Robust Score and Portmanteau Tests of Volatility Spillover

The present work has been designed to intensely investigate the capability of the commodity futures market in achieving the aim of price discovery. Further, the downside of the cash and futures market and transfer of the risk to other markets has also been studied using VaR, and Bivariate EGARCH. The findings of the work point that the metal commodity derivative market helps in the efficient discovery of price in the spot market except for nickel. But, in the case of the agricultural commodities, the spot is found to be leading and thus there is no price discovery except turmeric. On the other hand, the volatility spillover is bidirectional for both agri and metal commodities except copper, where volatility spills only from futures to spot. Further, the effect of negative shock informational bias differs from commodity to commodity, irrespective of metal or agriculture.

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

Mathematics ◽

10.3390/math9121411 ◽

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.

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QUANTILE DOUBLE AUTOREGRESSION

Econometric Theory ◽

10.1017/s026646662100030x ◽

2021 ◽

pp. 1-47

Author(s):

Qianqian Zhu ◽

Guodong Li

Keyword(s):

Time Series ◽

Financial Time Series ◽

Simulation Studies ◽

Finite Sample ◽

Conditional Quantile ◽

New Model ◽

Conditional Quantiles ◽

Financial Time ◽

Portmanteau Tests ◽

Strict Stationarity

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.

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