Interindustry volatility spillover effects in China’s stock market

2020 ◽  
Vol 539 ◽  
pp. 122936 ◽  
Author(s):  
Kedong Yin ◽  
Zhe Liu ◽  
Xue Jin
Keyword(s):  
Stock Market ◽  
Spillover Effects ◽  
Volatility Spillover

A Study on Information Spillover Effects from NASDAQ 100 to KOSDAQ 50 Index Futures Markets

2003 ◽  
Vol 11 (1) ◽  
pp. 145-167
Author(s):  
Gyu Hyeon Mun ◽  
Jeong Hyo Hong
Keyword(s):  
Stock Market ◽  
Real Time ◽  
Futures Markets ◽  
Spillover Effects ◽  
Time Varying ◽  
Volatility Spillover ◽  
Index Futures ◽  
Conditional Mean ◽  
Time Basis ◽  
Information Spillover

This paper studies the information spillover effects over price and volatility across countries by using open-to-close (daytime) returns and close-to-open (overnight) returns of NASDAQ 100 and KOSDAQ 50 index futures data from January 1, 2001 to December 31, 2001. Based on the time-varying AR(1)-GARCH (1,1)-M models, we document that statistically significant conditional mean and volatility spillover effects from the daytime returns of NASDAQ 100 index futures to both overnight returns and daytime returns of KOSDAQ 50 index futures were observed. We also find that there were information spillover effects from overnight returns of NASDAQ 100 index futures to daytime returns of KOSDAQ 50 index futures returns because investors in Korean stock markets can get information on U.S. stock market movement on real time basis due to the ECN transaction with its trading hour overlapped. Finally, we find that the daytime returns of KOSDAQ 50 index futures significantly influence the overnight and daytime returns of the NASDAQ 100 index futures.

scholarly journals Volatility Spillover Effects In Asean-5 Stock Market: Does The Different Oil Price Era Change The Pattern?

2019 ◽  
Vol 3 (2) ◽  
pp. 116-134
Author(s):  
Dwika Darinda ◽  
Fikri C Permana
Keyword(s):  
Stock Market ◽  
Spillover Effects ◽  
The Philippines ◽  
Oil Price ◽  
Volatility Spillover ◽  
Jones Index ◽  
Volatility Transmission ◽  
Independent Variables ◽  
Daily Data ◽  
Asymmetric Garch

The aim of this study is to identify the pattern of volatility transmission in ASEAN-5 (Indonesia, Malaysia, Thailand, Singapore and the Philippines) stock market by examine Global Macro Shocks (proxyed by Brent oil price); Cross-Market Linkages (proxied by Dow Jones Index); and Economic Fundamental (proxied by exchange rate) as the sources of volatility. This paper utilizing VAR and asymmetric GARCH (1,1)-BEKK  model using the daily data between 4 January 2012 and 30 June 2017. The result shows that all independent variables have a significant volatility transmission to every ASEAN-5 stock market. Then in order to capture the different volatility transmission pattern, we divided the data into two periods which are “high-oil price” era and “low-oil price” era. Besides the different rate of volatility, we also find a different pattern of volatility transmission at Malaysia stock market (KLCI); Thailand stock market (SETI); and at Philippines stock market (PSEI) between these two eras.

scholarly journals Sector Volatility Spillover and Economic Policy Uncertainty: Evidence from China’s Stock Market

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1411
Author(s):  
Xiaqing Su ◽  
Zhe Liu
Keyword(s):  
Economic Policy ◽  
Stock Market ◽  
Asset Allocation ◽  
Policy Uncertainty ◽  
Volatility Spillover ◽  
Economic Policy Uncertainty ◽  
Long Run ◽  
Transmission Structure ◽  
Long Term Impact

Following generalized variance decomposition, we identify the transmission structure of financial shock among ten sectors in China. Then, we examine whether economic policy uncertainty (EPU) affects it through GARCH-MIDAS regression. We find that consumer discretionary, industrials, and materials sectors are systemically important industries during the sample period. Further research of dynamic analysis shows that each sector acts in a time-varying role in this structure. The results of the GARCH-MIDAS regression indicate that none of the selected EPU indexes has a significant long-term impact on the total volatility spillover of the inter-sector stock market in China. However, the EPUs do affect some sectors’ spillover indexes in the long run, and they are significantly heterogeneous. This paper can provide regulatory suggestions for policymakers and reasonable asset allocation and risk avoidance methods for investors.

Linkages between cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles

2016 ◽  
Vol 9 (2) ◽  
pp. 123-146 ◽  
Author(s):  
Kim Hiang Liow
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Business Cycles ◽  
Spillover Effects ◽  
Real Estate Market ◽  
Stock Market Index ◽  
Content Type ◽  
Market Index ◽  
Cross Country ◽  
The Cross

Purpose This research aims to investigate whether and to what extent the co-movements of cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles are linked across G7 from February 1990 to June 2014. Design/methodology/approach The empirical approaches include correlation analysis on Hodrick–Prescott (HP) cycles, HP cycle return spillovers effects using Diebold and Yilmaz’s (2012) spillover index methodology, as well as Croux et al.’s (2001) dynamic correlation and cohesion methodology. Findings There are fairly strong cycle-return spillover effects between the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles. The interactions among the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles in G7 are less positively pronounced or exhibit counter-cyclical behavior at the traditional business cycle (medium-term) frequency band when “pure” stock market cycles are considered. Research limitations/implications The research is subject to the usual limitations concerning empirical research. Practical implications This study finds that real estate is an important factor in influencing the degree and behavior of the relationship between cross-country business cycles and cross-country stock market cycles in G7. It provides important empirical insights for portfolio investors to understand and forecast the differential benefits and pitfalls of portfolio diversification in the long-, medium- and short-cycle horizons, as well as for research studying the linkages between the real economy and financial sectors. Originality/value In adding to the existing body of knowledge concerning economic globalization and financial market interdependence, this study evaluates the linkages between business cycles, stock market cycles and public real estate market cycles cross G7 and adds to the academic real estate literature. Because public real estate market is a subset of stock market, our approach is to use an original stock market index, as well as a “pure” stock market index (with the influence of real estate market removed) to offer additional empirical insights from two key complementary perspectives.

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