加密貨幣價格跳躍和共同跳躍－方法比較與實證

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張乃文(Nai-Wen Chang) 查詢紙本館藏

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財務金融學系

論文名稱

加密貨幣價格跳躍和共同跳躍－方法比較與實證

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摘要(中)

金融市場上常因反應重大訊息使得價格突然大漲或大跌，並產生跳躍或不連續的情形，價格跳躍對於投資人資產配置和風險管理顯然造成很大的影響，為此，研究與識別金融資產的價格跳躍非常重要。本文透過研究2020年到2022年共738個交易日17種名氣較大的加密貨幣高頻分鐘資料，分別參考BN-S (Barndorff-Nielsen 和 Shephard (2004)) 方法與 Yeh and Yun (2014) 迴歸測試 (Regression-based Tests) 方法，應用在觀察各類型加密貨幣對於貨幣跳躍和共同跳躍的檢測效用。此外，也使用迴歸測試模型的係數來檢測跳躍的整體存在，以及透過迴歸殘差識別跳躍天數的確切日期。實證結果顯示在不同信賴水準下，不論是跳躍檢測還是共跳檢測，迴歸測試的結果都比BN-S跳躍測試的結果更穩健，也沒有過度警報檢測的問題。另外，迴歸測試模型的整體跳躍檢定非常顯著，表明加密貨幣在樣本期間內確實存在跳躍；而跳躍日期檢測則在經過比對後發現與歷史事件吻合，能成功的將跳躍日期鑑別出來。

摘要(英)

In the financial market, prices often rise or fall suddenly in response to major news, resulting in jumps or discontinuities. Price jumps obviously have a great impact on investors′ asset allocation and risk management. Therefore, it is very important to study and identify jumps in financial assets. In this paper, by studying the high-frequency one-minute data of 17 kinds of well-known cryptocurrencies in 738 days from 2020 to 2022, we use the BN-S (Barndorff-Nielsen and Shephard (2004)) method and Regression-based tests (Yeh and Yun (2014)), respectively. Both methods are applied to examine the detection performance of jumps and co-jumps for various cryptocurrencies. In addition, the coefficients of the regression test model were used to detect the overall presence of jumps, as well as to identify the exact date of jump days through the regression residuals. Empirical results show that under different confidence levels, for jump or co-jump detection, the regression test results are more robust than the BN-S jump test results. Moreover, the regression test is free from the over alarming problem. After the comparison, it is found that the jump date detection is consistent with the historical events, and the jump dates can be successfully identified.

關鍵字(中)

★ 加密貨幣
★ 比特幣
★ BN-S
★ 迴歸測試
★ 跳躍
★ 共同跳躍

關鍵字(英)

★ cryptocurrency
★ bitcoin
★ BN-S
★ regression-based tests
★ jump
★ cojump

論文目次

摘要······································································i
Abstract···································································ii
致謝·····································································iii
第一章緒論·······························································1
第二章文獻回顧···························································3
第三章研究方法···························································5
3.1 研究模型···························································5
3.2 資料樣本···························································5
3.3 模型介紹···························································6
3.3.1 BN-S模型·······················································6
3.3.2 Regression-based Tests 迴歸跳躍測試模型························7
3.3.3 Regression-based Tests for Jump Days···························8
3.3.4 Regression-based Cojump Tests··································9
第四章研究結果··························································12
4.1基本資料··························································12
4.2 Regression-based jump and BN-S jump································13
4.3 Cojump····························································21
第五章結論······························································25
參考文獻·································································26
附錄·····································································28

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指導教授

葉錦徽(Jin‑Huei Yeh)

審核日期

2022-9-16

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