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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88386完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪茂蔚 | zh_TW |
| dc.contributor.advisor | Mao-Wei Hung | en |
| dc.contributor.author | 陳麒文 | zh_TW |
| dc.contributor.author | CHI-WEN CHEN | en |
| dc.date.accessioned | 2023-08-09T16:49:55Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-09 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-26 | - |
| dc.identifier.citation | [1] E. Bouri, S. J. H. Shahzad, D. Roubaud, L. Kristoufek, and B. Lucey. Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis. The Quarterly Review of Economics and Finance, 77:156–164, 2020.
[2] G. W. Buetow and B. J. Henderson. The vix futures basis: Determinants and implications. Journal of Portfolio Management, 42(2):119, 2016. [3] G. Chalamandaris and A. E. Tsekrekos. Predictable dynamics in implied volatility surfaces from otc currency options. Journal of Banking & Finance, 34(6):1175– 1188, 2010. [4] S. Corbet, B. Lucey, A. Urquhart, and L. Yarovaya. Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis, 62:182– 199, 2019. [5] D. S. Damianov and A. H. Elsayed. Does bitcoin add value to global industry portfolios? Economics Letters, 191:108935, 2020. [6] F. Diz, T. J. Finucane, et al. Do the options markets really overreact? Journal of Futures Markets, 13:299–299, 1993. [7] A. H. Dyhrberg, S. Foley, and J. Svec. How investible is bitcoin? analyzing the liquidity and transaction costs of bitcoin markets. Economics Letters, 171:140–143, 2018. [8] A. P. Fassas and C. Siriopoulos. The efficiency of the vix futures market: A panel data approach. Journal of Alternative Investments, 14(3):55, 2012. [9] R. Heynen, A. Kemna, and T. Vorst. Analysis of the term structure of implied volatilities. Journal of Financial and Quantitative Analysis, 29(1):31–56, 1994. [10] Y. Huang, K. Duan, and T. Mishra. Is bitcoin really more than a diversifier? a pre-and post-covid-19 analysis. Finance Research Letters, 43:102016, 2021. [11] A. Kajtazi and A. Moro. The role of bitcoin in well diversified portfolios: A comparative global study. International Review of Financial Analysis, 61:143–157, 2019. [12] T. Klein, H. P. Thu, and T. Walther. Bitcoin is not the new gold–a comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis, 59:105–116, 2018. [13] E. Krylova, J. Nikkinen, and S. Vähämaa. Cross-dynamics of volatility term structures implied by foreign exchange options. 2005. [14] J. R. H. Ornelas and R. B. Mauad. Implied volatility term structure and exchange rate predictability. International Journal of Forecasting, 35(4):1800–1813, 2019. [15] J. M. Poterba and L. H. Summers. The persistence of volatility and stock market fluctuations. Technical report, National Bureau of Economic Research, 1984. [16] J. Stein. Overreactions in the options market. The Journal of Finance, 44(4):1011– 1023, 1989. [17] S.-Y. Wen. Term structure analysis of option implied volatility in the foreign exchange markets. 2021. [18] X. Xu and S. J. Taylor. Conditional volatility and the informational efficiency of the phlx currency options market. Journal of Banking & Finance, 19(5):803–821, 1995. [19] D. Yermack. Is bitcoin a real currency? an economic appraisal. In Handbook of digital currency, pages 31–43. Elsevier, 2015. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88386 | - |
| dc.description.abstract | 在本篇研究中,我們對新興且發展迅速的加密貨幣期權市場進行了深入探討。我們關注的重點在於選擇權市場的情緒如何影響期貨的未來持有價格回報。我們利用了比特幣(BTC)和以太坊(ETH)的高頻期權數據,進行隱含波動度曲面的解構,並構建了微笑曲線與期限結構,以便更深入地理解這種關係。
我們的實證結果顯示,從微笑曲線中分解出的期限結構變量的隱含收益與日內未來收益相符,並達到了統計顯著性。這表明期限結構變量所隱含的市場情緒預期有效地預測了未來的持有價格回報。此外,我們發現,期限結構變量與未來收益的關係在日內呈現增強動能,且在日內與日級別間進行交替時,會依變量屬性出現反轉效應和動能效應。這兩種效應揭示了短期和長期投資者間的市場情緒差異,以及未來回報差異的現象。 我們的研究結果強調了,隱含波動率曲面變量不僅可以預測未來回報的方向,也可以估計回報的規模。透過長期和短期變量,我們可以實施反轉或動能策略。總結來說,這項研究對於深化我們對於加密貨幣期權市場中市場情緒與未來持有價格回報關係的理解具有重大意義。 | zh_TW |
| dc.description.abstract | In this scholarly investigation, we conduct an in-depth exploration of the nascent yet rapidly advancing realm of cryptocurrency options markets. Our central inquiry pertains to the role of market sentiment within the options landscape and its influence on the future carry price returns of futures. Utilizing high-frequency options data from Bitcoin (BTC) and Ethereum (ETH), we deconstruct the implied volatility surface, further formulating a smile curve and term structure to enhance our understanding of this intricate relationship.
Our empirical evidence indicates that the implied returns from term structure variables, derived from the smile curve, align seamlessly with intraday future returns, culminating in statistical significance. This underscores the capacity of term structure variables to effectively prognosticate future carry price returns through implied market sentiment expectations. Additionally, we discern an intraday escalation in momentum within the relationship between term structure variables and future returns. During intraday alternations with daily-level shifts, depending on variable attributes, reversal and momentum effects become apparent. These effects illuminate the disparity in market sentiment between short-term and long-term investors, as well as the variance in future returns. Our research findings emphasize the utility of implied volatility surface variables not only in predicting the direction of future returns, but also in estimating their magnitude. Through the implementation of either reversal or momentum strategies, these variables, whether long-term or short-term, demonstrate their applicability. In essence, this study significantly enhances our understanding of the relationship between market sentiment and future carry price returns within the cryptocurrency options market, rendering it of paramount importance. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-09T16:49:55Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-09T16:49:55Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements ii 摘要 iii Abstract iv Contents vi List of Figures viii List of Tables ix Denotation x Chapter 1 緒論 1 1.1 研究動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究目的與論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . 4 Chapter 2 文獻回顧 6 2.1 選擇權隱含波動度 . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 隱含波動度議題 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 3 研究方法 13 3.1 樣本與樣本來源 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 研究期間 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.2 資料來源 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 研究變數 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.1 應變數 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.2 解釋變數 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.3 隱含波動度之微笑曲線估計 . . . . . . . . . . . . . . . . . . . . 16 3.2.4 隱含波動度之期限結構 . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.4.1 期限結構迴歸式 . . . . . . . . . . . . . . . . . . . . 18 3.2.4.2 期限結構 PCA . . . . . . . . . . . . . . . . . . . . . 25 3.3 實證模型 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 基於期限結構迴歸式之模型 (3.2.4.1) . . . . . . . . . . . . . . . . 32 3.3.2 基於期限結構 PCA 之模型 (3.2.4.2) . . . . . . . . . . . . . . . . 32 Chapter 4 實證研究 34 4.1 敘述性統計 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2 相關係數矩陣 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2.1 迴歸法相關係數矩陣 . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2.2 PCA 法相關係數矩陣 . . . . . . . . . . . . . . . . . . . . . . . . 38 4.3 實證結果分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.1 迴歸法實證結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.2 PCA 法實證結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Chapter 5 結論與建議 50 5.1 研究結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2 研究建議 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 References 53 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 虛擬貨幣 | zh_TW |
| dc.subject | 高頻資料 | zh_TW |
| dc.subject | 日內報酬 | zh_TW |
| dc.subject | 波動度期限結構 | zh_TW |
| dc.subject | 波動度微笑曲線 | zh_TW |
| dc.subject | 波動度曲面拆解 | zh_TW |
| dc.subject | 隱含波動度 | zh_TW |
| dc.subject | 以太幣 | zh_TW |
| dc.subject | 比特幣 | zh_TW |
| dc.subject | Cryptocurrency | en |
| dc.subject | High-frequency data | en |
| dc.subject | Intraday return | en |
| dc.subject | Volatility Smile | en |
| dc.subject | Volatility Term structure | en |
| dc.subject | Volatility surface decomposition | en |
| dc.subject | Implied Volatilities | en |
| dc.subject | ETH | en |
| dc.subject | BTC | en |
| dc.title | 日內隱含波動度資訊-以虛擬貨幣為例 | zh_TW |
| dc.title | The intraday information of Implied Volatility - Evidence on crypto currency | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蔡豐澤;蔡佳芬 | zh_TW |
| dc.contributor.oralexamcommittee | Feng-Tse Tsai;Chia-Fen Tsai | en |
| dc.subject.keyword | 虛擬貨幣,比特幣,以太幣,隱含波動度,波動度曲面拆解,波動度微笑曲線,波動度期限結構,日內報酬,高頻資料, | zh_TW |
| dc.subject.keyword | Cryptocurrency,BTC,ETH,Implied Volatilities,Volatility surface decomposition,Volatility Smile,Volatility Term structure,Intraday return,High-frequency data, | en |
| dc.relation.page | 55 | - |
| dc.identifier.doi | 10.6342/NTU202300915 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-07-27 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 國際企業學系 | - |
| 顯示於系所單位: | 國際企業學系 | |
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