The network connectedness of volatility spillovers across global futures markets

2019 ◽  
Vol 526 ◽  
pp. 120756 ◽  
Author(s):  
Sang Hoon Kang ◽  
Jang Woo Lee
Keyword(s):  
Futures Markets ◽  
Volatility Spillovers ◽  
Network Connectedness ◽  
Global Futures

The Impact of Structural Breaks on Volatility Linkages between Asian Stock and Oil Futures Markets

2016 ◽  
Vol 24 (1) ◽  
pp. 31-64
Author(s):  
Sang Hoon Kang ◽  
Seong-Min Yoon
Keyword(s):  
Futures Markets ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Garch Model ◽  
Time Varying ◽  
Oil Futures ◽  
Volatility Spillovers ◽  
Icss Algorithm ◽  
The Global Financial Crisis ◽  
The Impact

This paper investigates the impact of structural breaks on volatility spillovers between Asian stock markets (China, Hong Kong, India, Indonesia, Japan, Korea, Singapore, and Taiwan) and the oil futures market. To this end, we apply the bivariate DCC-GARCH model to weekly spot indices during the period 1998-2015. The results reveal significant volatility transmission for the pairs between the Asian stock and oil futures markets. Moreover, we find a significant variability in the time-varying conditional correlations between the considered markets during both bullish and bearish markets, particularly from early 2007 to the summer of 2008. Using the modified ICSS algorithm, we find several sudden changes in these markets with a common break date centred on September 15, 2008. This date corresponds to the collapse of Lehman Brothers which is considered as our breakpoint to define the global financial crisis. Also, we analyse the optimal portfolio weights and time-varying hedge ratios based on the estimates of the multivariate DCC-GARCH model. The results emphasize the importance of overweighting optimal portfolios between Asian stock and the oil futures markets.

Does Index Futures Dominate Index Spot? Evidence from Taiwan Market

2002 ◽  
Vol 05 (02) ◽  
pp. 255-275 ◽  
Author(s):  
Ching-Chung Lin ◽  
Shen-Yuan Chen ◽  
Dar-Yeh Hwang ◽  
Chien-Fu Lin
Keyword(s):  
Futures Markets ◽  
Price Discovery ◽  
Stock Exchange ◽  
Impulse Response Function ◽  
Spot Market ◽  
Stock Index ◽  
Index Futures ◽  
Volatility Spillovers ◽  
Egarch Model ◽  
Intraday Data

By utilizing vector error correction model (VECM) and EGARCH model, this article uses 5-minute intraday data to examine the interaction of return and volatility between Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the newly introduced TAIEX futures. VECM model shows that there exists bi-directional Granger causality between index spot and index futures markets, but spot market plays a more important role in price discovery. The results of impulse response function and information share indicate that most of the price discovery happens in index spot market. The evidence of EGRACH shows that the impacts of spot and futures innovations are asymmetrical, and the volatility spillovers between spot and futures markets are bi-directional. However, the information flow from spot to futures is stronger. These results suggest that the TAIEX spot market dominates the TAIEX futures market in terms of return and volatility.

Price Discoveries and Volatility Spillovers in S&P CNX Nifty Future and its Underlying Index CNX Nifty

2009 ◽  
Vol 34 (2) ◽  
pp. 41-56 ◽  
Author(s):  
Madhusudan Karmakar
Keyword(s):  
Futures Markets ◽  
Price Discovery ◽  
Spot Market ◽  
The Other ◽  
Future Market ◽  
Volatility Spillovers ◽  
Long Run ◽  
Cash Market ◽  
Cointegration Tests ◽  
First Moment

In a perfectly functioning world, every piece of information should be reflected simultaneously in the underlying spot market and its futures markets. However, in reality, information can be disseminated in one market first and then transmitted to other markets due to market imperfections. And, if one market reacts faster to information than the other, a lead-lag relation is observed The lead-lag relationship in returns and volatilities between spot and futures markets is of interest to academics, practitioners, and regulators. In India, there are very few studies which have investigated the lead-lag relationship in the first moment of the spot and futures markets This study investigates the lead-lag relationship in the first moment as well as the second moment between the S&P CNX Nifty and the Nifty future. It also investigates how much of the volatility in one market can be explained by volatility innovations in the other market and how fast these movements transfer between these markets. It conducts Multivariate Cointegration tests on the long-run relation between these two markets. It investigates the daily price discovery process by exploring the common stochastic trend between the S&P CNX Nifty and the Nifty future based on vector error correction model (VECM). It examines the volatility spillover mechanism with a bivariate BEKK model. Finally, this study captures the effects of recent policy changes in the Indian stock market. The results reveal the following: The VECM results show that the Nifty futures dominate the cash market in price discovery. The bivariate BEKK model shows that although the persistent volatility spills over from one market to another market bi-directionally, past innovations originating in future market have the unidirectional significant effect on the present volatility of the spot market. The findings of the study thus suggest that the Nifty future is more informationally efficient than the underlying spot market. These findings may provide insights on the information transaction and index arbitrage between the CNX Nifty and futures markets.

scholarly journals An Econometric Analysis of ETF and ETF Futures in Financial and Energy Markets Using Generated Regressors

2017 ◽  
Author(s):  
Chia-Lin Chang ◽  
Michael McAleer ◽  
Chien-Hsun Wang
Keyword(s):  
Futures Markets ◽  
Spillover Effects ◽  
Exchange Traded Funds ◽  
Data Set ◽  
Volatility Spillovers ◽  
Daily Data ◽  
Volatility Model ◽  
Management Perspective ◽  
The Us ◽  
Generated Regressors

It is well known that that there is an intrinsic link between the financial and energy sectors, which can be analyzed through their spillover effects, which are measures of how the shocks to returns in different assets affect each other’s subsequent volatility in both spot and futures markets. Financial derivatives, which are not only highly representative of the underlying indices, but can also be traded on both the spot and futures markets, include Exchange Traded Funds (ETFs), a tradable spot index whose aim is to replicate the return of an underlying benchmark index. When ETF futures are not available to examine spillover effects, “generated regressors” are useful for constructing both Financial ETF futures and Energy ETF futures. The purpose of the paper is to investigate the co-volatility spillovers within and across the US energy and financial sectors in both spot and futures markets, by using “generated regressors” and a multivariate conditional volatility model, namely Diagonal BEKK. The daily data used are from 1998/12/23 to 2016/4/22. The data set is analyzed in its entirety, and are also subdivided into three distinct subsets. The empirical results show there is a significant relationship between the Financial ETF and Energy ETF in the spot and futures markets. Therefore, financial and energy ETFs are suitable for constructing a financial portfolio from an optimal risk management perspective, and also for dynamic hedging purposes.

scholarly journals Volatility Transmission across Commodity Futures Markets

2018 ◽  
Author(s):  
Frankie Ho-Chi Chau
Keyword(s):  
Crude Oil ◽  
Futures Markets ◽  
Oil And Gas ◽  
Commodity Futures ◽  
Dynamic Interactions ◽  
Volatility Transmission ◽  
Volatility Spillovers ◽  
Crude Oil Prices ◽  
Commodity Futures Markets ◽  
Copper Gold

Sharp movements in crude oil prices and their impact on other commodities have renewed interest in the assessment of dynamic interactions between commodity futures markets. This chapter examines this topic by investigating the intensity and direction of volatility transmission across three major classes of commodities, including agricultural products (corn, coffee, and soybeans), energy (crude oil and gas), and metals (copper, gold, and silver). Overall, the evidence suggests that important volatility episodes and fluctuations exist across major commodity markets; the total cross-market spillovers are limited until the onset of financial crisis of 2007–2008. As the crisis intensified, so too did the commodity volatility spillovers, with substantial stress carrying over from the energy and metal markets to others. These findings are important in understanding the level and transmission mechanism of risk across commodity futures markets and are relevant to regulators in formulating policies to tackle excessive volatility, particularly during turbulent periods.

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