Understanding the Liquidity Connectedness in Global Stock Markets
碩士 === 國立中央大學 === 經濟學系 === 104 === Based on the generalized vector autoregressive framework in which forecast error variance decompositions are invariant to variable ordering, we provide both static (full-sample) and dynamic (rolling-sample) analyses for the liquidity connectedness in 12 developed a...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | en_US |
Published: |
2016
|
Online Access: | http://ndltd.ncl.edu.tw/handle/42992826989027448999 |
id |
ndltd-TW-104NCU05389006 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-104NCU053890062017-06-10T04:46:48Z http://ndltd.ncl.edu.tw/handle/42992826989027448999 Understanding the Liquidity Connectedness in Global Stock Markets 全球股市流動性之關聯性分析 Chun-Che Chou 周群哲 碩士 國立中央大學 經濟學系 104 Based on the generalized vector autoregressive framework in which forecast error variance decompositions are invariant to variable ordering, we provide both static (full-sample) and dynamic (rolling-sample) analyses for the liquidity connectedness in 12 developed and 8 emerging stock markets during 2001-2015 period. The liquidity data we use in this paper is constructed from daily price index and turnover and it is calculated by the revised Amihud illiquidity ratio. Our empirical results indicate that the liquidity connectedness seems to be more intensive when economic downturns happen. What’s more, stock markets in developed countries generate higher spillovers and connectedness than stock markets in emerging countries. We also prove that the geographical features and economic developments of a country are crucial factors deciding its level of connectedness to others. It is noteworthy that some empirical outcomes in this paper is consistent with some equity connectedness studies at a certain extent. Chih-Chiang Hsu 徐之強 2016 學位論文 ; thesis 65 en_US |
collection |
NDLTD |
language |
en_US |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立中央大學 === 經濟學系 === 104 === Based on the generalized vector autoregressive framework in which forecast error variance decompositions are invariant to variable ordering, we provide both static (full-sample) and dynamic (rolling-sample) analyses for the liquidity connectedness in 12 developed and 8 emerging stock markets during 2001-2015 period. The liquidity data we use in this paper is constructed from daily price index and turnover and it is calculated by the revised Amihud illiquidity ratio. Our empirical results indicate that the liquidity connectedness seems to be more intensive when economic downturns happen. What’s more, stock markets in developed countries generate higher spillovers and connectedness than stock markets in emerging countries. We also prove that the geographical features and economic developments of a country are crucial factors deciding its level of connectedness to others. It is noteworthy
that some empirical outcomes in this paper is consistent with some equity
connectedness studies at a certain extent.
|
author2 |
Chih-Chiang Hsu |
author_facet |
Chih-Chiang Hsu Chun-Che Chou 周群哲 |
author |
Chun-Che Chou 周群哲 |
spellingShingle |
Chun-Che Chou 周群哲 Understanding the Liquidity Connectedness in Global Stock Markets |
author_sort |
Chun-Che Chou |
title |
Understanding the Liquidity Connectedness in Global Stock Markets |
title_short |
Understanding the Liquidity Connectedness in Global Stock Markets |
title_full |
Understanding the Liquidity Connectedness in Global Stock Markets |
title_fullStr |
Understanding the Liquidity Connectedness in Global Stock Markets |
title_full_unstemmed |
Understanding the Liquidity Connectedness in Global Stock Markets |
title_sort |
understanding the liquidity connectedness in global stock markets |
publishDate |
2016 |
url |
http://ndltd.ncl.edu.tw/handle/42992826989027448999 |
work_keys_str_mv |
AT chunchechou understandingtheliquidityconnectednessinglobalstockmarkets AT zhōuqúnzhé understandingtheliquidityconnectednessinglobalstockmarkets AT chunchechou quánqiúgǔshìliúdòngxìngzhīguānliánxìngfēnxī AT zhōuqúnzhé quánqiúgǔshìliúdòngxìngzhīguānliánxìngfēnxī |
_version_ |
1718457148577415168 |