TY - JOUR
T1 - Extreme Connectedness Across Chinese Stock and Commodity Futures Markets
AU - Mensi, Walid
AU - Ahmadian-Yazdi, Farzaneh
AU - Al-Kharusi, Sami
AU - Roudari, Soheil
AU - Kang, Sang Hoon
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/6/1
Y1 - 2024/6/1
N2 - This study examines the price spillovers and connectedness among stock markets, the Shanghai Stock Exchange Composite (SSEC) index, Hang Seng index, Shenzhen Stock Exchange (SZSE) index, commodity futures markets, Aluminum (AL), Gold (AU), Copper (CU), Zinc, Steel Rebar, and Natural Rubber in bearish and bullish market situations. We use the quantile connectedness approach of Ando et al. (2022) to calculate hedge coverage ratios, optimal portfolio weights, and hedge coverage effectiveness. Our empirical analysis yields several important results. First, there is no difference in the results of the TVP-VAR-DY and TVP-VAR models in relation to the magnitude of return transmission during the sample period. Second, CU is the main transmitter and AU is the main receiver of shocks from the network. Third, under the QVAR method estimations, the CU (AU) is the major (minor) contributors to the network during the normal, bearish, and bullish market statuses. Fourth, the results of the hedge ratio strategy confirm that CU and AU are respectively the most and the least expensive assets for a long-term investment in the Chinese stock market in different market conditions.
AB - This study examines the price spillovers and connectedness among stock markets, the Shanghai Stock Exchange Composite (SSEC) index, Hang Seng index, Shenzhen Stock Exchange (SZSE) index, commodity futures markets, Aluminum (AL), Gold (AU), Copper (CU), Zinc, Steel Rebar, and Natural Rubber in bearish and bullish market situations. We use the quantile connectedness approach of Ando et al. (2022) to calculate hedge coverage ratios, optimal portfolio weights, and hedge coverage effectiveness. Our empirical analysis yields several important results. First, there is no difference in the results of the TVP-VAR-DY and TVP-VAR models in relation to the magnitude of return transmission during the sample period. Second, CU is the main transmitter and AU is the main receiver of shocks from the network. Third, under the QVAR method estimations, the CU (AU) is the major (minor) contributors to the network during the normal, bearish, and bullish market statuses. Fourth, the results of the hedge ratio strategy confirm that CU and AU are respectively the most and the least expensive assets for a long-term investment in the Chinese stock market in different market conditions.
KW - Chinese stock market
KW - Commodity
KW - hedging cost
KW - quantile
KW - spillover
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UR - https://www.mendeley.com/catalogue/db817544-2c3d-3b26-b085-0ffc7c36c0c3/
U2 - 10.1016/j.ribaf.2024.102299
DO - 10.1016/j.ribaf.2024.102299
M3 - Article
AN - SCOPUS:85188682954
SN - 0275-5319
VL - 70
JO - Research in International Business and Finance
JF - Research in International Business and Finance
M1 - 102299
ER -