility‐managed portfolios. The Journal of Finance, 72(4), 1611-1644.\n\nMylnikov, G. (2021). Volatility Targeting: It’s Complicated!. The Journal of Portfolio Management, 47(8), 57-74.\n\nNadarajah, S., Zhang, B., & Chan, S. (2014). Estimation methods for expected shortfall. Quantitative Finance, 14(2), 271-291.\n\nPong, S., Shackleton, M. B., Taylor, S. J., & Xu, X. (2004). Forecasting currency volatility: A comparison of implied volatilities and AR (FI) MA models. Journal of Banking & Finance, 28(10), 2541-2563.\n\nWhaley, R. E. (2009). Understanding the VIX. Journal of Portfolio Management, 35(3), 98-105.\n\nQian, E., Sorensen, E. H., & Hua, R. (2007). Information horizon, portfolio turnover, and optimal alpha models. The Journal of Portfolio Management, 34(1), 27-40.; G0110351031; https://nccur.lib.nccu.edu.tw//handle/140.119/146341; https://nccur.lib.nccu.edu.tw/bitstream/140.119/146341/1/103101.pdf
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5Dissertation/ Thesis
المؤلفون: 呂菱
المساهمون: 林信助
مصطلحات موضوعية: 目標波動度策略, 波動度指數, 下檔風險, Target volatility strategy, Volatility index, Downside risk
وصف الملف: 1137219 bytes; application/pdf
Relation: 1. 周雨田、陳唯帆、殷正華（2011），VIX對崩盤風險之避險功能分析。Journal of Futures and Options Vol.4 No.2。\n2. 黃韋中（2021），利用VIX指數和ARMA-GARCH模型預測波動度之目標波動度策略績效分析。國立政治大學金融學系研究所未出版碩士論文，臺灣臺北。\n3. Auinger, F. (2015). The Causal Relationship between the S&P 500 and the VIX Index: Critical Analysis of Financial Market Volatility and Its Predictability. Springer, 37-41.\n4. Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual investors. The journal of Finance, 55(2), 773-806.\n5. Bongaerts, D., Kang, X., Dijk, M.V. (2020). Conditional Volatility\nTargeting, Financial Analysts Journal, 76:4, 54-71.\n6. Cirelli, S., Vitali, S., Ortobelli Lozza, S., Moriggia, V. (2017). A conservative discontinuous target volatility strategy. Investment Management and Financial Innovations, 14(2-1), 176-190.\n7. Dachraoui, K. (2018). On the Optimality of Target Volatility Strategies. Journal of Portfolio Management 44(5): 58-67.\n8. Harvey, C. R., E. Hoyle, R. Korgaonkar, S. Rattray, M. Sargaison, and O. Van Hemert. (2018). The Impact of Volatility Targeting. The Journal of Portfolio Management August 2021, 47 (8) 57-74.\n9. Liu, F., Tang, X., & Zhou, G. (2019). Volatility-Managed Porfolio: Does It Really Work? The Journal of Portfolio Management.\n10. Markowitz, H. M. (1952). Portfolio Selection. Journal of Finance, 7, 77-91.\n11. Moreira, A., & Muir, T. (2017). Volatility‐Managed Portfolios. The Journal of Finance, 72(4): 1611-1644.\n12. Mylnikov, G. (2021). Volatility Targeting: It`s Complicated! The Journal of Portfolio Management August 2021, 47 (8) 57-74.\n13. Tukey, J. W. (1977). Exploratory data analysis.\n14. Wang, H. (2019). VIX and volatility forecasting: A new insight. Physica A: Statistical Mechanics and its Applications, 533, 121951.; G0110351017; https://nccur.lib.nccu.edu.tw//handle/140.119/146334; https://nccur.lib.nccu.edu.tw/bitstream/140.119/146334/1/101701.pdf
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6Dissertation/ Thesis
المؤلفون: 許茱媛, Hsu, Jhu-Yuan
المساهمون: 黃泓智
مصطلحات موضوعية: 機器學習, ETF, 波動度控制, Machine Learning, Volatility Control
وصف الملف: 1750757 bytes; application/pdf
Relation: 林晏緯（2021）。利用集成學習建構股市最適投資組合。〔未出版之碩士論文〕。淡政治大學風險管理與保險學系。\n錢慧娟（2022）。訊號分解對於集成學習預測股價準確率之影響—以台灣加權股價指數為例。〔未出版之碩士論文〕。淡政治大學風險管理與保險學系。\nChen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).\nChoudhury, S., Ghosh, S., Bhattacharya, A., Fernandes, K. J., & Tiwari, M. K. (2014). A real-time clustering and SVM based price-volatility prediction for optimal trading strategy. Neurocomputing, 131, 419-426. ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2013.10.002\nConnell, P., & Hodgson, M. (2016). Managing investment outcomes with volatility control. Schroder Investment Management North America Inc.\nHochreiter, Sepp & Schmidhuber, Jürgen. (1997). Long Short-term Memory. Neural computation. 9. 1735-80. 10.1162/neco.1997.9.8.1735.\nHuang, Y., Capretz, L. F., & Ho, D. (2021). Machine Learning for Stock Prediction Based on Fundamental Analysis. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01-10). Orlando, FL, USA. https://doi.org/10.1109/SSCI50451.2021.9660134\nJiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184, 115537. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2021.115537.\nJidong, L., & Ran, Z. (2018). Dynamic Weighting Multi Factor Stock Selection Strategy Based on XGBoost Machine Learning Algorithm. In 2018 IEEE International Conference of Safety Produce Informatization (IICSPI) (pp. 868-872). Chongqing, China. doi:10.1109/IICSPI.2018.8690416.\nLi, Y., & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analysis, 13, 139–149. https://doi.org/10.1007/s41060-021-00279-9\nMa, Y., Wang, Y., Wang, W., & Zhang, C. (2023). Portfolios with return and volatility prediction for the energy stock market. Energy, 270, 126958. ISSN 0360-5442. https://doi.org/10.1016/j.energy.2023.126958\nMaqbool, J., Aggarwal, P., Kaur, R., Mittal, A., & Ganaie, I. A. (2023). Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach. Procedia Computer Science, 218, 1067-1078. ISSN 1877-0509. https://doi.org/10.1016/j.procs.2023.01.086.\nMarkowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.\nMehta, S., Rana, P., Singh, S., Sharma, A., & Agarwal, P. (2019). Ensemble Learning Approach for Enhanced Stock Prediction. In 2019 Twelfth International Conference on Contemporary Computing (IC3) (pp. 1-5). Noida, India. doi:10.1109/IC3.2019.8844891.\nPatel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172. https://doi.org/10.1016/j.eswa.2014.10.031\nRumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.\nVapnik, V., & Chervonenkis, A. (1997). Support Vector Regression Machines. In Advances in Neural Information Processing Systems 9 (pp. 281-287).\nYun, K. K., Yoon, S. W.&Won, D. (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 115716. https://doi.org/10.1016/j.eswa.2021.115716.; G0110358011; https://nccur.lib.nccu.edu.tw//handle/140.119/147206; https://nccur.lib.nccu.edu.tw/bitstream/140.119/147206/1/801101.pdf
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7Dissertation/ Thesis
المؤلفون: 林崇仁, Lin, Chung-Jen
المساهمون: 林士貴 羅秉政, Lin, Shih-Kuei Kendro Vincent
مصطلحات موضوعية: 外匯市場, 隨機波動度模型, 最小變異 Delta, 避險, Forex, Stochastic Volatility Model, Minimum Variance Delta, Hedge
وصف الملف: 2237585 bytes; application/pdf
Relation: [1] Ahlip, R. and Rutkowski, M. (2013). Pricing of foreign exchange options under the heston stochastic volatility model and cir interest rates. Quantitative Finance, 13(6):955–966.\n[2] Aït-Sahalia, Y., Li, C., and Li, C. X. (2021). Implied stochastic volatility models. The Review of Financial Studies, 34(1):394–450.\n[3] Alexander, C. and Kaeck, A. (2012). Does model fit matter for hedging? evidence from ftse 100 options. Journal of Futures Markets, 32(7):609–638.\n[4] Alexander, C. and Nogueira, L. M. (2007). Model-free hedge ratios and scale-invariant models. Journal of Banking & Finance, 31(6):1839–1861.\n[5] Alexander, C., Rubinov, A., Kalepky, M., and Leontsinis, S. (2012). Regime-dependent smile- adjusted delta hedging. Journal of Futures Markets, 32(3):203–229.\n[6] Almeida, C., Fan, J., Freire, G., and Tang, F. (2022). Can a machine correct option pricing models? Journal of Business & Economic Statistics, pages 1–14.\n[7] Amin, K. I. and Jarrow, R. A. (1991). Pricing foreign currency options under stochastic interest rates. Journal of International Money and Finance, 10(3):310–329.\n[8] Andersen, L. and Andreasen, J. (2000). Jump-diffusion processes: Volatility smile fitting and numerical methods for option pricing. Review of Derivatives Research, 4:231–262.\n[9] Bakshi, G., Cao, C., and Chen, Z. (1997). Empirical performance of alternative option pricing models. The Journal of Finance, 52(5):2003–2049.\n[10] Bates, D. S. (1996). Jumps and stochastic volatility: Exchange rate processes implicit in deutsche mark options. The Review of Financial Studies, 9(1):69–107.\n[11] Bates, D. S. (2000). Post-’87 crash fears in the s&p 500 futures option market. Journal of Econometrics, 94(1-2):181–238.\n[12] Bates, D. S. (2012). Us stock market crash risk, 1926–2010. Journal of Financial Economics, 105(2):229–259.\n[13] Black, F. (1976). The pricing of commodity contracts. Journal of Financial Economics, 3(1- 2):167–179.\n[14] Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3):637–654.\n[15] Bossens, F., Rayée, G., Skantzos, N. S., and Deelstra, G. (2010). Vanna-volga methods applied to fx derivatives: from theory to market practice. International Journal of Theoretical and Applied Finance, 13(08):1293–1324.\n[16] Branger, N., Krautheim, E., Schlag, C., and Seeger, N. (2012). Hedging under model misspecification: All risk factors are equal, but some are more equal than others. Journal of Futures Markets, 32(5):397–430.\n[17] Carr, P. and Cousot, L. (2011). A pde approach to jump-diffusions. Quantitative Finance, 11(1):33–52.\n[18] Carr, P. and Cousot, L. (2012). Explicit constructions of martingales calibrated to given implied volatility smiles. SIAM Journal on Financial Mathematics, 3(1):182–214.\n[19] Carr, P., Geman, H., Madan 5, D. B., and Yor, M. (2004). From local volatility to local lévy models. Quantitative Finance, 4(5):581–588.\n[20] Cheng, H.-W., Chang, L.-H., Lo, C.-L., and Tsai, J. T. (2023). Empirical performance of component garch models in pricing vix term structure and vix futures. Journal of Empirical Finance, 72:122–142.\n[21] Christie, A. A. (1982). The stochastic behavior of common stock variances: Value, leverage and interest rate effects. Journal of Financial Economics, 10(4):407–432.\n[22] Christoffersen, P. and Jacobs, K. (2004). The importance of the loss function in option valuation. Journal of Financial Economics, 72(2):291–318.\n[23] Christoffersen, P., Jacobs, K., and Mimouni, K. (2010). Volatility dynamics for the s&p500: Evidence from realized volatility, daily returns, and option prices. The Review of Financial Studies, 23(8):3141–3189.\n[24] Christoffersen, P., Jacobs, K., and Ornthanalai, C. (2012). Dynamic jump intensities and risk premiums: Evidence from s&p500 returns and options. Journal of Financial Economics, 106(3):447–472.\n[25] Cont, R. and Da Fonseca, J. (2002). Dynamics of implied volatility surfaces. Quantitative Finance, 2(1):45.\n[26] Cox, J. C., Ingersoll Jr, J. E., and Ross, S. A. (1985). An intertemporal general equilibrium model of asset prices. Econometrica: Journal of the Econometric Society, pages 363–384.\n[27] Duffie, D., Pan, J., and Singleton, K. (2000). Transform analysis and asset pricing for affine jump-diffusions. Econometrica, 68(6):1343–1376.\n[28] Dumas, B., Fleming, J., and Whaley, R. E. (1998). Implied volatility functions: Empirical tests. The Journal of Finance, 53(6):2059–2106.\n[29] Dupire, B. et al. (1994). Pricing with a smile. Risk, 7(1):18–20.\n[30] Garman, M. B. and Kohlhagen, S. W. (1983). Foreign currency option values. Journal of\nInternational Money and Finance, 2(3):231–237.\n[31] Heston, S. L. (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options. The Review of Financial Studies, 6(2):327–343.\n[32] Hull, J. and White, A. (1987). The pricing of options on assets with stochastic volatilities. The Journal of Finance, 42(2):281–300.\n[33] Hull, J. and White, A. (2017). Optimal delta hedging for options. Journal of Banking and Finance, 82:180–190.\n[34] Lee, R. W. (2001). Implied and local volatilities under stochastic volatility. International Journal of Theoretical and Applied Finance, 4(01):45–89.\n[35] Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science, pages 141–183.\n[36] Nandi, S. (1996). Pricing and hedging index options under stochastic volatility: an empirical examination. Technical report, Working paper.\n[37] Ornthanalai, C. (2014). Levy jump risk: Evidence from options and returns. Journal of Financial Economics, 112(1):69–90.; G0110352029; https://nccur.lib.nccu.edu.tw//handle/140.119/146601; https://nccur.lib.nccu.edu.tw/bitstream/140.119/146601/1/202901.pdf
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8Dissertation/ Thesis
المؤلفون: 林秉陞, Lin, Ping-Sheng
المساهمون: 岳夢蘭, Yueh, Meng-Lan
مصطلحات موضوعية: 加密貨幣, 波動度, VIX 指數, 非系統風險, Cryptocurrency, Volatility, VIX index, Idiosyncratic volatility
وصف الملف: 1339959 bytes; application/pdf
Relation: Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1), 31-56.\nAng, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross‐section of volatility and expected returns. The Journal of Finance, 61(1), 259-299.\nBollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.\nBaker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.\nBali, T. G., & Cakici, N. (2008). Idiosyncratic volatility and the cross section of expected returns. Journal of Financial and Quantitative Analysis, 43(1), 29-58.\nBai, J., Bali, T. G., & Wen, Q. (2021). Is there a risk-return tradeoff in the corporate bond market? Time-series and cross-sectional evidence. Journal of Financial Economics, 142(3), 1017-1037.\nCarhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57-82.\nChordia, T., Subrahmanyam, A., & Anshuman, V. R. (2001). Trading activity and expected stock returns. Journal of Financial Economics, 59(1), 3-32.\nCorbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28-34.\nChung, K. H., Wang, J., & Wu, C. (2019). Volatility and the cross-section of corporate bond returns. Journal of Financial Economics, 133(2), 397-417.\nDaniel, K., & Titman, S. (1997). Evidence on the characteristics of cross-sectional variation in stock returns. Journal of Finance, 52(1), 1-33.\nEngle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.\nFama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.\nFu, F. (2009). Idiosyncratic risk and the cross-section of expected stock returns. Journal of Financial Economics, 91(1), 24-37.\nLiu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6), 2689-2727.\nLiu, Y., Tsyvinski, A., & Wu, X. (2022). Common risk factors in cryptocurrency. The Journal of Finance, 77(2), 1133-1177.\nSharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.\nStambaugh, R. F., Yu, J., & Yuan, Y. (2015). Arbitrage asymmetry and the idiosyncratic volatility puzzle. The Journal of Finance, 70(5), 1903-1948.; G0110357023; https://nccur.lib.nccu.edu.tw//handle/140.119/146285; https://nccur.lib.nccu.edu.tw/bitstream/140.119/146285/1/702301.pdf
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9Dissertation/ Thesis
المؤلفون: 沈少飛, Shen, Shao-Fei
المساهمون: 郭維裕, Guo, Wei-Yu
مصطلحات موضوعية: 地緣政治, 台股, 波動度, Geopolitical Risk, Taiwan, Volatility
وصف الملف: 1308453 bytes; application/pdf
Relation: Ahir, H., Bloom, N., & Furceri, D. (2022). The world uncertainty index.\nBaker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636.\nBrandt, M. W., & Gao, L. (2019). Macro fundamentals or geopolitical events? A textual analysis of news events for crude oil. Journal of Empirical Finance, 51, 64-94.\nBrecher, M., & Wilkenfeld, J. (1997). A study of crisis. University of Michigan Press.\nCaldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194-1225.\nChoi, S.-Y. (2022). Evidence from a multiple and partial wavelet analysis on the impact of geopolitical concerns on stock markets in North-East Asian countries. Finance Research Letters, 46, 102465.\nDavis, S. J. (2016). An index of global economic policy uncertainty.\nEngle, R. F., & Campos-Martins, S. (2023). What are the events that shake our world? Measuring and hedging global COVOL. Journal of Financial Economics, 147(1), 221-242.\nGolub, B., Greenberg, D., & Ratcliffe, R. (2018). Market-driven scenarios: An approach for plausible scenario construction. The Journal of Portfolio Management.\nInternational Monetary Fund. (2023). World Economic Outlook: A Rocky Recovery.\nKannadhasan, M., & Das, D. (2020). Do Asian emerging stock markets react to international economic policy uncertainty and geopolitical risk alike? A quantile regression approach. Finance Research Letters, 34, 101276.\nKaragozoglu, A. K., Wang, N., & Zhou, T. (2022). Comparing Geopolitical Risk Measures. The Journal of Portfolio Management, 48(10), 226-257.\nSalisu, A. A., Lasisi, L., & Tchankam, J. P. (2022). Historical geopolitical risk and the behaviour of stock returns in advanced economies. The European Journal of Finance, 28(9), 889-906.\nYimou Lee, D. L., and Ben Blanchard. (2020). China launches "gray-zone" warfare to subdue Taiwan. https://www.reuters.com/investigates/special-report/hongkong-taiwan-military/; G0110351023; https://nccur.lib.nccu.edu.tw//handle/140.119/145784; https://nccur.lib.nccu.edu.tw/bitstream/140.119/145784/1/102301.pdf
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10Dissertation/ Thesis
المؤلفون: 黃僑尉, Huang, Chiao-Wei
المساهمون: 李志宏, Lee, Jie-Haun
مصطلحات موضوعية: 當沖交易, 波動度, 流動性, 市場效率, 順勢交易, Day trading, Volatility, Liquidity, Market efficiency, Positive feedback trading
وصف الملف: 1640607 bytes; application/pdf
Relation: 1. Barber, B. M., & Odean, T. (2001). The internet and the investor. Journal of Economic Perspectives, 15(1), 41-54.\n2. Barber, B. M., Odean, T., & Zhu, N. (2006, September). Do noise traders move markets?. In EFA 2006 Zurich meetings paper.\n3. Barber, B. M., Lee, Y. T., Liu, Y. J., & Odean, T. (2014). The cross-section of speculator skill: Evidence from day trading. Journal of Financial Markets, 18, 1-24.\n4. Black, F. (1986). Noise. The journal of finance, 41(3), 528-543.\n5. Bloomfield, R., O’hara, M., & Saar, G. (2009). How noise trading affects markets: An experimental analysis. The Review of Financial Studies, 22(6), 2275-2302.\n6. Brockman, P., & Chung, D. Y. (1999). Bid‐ask spread components in an order‐driven environment. Journal of Financial Research, 22(2), 227-246.\n7. Chague, F., De-Losso, R., & Giovannetti, B. (2020). Day trading for a living?. Available at SSRN 3423101.\n8. Chung, J. M., Choe, H., & Kho, B. C. (2009). The impact of day‐trading on volatility and liquidity. Asia‐Pacific Journal of Financial Studies, 38(2), 237-275.\n9. De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Positive feedback investment strategies and destabilizing rational speculation. the Journal of Finance, 45(2), 379-395.\n10. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.\n11. Harris, J. H., & Schultz, P. H. (1998). The trading profits of SOES bandits. Journal of Financial Economics, 50(1), 39-62.\n12. Jegadeesh, N., & Titman, S. (1995). Short-horizon return reversals and the bid-ask spread. Journal of Financial Intermediation, 4(2), 116-132.\n13. Jordan, D. J., & Diltz, J. D. (2003). The profitability of day traders. Financial Analysts Journal, 59(6), 85-94.\n14. Kang, J., Kim, I. J., Lee, W. G., & Moon, H. (2005). Do Day-traders Destabilize the Market?: The Case of the KOSPI200 Futures Market. In 한국증권학회 KSA (Korean Secutiyies Association) 학술발표회 (pp. 1-34). 한국증권학회.\n15. Koski, J. L., Rice, E. M., & Tarhouni, A. (2004). Noise trading and volatility: Evidence from day trading and message boards. Available at SSRN 533943.\n16. Kumar, A., & Lee, C. M. (2006). Retail investor sentiment and return comovements. The Journal of Finance, 61(5), 2451-2486.\n17. Kyröläinen, P. (2008). Day trading and stock price volatility. Journal of Economics and Finance, 32(1), 75-89.\n18. Lin, J. C., Sanger, G. C., & Booth, G. G. (1995). Trade size and components of the bid-ask spread. The Review of Financial Studies, 8(4), 1153-1183.\n19. Linnainmaa, J. T. (2003). The anatomy of day traders. Available at SSRN 472182.\n20. Ryu, D. (2012). The profitability of day trading: An empirical study using high-quality data. Investment Analysts Journal, 41(75), 43-54.\n21. Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The review of financial studies, 1(1), 41-66.\n22. Song, C. S. (2003). Day Trading and Price Volatility: Observation of the Korea Stock Exchange. Asia-Pacific Journal of Financial Studies, 32(3), 45-84.\n23. Verma, R. K., & Verma, S. K. (2006). Phytochemical and termiticidal study of Lantana camara var. aculeata leaves. Fitoterapia, 77(6), 466-468.\n24. Wan, D., & Yang, X. (2017). High‐frequency positive feedback trading and market quality: evidence from China`s stock market. International Review of Finance, 17(4), 493-523.\n25. Yang, T. Y., Huang, S. Y., Tsai, W. C., & Weng, P. S. (2020). The impacts of day trading activity on market quality: evidence from the policy change on the Taiwan stock market. Journal of Derivatives and Quantitative Studies: 선물연구.; G0109357022; https://nccur.lib.nccu.edu.tw//handle/140.119/143912; https://nccur.lib.nccu.edu.tw/bitstream/140.119/143912/1/702201.pdf
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11Academic Journal
المؤلفون: 袁淑芳, 陳姿穎, Shu-Fang Yuan, Zih-Ying Chen
المساهمون: 南華大學企業管理學系, 南華大學企業管理學系管理科學碩士班
مصطلحات موضوعية: 現貨, 期貨, 選擇權, 價格發現, 隱含波動度, Stock, Future, Option, Price Volatility, Implied Volatility
وصف الملف: 835041 bytes; application/pdf
Relation: 管理科學研究; 12卷1期; http://nhuir.nhu.edu.tw/handle/987654321/27039; http://nhuir.nhu.edu.tw/bitstream/987654321/27039/-1/index.html; http://nhuir.nhu.edu.tw/bitstream/987654321/27039/1/3092120103.pdf
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12Report
المؤلفون: 袁淑芳
المساهمون: 南華大學企業管理學系
مصطلحات موضوعية: 量與波動度關係, 市場情緒, 異質預期, 報酬偏態, 金融危機, the volume-volatility relationship, market sentiment, heterogeneous opinion, skewness, financial crisis
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13Dissertation/ Thesis
المؤلفون: 范姜峻浩, Fan Jiang, Jun-Hao
المساهمون: 岳夢蘭, Yueh, Meng-Lan
مصطلحات موضوعية: 真實波動度, HAR-RV模型, 波動度預測, 機器學習, 決策樹, 隨機森林, Realized Volatility, HAR-RV Model, Volatility Forecasting, Machine Learning, Decision Tree, Random Forest
وصف الملف: 4835989 bytes; application/pdf
Relation: [1] Andersen, T. G., & Bollerslev, T. (1998). Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts. International Economic Review, 39(4), 885–905.\n[2] Andersen, T.G., Bollerslev, T., Diebold, F.X. & Labys, P. (2003). Modeling and Forecasting Realized Volatility. Econometrica, 71, 579-625.\n[3] Andersen , T.G. & Teräsvirta, T. (2009). Realized Volatility. In: Mikosch, T., Kreiß, JP., Davis, R., Andersen, T. (eds) Handbook of Financial Time Series. Springer, Berlin, Heidelberg.\n[4] Andersen, T.G., Bollerslev, T. & Diebold, F.X. (2007). Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility. The Review of Economics and Statistics, 89 (4), 701–720.\n[5] Barndorff-Nielsen, O.E., Shephard, N. (2004). Power and Bipower Variation with Stochastic Volatility and Jumps, Journal of Financial Econometrics, Volume 2, Issue 1 , 1–37.\n[6] Barndorff-Nielsen, O.E., Kinnebrock, S. & Shephard, N. (2008). Measuring Downside Risk - Realised Semivariance. CREATES Research Paper, No. 2008-42\n[7] Breiman, L. (2001). Random Forests. Machine Learning 45, 5–32.\n[8] Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification And Regression Trees (1st ed.). Routledge.\n[9] Browne, M.W. (2000), Cross-Validation Methods, Journal of Mathematical Psychology, Volume 44, Issue 1, 108-132.\n[10] Bouri, E., Gkillas, K., Gupta, R., & Pierdzioch, C. (2021). Forecasting realized volatility of bitcoin: The role of the trade war. Computational Economics, 57(1), 29-53.\n[11] Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.\n[12] Christensen, K., Siggaard, M., & Veliyev, B. (2021). A machine learning approach to volatility forecasting. Available at SSRN.\n[13] Christoffersen, P. F., & Diebold, F. X. (2006). Financial asset returns, direction-of-change forecasting, and volatility dynamics. Management Science, 52(8), 1273-1287.\n[14] Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196.\n[15] Corsi, F., & Renò, R. (2012). Discrete-time volatility forecasting with persistent leverage effect and the link with continuous-time volatility modeling. Journal of Business & Economic Statistics, 30(3), 368-380.\n[16] Degiannakis, S., & Filis, G. (2017). Forecasting oil price realized volatility using information channels from other asset classes. Journal of International Money and Finance, 76, 28-49.\n[17] De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38-48.\n[18] Engle R.F., Patton A.J. (2007), 2 - What good is a volatility model?*, Editor(s): John Knight, Stephen Satchell, In Quantitative Finance, Forecasting Volatility in the Financial Markets (Third Edition), Butterworth-Heinemann, 47-63.\n[19] Franses, P.H. & Van Dijk, D. (1996). Forecasting stock market volatility using (non-linear) Garch models. J. Forecast., 15: 229-235.\n[20] Huang, J., & Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on knowledge and Data Engineering, 17(3), 299-310.\n[21] Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.\n[22] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.\n[23] Liu, L. Y., Patton, A. J., & Sheppard, K. (2015). Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes. Journal of Econometrics, 187(1), 293-311.\n[24] Liu, L., & Zhang, T. (2015). Economic policy uncertainty and stock market volatility. Finance Research Letters, 15, 99-105.\n[25] Luong, C., & Dokuchaev, N. (2018). Forecasting of realised volatility with the random forests algorithm. Journal of Risk and Financial Management, 11(4), 61.\n[26] McAleer, M., & Medeiros, M. C. (2008). Realized volatility: A review. Econometric reviews, 27(1-3), 10-45.\n[27] Myles, A.J., Feudale, R.N., Liu, Y., Woody, N.A. and Brown, S.D. (2004). An introduction to decision tree modeling. J. Chemometrics, 18: 275-285.\n[28] Patton , A.J. & Sheppard K. (2015). Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility. The Review of Economics and Statistics; 97 (3), 683–697.\n[29] Peng, Y., Albuquerque, P. H. M., de Sá, J. M. C., Padula, A. J. A., & Montenegro, M. R. (2018). The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Systems with Applications, 97, 177-192.\n[30] Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery.\n[31] Trevor Hastie, Jerome Friedman, Robert Tibshirani (2001). The Elements of Statistical Learning: data mining, inference, and prediction. Springer New York, NY\n[32] Wen, F., Gong, X., & Cai, S. (2016). Forecasting the volatility of crude oil futures using HAR-type models with structural breaks. Energy Economics, 59, 400-413.\n[33] Zhou, Y., Li, T., Shi, J., & Qian, Z. (2019). A CEEMDAN and XGBOOST-based approach to forecast crude oil prices. Complexity, 201; G0109357020; https://nccur.lib.nccu.edu.tw//handle/140.119/141022; https://nccur.lib.nccu.edu.tw/bitstream/140.119/141022/1/702001.pdf
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14Academic Journal
المؤلفون: 陳威光, 郭維裕, 黃暐能, 王朝生, Chen,Wei-Kuang, Kuo,Wei-Yu, Huang,Wei-Neng, Wang,Chao-Sheng
المساهمون: 金融系
مصطلحات موضوعية: 波動度選擇權, 隱含波動度, 笑狀波幅, VIX options, Implied volatilities, Volatility smile
وصف الملف: 2022916 bytes; application/pdf
Relation: 風險管理學報,15(1),57-80; https://nccur.lib.nccu.edu.tw//handle/140.119/70572; https://nccur.lib.nccu.edu.tw/bitstream/140.119/70572/1/57-80.pdf
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15Dissertation/ Thesis
المؤلفون: 曾柏鈞, Tseng, Po-Chun
المساهمون: 江彌修, Chiang, Mi-Hsiu
مصطلحات موضوعية: 加密貨幣, 階層式風險評價, 設定目標波動度投資組合, GARCH-LSTM, cryptocurrency, Hierarchical Risk Parity, VolTarget Portfolio
وصف الملف: 6778102 bytes; application/pdf
Relation: Albeverio, S., Steblovskaya, V., & Wallbaum, K. (2013). Investment instruments with volatility target mechanism. Quantitative Finance, 13(10), 1519-1528.\nAlbeverio, S., Steblovskaya, V., & Wallbaum, K. (2018). The volatility target effect in structured investment products with capital protection. Review of Derivatives Research, 21(2), 201-229.\nBildirici, M., & Ersin, Ö. Ö. (2013). Forecasting oil prices: Smooth transition and neural network augmented GARCH family models. Journal of Petroleum Science and Engineering, 109, 230-240.\nBlack, F., & Jones, R. (1987). Simplifying portfolio insurance. Journal of Portfolio Management, 14(1), 48.\nBollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.\nBollerslev, T. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 542-547.\nBurggraf, T. (2021). Beyond risk parity–A machine learning-based hierarchical risk parity approach on cryptocurrencies. Finance Research Letters, 38, 101523.\nDe Prado, M. L. (2016). Building diversified portfolios that outperform out of sample. The Journal of Portfolio Management, 42(4), 59-69.\nDi Persio, L., Garbelli, M., & Wallbaum, K. (2021). Forward-looking volatility estimation for risk-managed investment strategies during the covid-19 crisis. Risks, 9(2), 33.\nEngle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 987-1007.\nGlosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779-1801.\nGoel, V. K., Lazar, A. J., Warneke, C. L., Redston, M. S., & Haluska, F. G. (2006). Examination of mutations in BRAF, NRAS, and PTEN in primary cutaneous melanoma. Journal of Investigative Dermatology, 126(1), 154-160.\nHochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.\nHu, Y., Ni, J., & Wen, L. (2020). A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction. Physica A: Statistical Mechanics and its Applications, 557, 124907.\nKim, H. Y., & Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103, 25-37.\nKristjanpoller, W., Fadic, A., & Minutolo, M. C. (2014). Volatility forecast using hybrid neural network models. Expert Systems with Applications, 41(5), 2437-2442.\nLongin, F., & Solnik, B. (1995). Is the correlation in international equity returns constant: 1960–1990? Journal of International Money and Finance, 14(1), 3-26.\nQiu, J., Wang, B., & Zhou, C. (2020). Forecasting stock prices with long-short term memory neural network based on attention mechanism. PloS one, 15(1), e0227222.\nRabemananjara, R., & Zakoian, J.-M. (1993). Threshold ARCH models and asymmetries in volatility. Journal of applied econometrics, 8(1), 31-49.\nTseng, C.-H., Cheng, S.-T., Wang, Y.-H., & Peng, J.-T. (2008). Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices. Physica A: Statistical Mechanics and its Applications, 387(13), 3192-3200.; G0109352020; https://nccur.lib.nccu.edu.tw//handle/140.119/141567; https://nccur.lib.nccu.edu.tw/bitstream/140.119/141567/1/202001.pdf
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16Dissertation/ Thesis
المؤلفون: 任魯姆, Lumta Ramadani, Ramadani, Lumta
المساهمون: 蔡政憲, Tsai, Jason
مصطلحات موضوعية: 比特幣, 加密貨幣, 以太坊, GARCH模型, 波動度外溢, Bitcoin, Cryptocurrencies, Ethereum, GARCH models, Volatility Spillovers
وصف الملف: 888505 bytes; application/pdf
Relation: Alexander, C., Choi, J., Massie, H. and Sohn, S., 2020. Price Discovery and Microstructure in Ether Spot and Derivative Markets. SSRN Electronic Journal,.Auer, R. and Claessens, S., 2018. Regulating cryptocurrencies: assessing market reactions. BIS Quarterly Review September.\r\nBaur, D., Hong, K. and Lee, A., 2018. Bitcoin: Medium of exchange or speculative assets?. Journal of International Financial Markets, Institutions and Money, 54, pp.177-189.\r\nBegu, L.S., Spătaru, S. and Marin, E., 2012. Investigating the Evolution of RON/EUR Exchange Rate: The Choice of Appropriate Model. Journal of Social and Economic Statistics, 1(2), pp.23-39.\r\nBrandvold, M., Molnár, P., Vagstad, K. and Andreas Valstad, O., 2015. Price discovery on Bitcoin exchanges. Journal of International Financial Markets, Institutions and Money, 36, pp.18-35.\r\nBouoiyour, J. and Selmi, R., 2015. What does Bitcoin look like?. Annals of Economics & Finance, 16(2).\r\nBorri, N. and Shakhnov, K., 2020. Regulation spillovers across cryptocurrency markets. Finance Research Letters, 36, p.101333.\r\nBouri, E., Molnár, P., Azzi, G., Roubaud, D. and Hagfors, L., 2017. On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?. Finance Research Letters, 20, pp.192-198.\r\nCasals, J., Jerez, M. and Sotoca, S., 2000. Exact smoothing for stationary and non-stationary time series. International Journal of Forecasting, 16(1), pp.59-69.\r\nChokor, A. and Alfieri, E., 2021. Long and short-term impacts of regulation in the cryptocurrency market. The Quarterly Review of Economics and Finance, 81, pp.157-173.\r\nCiaian, P., Rajcaniova, M. and Kancs, d., 2016. The economics of BitCoin price formation. Applied Economics, 48(19), pp.1799-1815.\r\nCheah, E. and Fry, J., 2015. Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters, 130, pp.32-36.\r\nCorbet, S., Lucey, B., Urquhart, A. and Yarovaya, L., 2019. Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62, pp.182-199.\r\nCorbet, S., Meegan, A., Larkin, C., Lucey, B. and Yarovaya, L., 2018. Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, pp.28-34.\r\nCorbet, S., Larkin, C., Lucey, B., 2020. The contagion effects of the covid-19 pandemic: Evidence from gold and cryptocurrencies. Finance Research Letters 101554.\r\nDyhrberg, A., 2016. Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Research Letters, 16, pp.85-92.\r\nEngle, R. and Kroner, K., 1995. Multivariate Simultaneous Generalised ARCH. Econometric Theory, 11(1), pp.122-150.\r\nFama, E.F., 1970. Efficient market hypothesis: A review of theory and empirical work. Journal of Finance, 25(2), pp.28-30.\r\nFerretti, S. and D`Angelo, G., 2019. On the Ethereum blockchain structure: A complex networks theory perspective. Concurrency and Computation: Practice and Experience, 32(12).\r\nFrisby, D., 2014. Bitcoin: the future of money?. Unbound Publishing.\r\nGeorgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D. and Giaglis, G., 2015. Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices. SSRN Electronic Journal.\r\nGeng, J., Chen, F., Ji, Q. and Liu, B., 2021. Network connectedness between natural gas markets, uncertainty and stock markets. Energy Economics, 95, p.105001.\r\nHuang, 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, p.102016.\r\nKatsiampa, P., Corbet, S. and Lucey, B., 2019. Volatility spillover effects in leading cryptocurrencies: A BEKK-MGARCH analysis. Finance Research Letters, 29, pp.68-74.\r\nKatsiampa, P., 2017. Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, pp.3-6.\r\nKatsiampa, P., 2019. Volatility co-movement between Bitcoin and Ether. Finance Research Letters, 30, pp.221-227.\r\nKhuntia, S. and Pattanayak, J., 2018. Adaptive market hypothesis and evolving predictability of bitcoin. Economics Letters, 167, pp.26-28.\r\nKristoufek, L., 2013. BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3(1).\r\nKroll, J.A., Davey, I.C. and Felten, E.W., 2013, June. The economics of Bitcoin mining, or Bitcoin in the presence of adversaries. In Proceedings of WEIS (Vol. 2013, No. 11).\r\nKondo, M., Oliva, G., Jiang, Z., Hassan, A. and Mizuno, O., 2020. Code cloning in smart contracts: a case study on verified contracts from the Ethereum blockchain platform. Empirical Software Engineering, 25(6), pp.4617-4675.\r\nLi, X. and Wang, C., 2017. The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin. Decision Support Systems, 95, pp.49-60.\r\nNakamoto, S., 2008. Bitcoin: A Peer-to-Peer Electronic Cash System. SSRN Electronic Journal, [online] Available at: .\r\nÖzdemir, O., 2022. Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis. Financial Innovation, 8(1).\r\nPagnottoni, P. and Dimpfl, T., 2019. Price discovery on Bitcoin markets. Digital Finance, 1(1-4), pp.139-161.\r\nPanagiotidis, T., Stengos, T. and Vravosinos, O., 2018. On the determinants of bitcoin returns: A LASSO approach. Finance Research Letters, 27, pp.235-240.\r\nM., Poongodi., Sharma, A., V., V., Bhardwaj, V., Sharma, A., Iqbal, R. and Kumar, R., 2020. Prediction of the price of Ethereum blockchain cryptocurrency in an industrial finance system. Computers & Electrical Engineering, 81, p.106527.\r\nSjölander, P., 2010. A stationary unbiased finite sample ARCH-LM test procedure. Applied Economics, 43(8), pp.1019-1033.\r\nTiwari, A., Kumar, S. and Pathak, R., 2019. Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models. Applied Economics, 51(37), pp.4073-4082.\r\nUrquhart, A., 2016. The inefficiency of Bitcoin. Economics Letters, 148, pp.80-82.\r\nUrquhart, A. and Zhang, H., 2019. Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. International Review of Financial Analysis, 63, pp.49-57.\r\nWeber, B., 2016. Bitcoin and the legitimacy crisis of money. Cambridge Journal of Economics, 40(1), pp.17-41.; G0109933049; https://nccur.lib.nccu.edu.tw//handle/140.119/141134; https://nccur.lib.nccu.edu.tw/bitstream/140.119/141134/1/304901.pdf
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17Dissertation/ Thesis
المؤلفون: 盛寶陞, Sheng, Bao-Sheng
المساهمون: 林靖庭, Lin, Ching-Ting
مصطلحات موضوعية: 樂透性質, 極端正報酬, 極端報酬, MAX效應, 市場風險, 異質波動度, MAX, MAX effect, Lottery feature, BETA anomaly, Idiosyncratic volatility, Extreme positive return
وصف الملف: 1212075 bytes; application/pdf
Relation: Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets,Volume 5, Issue 1, pp. 31-56.\nAng,A., Hodrick,RJ., Xing,Y., Zhang,X. (2006). The cross‐section of volatility and expected returns. The Journal of Finance, pp. 259-299.\nAng,A., Hodrick,RJ., Xing,Y., Zhang,X. (2009). High idiosyncratic volatility and low returns: International and further US evidence. Journal of Financial Economic, pp. 1-23.\nBali,TG.,Brown,SJ.,Murray,S.,Tang,Y. (2017). A lottery-demand-based explanation of the beta anomaly. Journal of Financial and Quantitative Analysis, Volume 52 , Issue 6, pp. 2369-2397.\nBali,TG.,Cakici,N.,Whitelaw,RF. (2011). Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of Financial Economics, pp. 427-446.\nBanz, R. (1981). The relationship between return and market value of common stocks. Journal of financial economics 9, pp. 3-18.\nChan,KC., Chen,NF. (1991). Structural and return characteristics of small and large firms. The Journal of Finance,Volume46, Issue4, pp. 1467-1484.\nCheon, Y., Lee, K. (2014). Maxing out globally: MAX-premium, uncertainty avoidance,and the cross-section of expected stock returns. Seoul National University Business School.\nFama,EF., French,KR. (1998). Value versus growth: The international evidence. The Journal of Finance,Volume53, Issue6, pp. 1975-1999.\nFama,EF., French,KR. (1992). The cross‐section of expected stock returns. the Journal of Finance,Volume47, Issue2, pp. 427-465.\nFrazzini,A.,Pedersen,LH. (2014). Betting against beta. Journal of Financial Economics 111, pp. 1-25.\nJensen,MC., Black,F., Scholes,MS. (1972). The capital asset pricing model: Some empirical tests. Praeger Publishers Inc.\nWalkshäusl, C. (2014). The MAX effect: European evidence. Journal of Banking & Finance 42, pp. 1-10.\nZhong,A., Gray,P. (2016). The MAX effect: An exploration of risk and mispricing explanations. Journal of Banking & Finance, pp. 76-90.; G0109352018; https://nccur.lib.nccu.edu.tw//handle/140.119/140603; https://nccur.lib.nccu.edu.tw/bitstream/140.119/140603/1/201801.pdf
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18
المؤلفون: 趙珮伶, Chao, Pei-Ling
المساهمون: 蔡蒔銓, 賴慧文, Tsai, Shih-Chuan, Lai, Whuei-Wen
مصطلحات موضوعية: 委託積極度, 委託簿失衡, 波動度, 偏態, Tobit模型, Order aggressiveness, Order imbalance, Volatility, Skewness, Tobit model
Relation: 60855004O-39653; https://etds.lib.ntnu.edu.tw/thesis/detail/8d1d29fc3e4f4c8c32a524b753522451/; http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/118348
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19Academic Journal
المؤلفون: 陳正暉, 廖四郎, Liao, Szu-Lang
المساهمون: 金融系
مصطلحات موضوعية: Optimal portfolio choice, stochastic volatility, time-changed Lévy processes, leverage effect, volatility feedback effect, asymmetric volatility, 最適投資組合, 隨機波動度, 時間轉換Lévy過程, 槓桿效果, 波動度回饋效果, 波動度不對稱, eco
Relation: http://nccur.lib.nccu.edu.tw/bitstream/140.119/120954/1/135-166.pdf; http://nccur.lib.nccu.edu.tw//handle/140.119/120954
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20Dissertation/ Thesis
المؤلفون: 鄭雅鐘, Cheng, Ya-Chung
المساهمون: 張榮顯 教授, 管理學院:財務金融學系
مصطلحات موضوعية: 隱含波動度的偏差, 隱含波動度的價差, 實際波動度, Implied Volatility Spread, Implied Volatility Skew, Realistic Volatility
وصف الملف: 130 bytes; text/html
Relation: http://ir.ncnu.edu.tw:8080/handle/310010000/15583; http://ir.ncnu.edu.tw:8080/bitstream/310010000/15583/1/index.html