The Impact of SHFE's Night Trading Session on Volume and Realized Volatility of Aluminum and Copper Futures Markets

2018 ◽  
Author(s):  
Tony Klein ◽  
Neda Todorova
Keyword(s):  
Futures Markets ◽  
Realized Volatility ◽  
The Impact

scholarly journals Volatility Forecasting Based on Cyclical Two-Component Model: Evidence from Chinese Futures Markets and Sector Stocks

2020 ◽  
Vol 25 (3) ◽  
pp. 59
Author(s):  
Conghua Wen ◽  
Junwei Wei
Keyword(s):  
Futures Markets ◽  
Realized Volatility ◽  
Futures Market ◽  
Component Model ◽  
Type Model ◽  
Volatility Forecasting ◽  
Improved Method ◽  
Two Component ◽  
Model Evidence ◽  
The Impact

This article aims to study the schemes of forecasting the volatilities of Chinese futures markets and sector stocks. An improved method based on the cyclical two-component model (CTCM) introduced by Harris et al. in 2011 is provided. The performance of CTCM is compared with the benchmark model: Heterogeneous Autoregressive model of Realized Volatility type (HAR-RV type). The impact of open interest for futures market is included in HAR-RV type model. We employ 3 different evaluation rules to determine the most efficient models when the results of different evaluation rules are inconsistent. The empirical results show that CTCM is more accurate than HAR-RV type in both estimation and forecasting. The results also show that the realized range-based tripower volatility (RTV) is the most efficient estimator for both Chinese futures markets and sector stocks.

scholarly journals Volatility Spillovers Arising from the Financialization of Commodities

2018 ◽  
Vol 11 (4) ◽  
pp. 72 ◽  
Author(s):  
Wing Chan ◽  
Bryce Shelton ◽  
Yan Wu
Keyword(s):  
Commodity Prices ◽  
Spillover Effects ◽  
Realized Volatility ◽  
Commodity Markets ◽  
Exchange Traded Funds ◽  
Volatility Transmission ◽  
Volatility Spillovers ◽  
The Difference ◽  
Index Investment ◽  
The Impact

This paper examines whether the proliferation of new index products, such as commodity-tracking exchange-traded funds (ETFs), amplified the volatility transmission channel introduced by financialization. This paper focuses on the volatility spillover effects among crude oil, metals, agriculture, and non-energy commodity markets. The results show financialization has an impact on the volatility of commodity prices, predominantly for non-energy commodities. However, the impact on volatility is not symmetric across all commodities. The analysis of index investment and investors’ positions in futures markets shows that, when a relationship exists, it is generally negatively correlated with the realized volatility of non-energy commodities. Using realized volatility in the difference-in-difference model provides estimates that are inconsistent with other findings that non-energy commodities, traded as a part of indices, have experienced higher volatility. The results are similar to the index investment and futures market analysis, where increased participation by investors through new investment products has put download pressure on realized volatility.

HOW DOES BLOCKCHAIN PARTICIPATION AFFECT THE CRYPTOCURRENCY MARKET?

2020 ◽  
pp. 1-24
Author(s):  
YI LI ◽  
WEI ZHANG ◽  
PENGFEI WANG
Keyword(s):  
Trading Volume ◽  
Positive Impact ◽  
Granger Causality Test ◽  
Realized Volatility ◽  
Var Model ◽  
Alternative Measure ◽  
Theoretical Studies ◽  
Causality Test ◽  
Vector Autoregressive ◽  
The Impact

Taking the unique advantage of the cryptocurrency market setting, this paper examines the relationships between blockchain participation and returns, trading volume and realized volatility of main cryptocurrencies (i.e., Bitcoin, Ethereum and Litecoin). Dissimilar to previous theoretical studies that model the influencing factors on participation, we employ the number of unique from addresses 1 as the proxy for cryptocurrency investors’ blockchain participation and further explore the impact of such participation. By using vector autoregressive (VAR) model, we find that the blockchain participation has a significant and positive impact on the next day’s trading volume and realized volatility for the main cryptocurrencies. Our results are robust to the Granger causality test and alternative measure for blockchain participation.

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.

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