Dynamic Correlation and Risk Contagion Between “Black” Futures in China: A Multi-scale Variational Mode Decomposition Approach

Wang, Qunwei; Dai, Xingyu; Zhou, Dequn

doi:10.1007/s10614-018-9857-y

Cited by 17 publications

(6 citation statements)

References 58 publications

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“…For investors with different investment horizons, the effects of jumps with multi-time scales may be not the same due to the heterogenous investment horizon ( Dai et al, 2020 ; Wang et al, 2020b ; Dai et al, 2021b ; Miao et al, 2022 ). For investors with short investment horizons, the short-term effects of stock price jumps will bring the most intuitive and significant losses ( Erdemlioglu and Gradojevic, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?

et al. 2022

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Section: Introductionmentioning

confidence: 99%

China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?

et al. 2022

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“…Q. Wang, Dai, and Zhou (2020); J. Wang and Zheng (2019) used the Vector Autoregressive (VAR) model and Granger causality test to investigate the linkage across futures and spot of iron ore and coke.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Q. Wang et al (2020) analyzed the co-movement between coke, cooking coal and pig iron. Johansen cointegration test and Granger causality test are applied.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Volatility Spillover Among Chinese Black Series Futures Under Structural Breaks

Yang¹,

Pastpipatkul²,

Nimanussornkul³

2020

Int. j. bus. adm. stud

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This paper explores the dynamic volatility spillovers among five major futures in China, including rebar, hot-rolled coils, iron ore, cooking coal and coke. We employ the Dynamic Conditional Correlation (DCC) GARCH model to examine the volatility spillover effects among the markets considering structural breaks invariance. We used a modified Iterated Cumulative Sum of Squares (ICSS) algorithm to detect the structural breaks. The empirical results show there are strong correlations across the black series futures market. Especially, the relation between rebar and hotrolled coils, coke and cooking coal is more closely than other pairs. This research provides insight into the information transmission in black series futures market, which is meaningful to market participants making hedging and trading strategies. This study extends the literature by investigating the dynamic linkage among five kinds of black series futures.

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“…However, the wavelet cannot arbitrarily represent the investment horizon that a portfolio maker would consider in practice. Dai et al (2023), Huang et al (2021), and Wang et al (2020) use variational modal decomposition (VMD) and empirical mode decomposition (EMD) group methods as MTA tools to get the subreturns representing different investment horizons, while both VMD and EMD group methods could not tell investors economic implications of subreturns and only get subreturns with some specific periods, not arbitrarily. Baruník and Křehlík (2018), Dai et al (2021), and Ouyang et al (2021) use an MTA tool named BK spillover index which flexibly reveals the interaction among multiassets under a certain investment horizon (frequency band), but BK spillover index cannot be directly applied to portfolio optimization studies.…”

Section: Introductionmentioning

confidence: 99%

“…This paper gives a proposed new methodology to calculate the weights of asset portfolio's under different investment horizons. Compared with the traditional EMD (Sun et al, 2022; Zhu et al, 2019), VMD (Dai et al, 2023; Wang et al, 2020) group models, or wavelet models (Dai et al, 2020), the proposed decomposition method in this paper is able to extract the subreturn series representing arbitrary investment horizon, and the economic meaning of the subreturn series is fully revealed. This paper also shows investors how to deal with multistage or long‐run decision‐making problems from a timescale perspective (Kim et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity

Dai

Zhang

Lau

et al. 2023

Journal of Forecasting

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This study designs a methodological framework to forecasting allocation weights and evaluating multiobjective portfolios considering the investment horizon heterogeneity. The investment horizon heterogeneity may induce the terrible data loss problem under traditional asset return definition, while in the real world, there are many portfolio makers whose investment horizons are not consistent. The methodological framework has three parts. First, this paper gives a novel multitimescale analysis (MTA) tool as a computation procedure to decompose the raw return series, and the decomposed subreturn series could represent the information for a portfolio maker with specific investment horizon and has the same data length as the raw return series. Second, proposed methodological framework uses a time‐varying parameter GAS‐D‐Vine‐Copula model to construct the joint distribution of subreturn series of multiassets in a portfolio. Third, due to the stochastic dominance consistency issue, this paper applies three different utility functions as the outputs of a portfolio strategy and two cost functions as the inputs of a portfolio strategy in an efficiency evaluation model. The empirical example of US aviation stock market data from 2013 to 2021 reveals that the Mean‐Skewness‐Volatility‐HMCR‐LPM multiobjective has the greatest numbers of optimal strategy timings for portfolio makers with 2‐, 3–5‐, and 10–50‐day‐length investment horizons. The investment horizon of 1‐day length is the least efficient, and the investment horizon between 10‐ and 50‐day length is the most efficient. The proposed methodology indicates that more multiobjectives in a portfolio strategy are not necessarily better, and there is an optimal range of investment horizons for portfolio makers.

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Dynamic Correlation and Risk Contagion Between “Black” Futures in China: A Multi-scale Variational Mode Decomposition Approach

Cited by 17 publications

References 58 publications

China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?

China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?

Dynamic Volatility Spillover Among Chinese Black Series Futures Under Structural Breaks

Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity

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