本校學位論文庫
CITYU Theses & Dissertations
論文詳情
林立超
張偉光
金融學院
金融學博士學位課程(中文學制)
博士
2021
雲經濟指數：資產定價與風險屬性研究
CCEI: Research on Asset Pricing and Risk Attributes
雲經濟 ; 經濟價值 ; 雲經濟指數 ; 股票市場
Cloud Economy ; Economic Value ; Cloud Economy Index ; Stock Market
移動互聯網與雲計算的有機結合正緊鑼密鼓地改變著社會經濟增長動能與消費市場走向。人們熟識的大多數經濟活動都將無從遁形於這場席捲全球的數字技術革命。雲經濟的崛起已成為推動社會發展的必然趨勢，事實上它們已經近在眼前。為了對雲經濟有一個更為深刻的認知，本文簡明扼要地討論了雲經濟的應用場景及其在過去的相關概念，還為雲經濟量身定制了一個定義：即“以技術研究或商業互動行為作為關鍵性生產要素，以移動終端或交互式數字化設備作為信息傳遞的主要媒介而開展的一系列非接觸式經濟活動。基於股票市場視角，結合這個新定義，創建了中國雲經濟市場指數(CCEI)及其子指標，考察了雲經濟是否以及如何影響中國經濟。利用2012年至2020年中國A股市場數據，分析了新指數的基本特徵、有效性及其與整個股市的關係，還對新指標進行了穩健性檢驗。證據表明：CCEI與股市之間的關係隨著時間的推移越來越密切，但在2019年1月11日之後這種關係似乎有所減弱，因為最近的雲相關政策擴大了CCEI與股市之間的市場表現差距。隨即從資本市場視角出發，借助FF-3、FF-4、FF-5模型重點討論了傳統辦法是否仍適用於新構造的雲指數以及雲經濟市場。經驗證據表明，其對雲指數的解釋效力十分可觀。緊接著考慮到與雲相關的事件曾令新指數的走勢與社會經濟的發展路徑產生了一些分歧，為了深究CCEI是否具有其它的內含價值，從風險對沖和避險工具的角度對新指數的實際效用進行了深入挖掘。研究發現：CCEI確是一項合適的金融市場避險工具。在將其與傳統避險資產進行避險能力測試後發現：雲經濟指數的避險能力雖稍遜於比特幣，但大幅優於黃金、原油、中美十年期國債等傳統避險資產。最後分別從投資者和政府角度對未來我國雲經濟市場的投資形勢和佈局思路提出了一些可供參考的發展建議。
The organic combination of mobile Internet and cloud computing is vigorously changing the economic growth momentum and consumer market trend of the whole society. Most familiar economic activities will be exposed in this global technological revolution. With the rapid rise of cloud economy, it has become an inevitable trend to promote social development. In fact, they are close at hand. In order to have a deeper understanding of cloud economy, I briefly discuss the application scenarios of cloud economy and its related concepts in the past, and customize a definition: The cloud economy is a series of contactless economic activities with technological research or commercial interaction as the key factor for production, and mobile terminals or interactive digital devices as the major medium of information dissemination. Based on the perspective of the stock market and combined with this new definition, I construct China's cloud economy market index (CCEI) and its sub indicators to examine the relationship between the cloud economy and the economy in China. Using the stock market data from 2012 to 2020, I analyze the basic characteristics and effectiveness of these indexes and their relationship with the whole stock market, which tests the robustness of the new indicators. The evidence shows that the relationship between CCEI and the stock market has become closer and closer over time, but this relationship seems to have weakened after January 11, 2019, as the recent cloud related policies have widened the market performance gap between CCEI and the stock market. From the perspective of capital market, based on the FF-3、FF-4、FF-5 Model, the emphasis is laid on whether the traditional approach is still applicable to the CCEI and the market of cloud economy. The empirical evidence table shows that the FF-3、FF-4、FF-5 Model is very effective in explaining the cloud index. Considering the huge divergence between the new index and the real economy, and investigating whether CCEI has other embedded value, I explore the actual utility of the new index from the perspective of risk hedging and hedging tools. I find that CCEI is indeed a better hedging tool in the financial market. Even after testing its hedging ability with traditional hedging assets, I find that the hedging ability of cloud economy index is only Second to bitcoin, but significantly better than traditional hedging assets such as gold, crude oil, China 10-years Treasury Bonds and U.S. 10-years Treasury Bonds. And last I put forward some suggestions and layout of China's cloud economy market in the future from the perspective of investors and the government.
2022
中文
152
致 謝 I
摘 要 III
Abstract V
目 錄 VII
圖目錄 X
表目錄 XI
第一章 緒 論 1
1.1 研究背景 1
1.2 研究意義 2
1.3 研究問題 4
1.4 本文的創新點 5
1.5 全文結構安排 5
第二章 文獻綜述 8
2.1 雲經濟的研究價值 8
2.1.1 雲計算與雲經濟 8
2.1.2 雲的挑戰與機遇 10
2.1.3 雲經濟價值研究 12
2.1.4 文獻評述與小結 13
2.2 金融市場定價研究 14
2.2.1 市場有效性假說 14
2.2.2 理性投資人行為 15
2.2.3 資產定價發展史 16
2.2.4 文獻評述與小結 18
2.3 傳統避險資產研究 19
2.3.1 黃金的避險屬性研究 19
2.3.2 債券的避險屬性研究 21
2.3.3 原油的避險屬性研究 24
2.3.4 比特幣避險屬性研究 26
2.3.5 文獻評述與小結 28
2.4 本章小結 29
第三章 研究方法及設計 30
3.1 指數設計方法 30
3.2 資產定價模型 30
3.2.1 投資組合排序法 32
3.2.2 時間序列與橫截面可預測性 36
3.2.3 固定效應存在的GPS測試 38
3.2.4 投資組合排序有效性測試 39
3.3 動態評價模型 40
3.3.1 VAR模型 41
3.3.2 ARCH模型 43
3.3.3 GARCH模型 44
3.3.4 DCC-GARCH模型 45
第四章 雲經濟，與社會經濟關聯性 48
4.1 雲經濟中國市場指數構造 48
4.1.1 雲經濟指數的由來 48
4.1.2 數據來源與選取標準 51
4.1.3 雲指數的架構方式 52
4.2 雲經濟指數一級指標 53
4.2.1 指數行業分類標準 53
4.2.2 新指數結構效度測試 54
4.2.3 細分指數相關性分析 55
4.2.4 細分指數市場表現分析 56
4.3 雲經濟中國指數市場表現 57
4.3.1 平穩性檢驗 60
4.3.2 間斷點檢驗 60
4.3.3 回歸結果與討論 62
4.4 穩健性檢驗 64
4.5 本章小結 67
第五章 雲經濟指數定價優化可行性分析 68
5.1 數據處理與變量選取 69
5.1.1 數據來源與處理 69
5.1.2 變量選取與估計 70
5.2 實證結果討論 72
5.2.1 單一投資組合績效估計 72
5.2.2 單一組合GPS穩健性測試 72
5.2.3 多元投資組合績效估計 75
5.2.4 多元組合GPS穩健性測試 76
5.2.5 頂部和底部五分位投資組合績效 77
5.2.6 頂部和底部差額GPS穩健性測試 78
5.2.7 三因子模型實證結果 79
5.2.8 描述統計與相關分析 80
5.2.9 三因子線性回歸模型 81
5.3 進一步研究 85
5.3.1 隨機效應假設檢驗 85
5.3.2 模型檢驗：FF-4和FF-5 87
5.4 本章小結 94
第六章 雲經濟指數避險屬性研究 96
6.1 金融資產收益率研究 97
6.2 數據來源與分析 98
6.2.1 數據來源 98
6.2.2 取樣數據基本分析 99
6.3 實證結果分析與討論 101
6.3.1 平穩性檢驗 101
6.3.2 ARCH-LM檢驗 101
6.3.3 格蘭傑因果關係檢驗 102
6.3.4 VAR模型構建 103
6.3.5 DCC-GARCH模型構建 105
6.4 進一步研究 109
6.4.1 不同資產動態相關性描述 109
6.4.2 金融資產的評價體系構建 112
6.5 本章小結 114
第七章 結論與建議 115
7.1 研究結論 115
7.2 雲經濟市場發展建議 117
7.3 研究限制 119
7.4 後續研究 120
參考文獻 123
附錄1 2020年雲相關重要事件一覽表 139
附錄2 投資組合模型的三個重要推論 140
命題1推論 140
(1) 基於單一投資組合 140
(2) 基於GPS模型的係數估計與標準誤 140
(3) 基於投資組合排序法的係數估計與標準誤 141
(4) 命題1推論過程 143
命題2推論 144
(1) 基於投資組合比較 144
(2) 命題2推論過程 144
命題3推論 147
(1) 兩組之間的表現差異 147
(2) 命題3推論過程 148
作者簡歷 152
摘 要 III
Abstract V
目 錄 VII
圖目錄 X
表目錄 XI
第一章 緒 論 1
1.1 研究背景 1
1.2 研究意義 2
1.3 研究問題 4
1.4 本文的創新點 5
1.5 全文結構安排 5
第二章 文獻綜述 8
2.1 雲經濟的研究價值 8
2.1.1 雲計算與雲經濟 8
2.1.2 雲的挑戰與機遇 10
2.1.3 雲經濟價值研究 12
2.1.4 文獻評述與小結 13
2.2 金融市場定價研究 14
2.2.1 市場有效性假說 14
2.2.2 理性投資人行為 15
2.2.3 資產定價發展史 16
2.2.4 文獻評述與小結 18
2.3 傳統避險資產研究 19
2.3.1 黃金的避險屬性研究 19
2.3.2 債券的避險屬性研究 21
2.3.3 原油的避險屬性研究 24
2.3.4 比特幣避險屬性研究 26
2.3.5 文獻評述與小結 28
2.4 本章小結 29
第三章 研究方法及設計 30
3.1 指數設計方法 30
3.2 資產定價模型 30
3.2.1 投資組合排序法 32
3.2.2 時間序列與橫截面可預測性 36
3.2.3 固定效應存在的GPS測試 38
3.2.4 投資組合排序有效性測試 39
3.3 動態評價模型 40
3.3.1 VAR模型 41
3.3.2 ARCH模型 43
3.3.3 GARCH模型 44
3.3.4 DCC-GARCH模型 45
第四章 雲經濟，與社會經濟關聯性 48
4.1 雲經濟中國市場指數構造 48
4.1.1 雲經濟指數的由來 48
4.1.2 數據來源與選取標準 51
4.1.3 雲指數的架構方式 52
4.2 雲經濟指數一級指標 53
4.2.1 指數行業分類標準 53
4.2.2 新指數結構效度測試 54
4.2.3 細分指數相關性分析 55
4.2.4 細分指數市場表現分析 56
4.3 雲經濟中國指數市場表現 57
4.3.1 平穩性檢驗 60
4.3.2 間斷點檢驗 60
4.3.3 回歸結果與討論 62
4.4 穩健性檢驗 64
4.5 本章小結 67
第五章 雲經濟指數定價優化可行性分析 68
5.1 數據處理與變量選取 69
5.1.1 數據來源與處理 69
5.1.2 變量選取與估計 70
5.2 實證結果討論 72
5.2.1 單一投資組合績效估計 72
5.2.2 單一組合GPS穩健性測試 72
5.2.3 多元投資組合績效估計 75
5.2.4 多元組合GPS穩健性測試 76
5.2.5 頂部和底部五分位投資組合績效 77
5.2.6 頂部和底部差額GPS穩健性測試 78
5.2.7 三因子模型實證結果 79
5.2.8 描述統計與相關分析 80
5.2.9 三因子線性回歸模型 81
5.3 進一步研究 85
5.3.1 隨機效應假設檢驗 85
5.3.2 模型檢驗：FF-4和FF-5 87
5.4 本章小結 94
第六章 雲經濟指數避險屬性研究 96
6.1 金融資產收益率研究 97
6.2 數據來源與分析 98
6.2.1 數據來源 98
6.2.2 取樣數據基本分析 99
6.3 實證結果分析與討論 101
6.3.1 平穩性檢驗 101
6.3.2 ARCH-LM檢驗 101
6.3.3 格蘭傑因果關係檢驗 102
6.3.4 VAR模型構建 103
6.3.5 DCC-GARCH模型構建 105
6.4 進一步研究 109
6.4.1 不同資產動態相關性描述 109
6.4.2 金融資產的評價體系構建 112
6.5 本章小結 114
第七章 結論與建議 115
7.1 研究結論 115
7.2 雲經濟市場發展建議 117
7.3 研究限制 119
7.4 後續研究 120
參考文獻 123
附錄1 2020年雲相關重要事件一覽表 139
附錄2 投資組合模型的三個重要推論 140
命題1推論 140
(1) 基於單一投資組合 140
(2) 基於GPS模型的係數估計與標準誤 140
(3) 基於投資組合排序法的係數估計與標準誤 141
(4) 命題1推論過程 143
命題2推論 144
(1) 基於投資組合比較 144
(2) 命題2推論過程 144
命題3推論 147
(1) 兩組之間的表現差異 147
(2) 命題3推論過程 148
作者簡歷 152
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