||The dynamic relationship between sovereign bonds and credit default swaps markets under high and low volatility states.
||Graduate Institute of Finance
本篇研究係利用馬可夫轉換向量誤差修正模型探討在高波動度及低波動度狀態下，主權債券與其信用違約交換之間的動態關係．本篇研究挑選四個位於成熟市場中的國家(美國、英國、德國及法國)作為主要研究樣本，另外亦挑選三個位於新興市場的國家(中國、墨西哥及南非)並將其結果摘錄於本研究中．本篇研究的實證結果整理如下: (一) 美國、德國及南非市場以外的其他市場，在低波動度狀態時，信用違約交換市場具有價格發現的功能；反之，在高波動度狀態時，價格發現功能將移轉至債券市場．此外，本篇研究發現，在美國市場中，不論是在高波動度還是低波動度狀態下，債券市場具有價格發現功能;而德國以及南非市場則顯示信用違約交換市場在整個樣本期間內皆具有價格發現的功能。(二)在所有市場中，低波動度狀態占所有樣本之比例皆大於高波動度狀態占所有樣本之比例，此結果顯示低波動度狀態較高波動度狀態更具有持續性；此外，在高波動度狀態下，債券利差及信用違約交換溢價之間的誤差，其絕對值大小明顯大於在低波動度狀態下的誤差絕對值．
This investigation is intended to examine the dynamic relationship between sovereign bonds and CDS markets under high and low volatility states using the Markov-switching vector error correction model (MS-VECM). Four countries in mature markets, including the U.S., the U.K., Germany and France, are selected as the main research sample in this study, and three countries in emerging markets, including China, Mexico and South Africa, are also selected, and their empirical results are also reported in this study. The empirical results of this study are shown as follows: First, the CDS market is shown to lead the bond market in the price discovery process during the low volatility state; conversely, the bond market plays a leadership role in the price discovery process under the high volatility condition except for U.S., Germany and South Africa. Specifically, for U.S. market, bond market plays a leadership role in price discovery during the whole sample period while for Germany and South Africa, CDS market has price discovery mechanism. Second, all of the countries in this study exhibit observation percentage for the low volatility state exceeding the percentage of a high volatility state and this result are consistent with the notion that the low volatility state is more persistent than the high volatility state; in addition, the means of the deviations between bond spread and CDS premiums during the high volatility state are much greater than those during the low volatility state.
LIST OF TABLES V
LIST OF FIGURES VI
2.1. Studies on the relationship between CDS premiums and bond spread....4
2.2. Measure of price leadership to the bond and CDS markets....5
2.3. Development of research questions....6
3. DATA DESCRIPTION AND MODEL SPECIFICATIONS....9
3.1. Data description....9
3.2. Model Specifications....10
3.2.1. Conventional VECM: The model with constant parameters....10
3.2.2. MS-VECM: The model with state-varying parameters....12
4. EMPIRICAL RESULTS AND INTERPRETATION....19
4.1. Parameter estimates of the conventional VECM model....19
4.2. Parameter estimates of the MS-VECM model....20
─Asymmetric adjustment process across various volatility regimes....20
4.3. The empirical analysis of emerging market....21
4.4. Comparative analysis between high and low volatility states....23
LIST OF TABLES
Table Ⅰ. Available data for all countries....31
Table Ⅱ. Unit root tests and cointegration tests of CDS premium and bond spread....32
Table Ⅲ. Parameter estimates of the VECM model for the system with constant term....33
Table Ⅳ. Parameters estimates of MS-VECM model for the system with state-varying....34
Table Ⅴ. Unit root tests and cointegration tests of CDS premium and bond spread (For emerging markets)....36
Table Ⅵ. Parameter estimates of the VECM model for the system with constant term (For emerging markets)....37
Table Ⅶ. Parameters estimates of MS-VECM model for the system with state-varying variances.(For emerging markets)....38
Table Ⅷ. Comparative analysis across various volatility states....40
LIST OF FIGURES
Figure 1. The logarithmic bond spread and CDS premium, the deviation between bond spread and CDS premium, the logarithmic first difference of bond spread and CDS premium, as well as the filtering probability of a high volatility state: the case of the U.S. market....42
Figure 2.The logarithmic bond spread and CDS premium, the deviation between bond spread and CDS premium, the logarithmic first difference of bond spread and CDS premium, as well as the filtering probability of a high volatility state: the case of the Chinese market....43
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