Return and volatility co-movement in commodity futures markets: the effects of liquidity risk

Zhang, Yongmin; Ding, Shusheng

doi:10.1080/14697688.2018.1444562

Cited by 19 publications

(12 citation statements)

References 38 publications

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“…Of course, trading volume is often taken as a rudimentary measure of liquidity (see, e.g., Darolles, Fol, & Mero, 2015; Domowitz & Wang, 1994; Gallant, Rossi, & Tauchen, 1992; Zhang & Ding, 2018). While both volume and liquidity in precious metals markets can be affected by a number of factors (see, e.g., Smales & Lucey, 2019), given liquidity's association with volume and as a corollary of Hypothesis 1a, we can state:…”

Section: Literature and Theoretical Underpinningsmentioning

confidence: 99%

Night trading and market quality: Evidence from Chinese and US precious metal futures markets

Jiang

Kellard

Liu

2020

Journal of Futures Markets

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Given a dominant exchange, how should other exchanges set their trading hours? We examine the introduction of a night session by the Shanghai Futures Exchange, allowing trading concurrently with daytime trading at the Commodity Exchange in the United States. After developing hypotheses, results for gold and silver show: trading activity has increased; liquidity in Shanghai has risen and prices are less volatile at market opening; the price discovery share of Chinese gold futures has fallen but this is not a sign of weakening market quality; and volatility spillovers increase bidirectionally. Longer trading hours have decreased market segmentation and increased information flow.

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Section: Literature and Theoretical Underpinningsmentioning

confidence: 99%

Night trading and market quality: Evidence from Chinese and US precious metal futures markets

Jiang

Kellard

Liu

2020

Journal of Futures Markets

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“…In addition, it has been found that the variation in the market liquidity (so called liquidity risk) is correlated with the informational content of the prices (Ng, ). Recent studies such as Zhang, Ding and Scheffel () and Zhang and Ding () have shown the significant liquidity effect on price volatility in commodity markets.…”

Section: Financial Hedging and Business Performance: A Reviewmentioning

confidence: 99%

Modeling Price Volatility Based on a Genetic Programming Approach

Ding

Zhang

Duygun

2019

British J of Management

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Business profitability is highly dependent on risk management strategies to hedge future cash flow uncertainty. Commodity price shocks and fluctuations are key risks for companies with global supply chains. The purpose of this paper is to show how artificial intelligence (AI) techniques can be used to model the volatility of commodity prices. More specifically, the authors introduce a new model – LIQ‐GARCH – that uses genetic programming to forecast volatility. The newly generated model is then used to forecast the volatility of the following three indexes: the Commodity Research Bureau (CRB) index, the West Texas Intermediate (WTI) oil futures prices and the Baltic Dry Index (BDI). The empirical model performance tests show that the newly generated model in this paper is considerably more accurate than the traditional GARCH model. As a result, this model can help businesses to design optimal risk management strategies and to hedge themselves against price uncertainty.

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“…Chen et al [17] reported evidence of a common time varying volatility factor in the United Kingdom, Singapore and Australia. In the same line Zhang and Ding [18] analyzed the volatility co-movements in different commodity futures markets, finding that various volatility measures of commodity return share a common trend, which can be interpreted as a common market volatility factor. Apparently, liquidity is an important transmission channel for volatility shocks.…”

Section: Introductionmentioning

confidence: 99%

Volatility Co-Movement in Stock Markets

et al. 2021

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The volatility and log-price collective movements among stocks of a given market are studied in this work using co-movement functions inspired by similar functions in the physics of many-body systems, where the collective motions are a signal of structural rearrangement. This methodology is aimed to identify the cause of coherent changes in volatility or price. The function is calculated using the product of the variations in volatility (or price) of a pair of stocks, averaged over all pair particles. In addition to the global volatility co-movement, its distribution according to the volatility of the stocks is also studied. We find that stocks with similar volatility tend to have a greater co-movement than stocks with dissimilar volatility, with a general decrease in co-movement with increasing volatility. On the other hand, when the average volatility (or log-price) is subtracted from the stock volatility (or log-price), the co-movement decreases notably and becomes almost zero. This result, interpreted within the background of many body physics, allows us to identify the index motion as the main source for the co-movement. Finally, we confirm that during crisis periods, the volatility and log-price co-movement are much higher than in calmer periods.

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Return and volatility co-movement in commodity futures markets: the effects of liquidity risk

Cited by 19 publications

References 38 publications

Night trading and market quality: Evidence from Chinese and US precious metal futures markets

Night trading and market quality: Evidence from Chinese and US precious metal futures markets

Modeling Price Volatility Based on a Genetic Programming Approach

Volatility Co-Movement in Stock Markets

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