Copper Futures Market Research Articles

Investigating the profit performance of quantitative timing trading strategies in the Shanghai copper futures market, 2020–2022

AbstractIn conducting an extensive examination, we scrutinize the efficacy of algorithmic trading strategies applied to Futures CopperMainContinuous in the Shanghai Futures Exchange, utilizing a comprehensive data set spanning from January 2020 to December 2022. To mitigate the potential risk of data‐snooping bias—the probability that any favorable results may inadvertently arise from random events rather than the inherent value of the strategies employed to generate these results—our study prudently conducts a reality check and advanced assessments. Throughout the evaluated period, the benchmark demarcation between the in‐sample and out‐of‐sample stages is established in February 2022. Regrettably, our meticulous exploration fails to identify any successful or advantageous algorithmic trading strategies within these categories, particularly following the systematic elimination of data snooping bias. These results underscore the intrinsic challenges in accurately identifying and implementing profit‐generating algorithmic trading strategies within the volatile and intricate futures market.

An analysis of the market efficiency of the Chinese copper futures based on intertemporal and intermarket arbitrages

This paper studies the market efficiency and relevant issues of the copper futures traded on the Shanghai Futures Exchange (SHFE) in China. Since its listing, the market, grown unceasingly in scale and internationalization level, has become the largest copper futures trading market in the world (the London Metal Exchange (LME) copper futures market is the second largest). Using the data from 2006 to 2022 as a sample, a period covering the outbreak of the COVID-19 pandemic and the internationalization of the Chinese copper futures, this paper analyzes the market efficiency of China’s copper futures based on both intertemporal and intermarket strategies (SHFE Copper Futures and LME Copper Futures, with considering time difference factor). To better reflect the actual situation, this paper, when establishing trading strategy models, not only took into account various actual trading factors such as transaction costs, market impact costs, liquidity costs, margin, and capital costs but also combined daily and high-frequency data to present highly practical arbitrage strategies. According to empirical results, overall, it was not found significant arbitrage opportunities to violate the market efficiency rule. Although some arbitrage opportunities indeed emerged, they were infrequent, especially when transaction costs were considered. Specifically, in terms of the intertemporal strategy, despite that infrequent arbitrage opportunities did appear in the SHFE copper futures market, they failed to bring substantial gains, some of which even caused losses in extreme cases. In terms of the intermarket strategy, there is a cointegration relationship between the logarithmic price of LME copper futures and the logarithmic price of SHFE copper futures. The average annualized rates of return in the results of the in-sample empirical analysis were high, but in the out-of-sample empirical analysis of high frequency and daily data were not found significant arbitrage opportunities. The influence between the LME copper futures market and the SHFE copper futures market was bidirectional.

Research on the Copper Futures Market in China

This paper examines the role of copper futures in China's financial market, listed in the Shanghai Futures Exchange (SHFE) in 1991 as the first nonferrous metal futures commodity. Today, it is one of the well-developed and mature futures contracts in the Chinese commodity futures market. We analyze the current situation of the Chinese copper market, both spot and futures, along with the role of hedging with copper futures in the whole copper industry chain and the role of copper futures in the spot market. The results show that hedging with copper futures is significant for metal enterprises from all parts of the copper industry chain as an essential tool to offset price risks and for stable development; it also optimizes resource allocation in the economy and improves the efficiency of Chinese metal enterprises.

The Impact of COVID-19 on the Volatility of Copper Futures

The COVID-19 pandemic has introduced significant uncertainty across various economic sectors, most notably in the industrial sector due to the high volatility in copper futures markets. These markets play a crucial role in the construction, electrical networks, electronic products, and industrial machinery industries. Therefore, the aim of this study is to evaluate the impact of the COVID-19 pandemic on the volatility of copper futures returns from 2 January 2018 to 30 December 2022 using an extended ARMA(m,n)–GARCH(p,q) framework. According to the results, the occurrence of the pandemic nearly doubled the long-run equilibrium volatility of returns compared to the values obtained in the pre-pandemic period. Hence, we conclude that the COVID-19 pandemic has a significant influence on the volatility in the copper futures market.

A comparative exploration of the chaotic characteristics of Chinese and international copper futures prices

This study used a C–C method to reconstruct the phase space of copper prices from January 2002 to December 2021, using Chinese and international copper futures price time series as a sample. It demonstrates the differences in the chaotic characteristics of two copper futures prices through recurrence plots and correlation dimensions. Recurrence complex networks have been employed to quantify the spatial structural similarity between the variables and copper prices, and the main factors influencing the structural changes in copper prices have been analysed by a systematic bifurcation mechanism. The results show that the efficiency of the copper futures market has improved gradually. The level of perfection and efficiency of the Chinese copper futures market is lower than that of the international copper futures market, and the predictability of Chinese copper futures prices is even weaker, which means that the investment in Chinese copper futures is riskier than that in the international copper futures market. Compared to other metal futures, such as aluminium and nickel, copper futures are likely to be a lower risk investment. The US dollar index, the federal funds rate and the price of gold are the main factors influencing changes in the spatial structure of copper prices in China, while political risk, crude oil and gold prices are key to shaping the chaotic character of international copper prices. A tipping point in the chaotic state of copper prices could correspond to the onset of a global financial crisis. This study can help policymakers to understand the market laws underlying prices and thus create robust and well-traded markets by controlling the chaotic characteristic structure of copper prices. In addition, the identification of chaotic state thresholds in Chinese and international copper prices can predict the occurrence of unexpected events such as financial crises, which is important to counteract financial market risks.

Copper cross-market volatility transition based on a coupled hidden Markov model and the complex network method

Copper, as a strategic resource, has attracted widespread attention due to its price changes. Copper's futures prices and spot prices tend to be highly correlated in different countries. The copper market price change should consider the relationship between market trends of different countries, especially in the current situation where economic globalization and financial integration are inevitable. The volatility transition between datasets of different countries presents the characteristics of nonlinearity, interaction, dynamics and heterogeneity. To explore the cross-market transition of copper price fluctuations, this paper proposes a hybrid method of the coupled hidden Markov model (CHMM) and complex network. The coupled hidden Markov model (CHMM) is used to identify the transition characteristics of the market price trends, and the complex network method is used to analyze the evolutionary characteristics of the market state transition. We take the copper futures and spot market prices between the U.S., China, and the UK between January 2003 and December 2019 as the object. The results show that the copper futures market's state transition has a clustering effect, the price rise is easy to maintain, and the decline is rapid and easy to transfer. The relationship between daily markets is more volatile, and weekly and monthly are more stable. The British copper futures market has shown advantages in terms of transition ability, assimilation ability, media ability and transfer structure, while the influence of China's copper futures market is relatively weak. In addition, during the financial crisis, the UK spot market played an important role in the frequent declining transition. The nonlinear measurement model and conclusion established in this paper provide a new framework and analysis tool for explaining the volatility transition of the copper market.

The Relationship Between China’s Real Estate Market and Industrial Metals Futures Market: Evidence from Non-price Measures of the Real Estate Market

By using non-price indicators of the real estate market, this paper examines the relationship between the real estate market and the industrial metals futures market in China during the 2004–2019 period. Empirical findings from a vector autoregression model (VAR), a causality study, and cointegration analysis suggest that, in the context of China, industrial metals futures have both short-run and long-run associations with the real estate market. The effectiveness of the mechanisms through which the real estate market affects the industrial metals futures market, however, varies across underlying assets and pre-specified indicators. More specifically, the shock of the size of newly started constructions has the greatest accumulated impacts on the copper futures market, increasing the price of copper futures by 2.46% after two years. Additionally, 11.31% of the changes in the price of copper futures can be attributed to fluctuations in the size of newly started constructions, in which the explanatory power has increased horizontally. The results of impulse response functions (IRFs) show that the price of rebar futures is the most sensitive to volatility in sales size in the real estate market, in which the rebar futures price can be expected to increase by 1.65% after two years. The results of the forecast error variance decomposition (FEVD) method suggest that fixed asset investment in the real estate market makes the largest contribution (about 6.28%) to corresponding movements in the rebar futures price.

Prediction of metal futures price volatility and empirical analysis based on symbolic time series of high-frequency

Abstract The metal futures price fluctuation prediction model was constructed based on symbolic high-frequency time series using high-frequency data on the Shanghai Copper Futures Exchange from July 2014 to September 2018, and the sample was divided into 194 histogram time series employing symbolic time series. The next cycle was then predicted using the K-NN algorithm and exponential smoothing, respectively. The results show that the trend of the histogram of the copper futures earnings prediction is gentler than that of the actual histogram, the overall situation of the prediction results is better, and the overall fluctuation of the one-week earnings of the copper futures predicted and the actual volatility are largely the same. This shows that the results predicted by the K-NN algorithm are more accurate than those predicted by the exponential smoothing method. Based on the predicted one-week price fluctuations of copper futures, regulators and investors in China’s copper futures market can timely adjust their regulatory policies and investment strategies to control risks.

China's copper futures market efficiency analysis: Based on nonlinear Granger causality and multifractal methods

Abstract: Price discovery and market efficiency are the centerpiece of the market microstructure design. This study investigates the nonlinear correlation between the spot and futures prices in China's copper market using nonlinear Granger causality and multifractal methods, and it further analyzes the dynamic efficiency of China's copper futures market. According to the results of the nonlinear Granger causality test, there is a significant bidirectional nonlinear causality between the spot and futures prices in China's copper market. Excluding the effects of the first and second orders, there is still a high-order correlation between the spot and futures prices of the copper market. Additionally, the multifractal detrended cross-correlation analysis (MF-DCCA) method is used to obtain the long-term correlations of high order in the spot and futures markets. The time-varying rolling Hurst exponent indicates that the efficiency of China's copper futures market has gradually become more effective over time.

Volatility–volume relationship of Chinese copper and aluminum futures market

Abstract Following Bessembinder and Seguins, trading volume is separated into expected and unexpected components. Meanwhile, realized volatility is divided into continuous and discontinuous jump components. We make the empirical research to investigate the relationship between trading volume components and various realized volatility using 1 min high frequency data of Shanghai copper and aluminum futures. Moreover, the asymmetry of volatility–volume relationship is investigated. The results show that there is strong positive correlation between volatility and trading volume when realized volatility and its continuous component are considered. The relationship between trading volume and discontinuous jump component is ambiguous. The expected and unexpected trading volumes have positive influence on volatility. Furthermore, the unexpected trading volume, which is caused by arrival of new information, has a larger influence on price volatility. The findings also show that an asymmetric volatility–volume relationship indeed exists, which can be interpreted by the fact that trading volume has more explanatory power in positive realized semi-variance than negative realized semi-variance. The influence of positive trading volume shock on volatility is larger than that of negative trading volume shock, which reflects strong arbitrage in Chinese copper and aluminum futures markets.

Structural breaks and volatility forecasting in the copper futures market

This paper examines whether structural breaks contain incremental information for forecasting the volatility of copper futures. Considering structural breaks in volatility, we develop four heterogeneous autoregressive (HAR) models based on classical or latest HAR‐type models. Subsequently, we apply these models to forecast volatility in the copper futures market. The empirical results reveal that our models exhibit better in‐sample and out‐of‐sample performances than classical or latest HAR‐type models. This suggests that structural breaks contain incremental prediction information for the volatility of copper futures. More importantly, we argue that considering structural breaks can improve the performances of most of existing HAR‐type models.Highlights There are many structural break points in return volatility of the copper futures. We propose 12 new heterogeneous autoregressive models. Our models outperform the existing heterogeneous autoregressive models. Structural breaks contain additional ex ante information for volatility forecasting. The ex ante information is obvious in forecasting mid‐ and long‐term volatilities.

Analysis of Optimal Hedging Ratio of Copper Futures Based on ECM Model

In this paper, on the one hand, we need to prove that there is a cointegration relationship between copper futures price and spot price. Then, using the ECM model to estimate the optimal hedging resale value of copper futures contract in our country, it is based on the frequency statistics method, the optimal hedging strategy, the approach does not consider the existence of risk estimation, and the most natural way to solve this problem is the bayesian method. So, on the other hand, we are under the bayesian framework, Gibbs sampling method is used to the Chinese copper futures market, the optimal hedging strategy has carried on the empirical analysis. By comparing the hedging efficiency, the result of hedging based on Bayesian statistics is better than that based on frequency statistics.

A Research on the Risk Measure of Chinese Copper Futures Market Based on VaR

Measuring the risk of the Chinese Copper futures market is the key point of the risk management. Based on the normal distribution, T-distribution and GED-distribution, this paper measures the VaR values of the risk of the copper futures by GARCH and EGARCH models. Using empirical testing, it shows the EGARCH-N model can characterize the market risk of the copper futures more precisely than other types of models.

Exogenous Shocks and Information Transmission in Global Copper Futures Markets

AbstractBivariate EGARCH models are used to investigate mean and volatility spillovers across major global copper futures markets before and after the Global Financial Crisis. We show that the overall magnitude and significance of information spillovers strengthen after the crisis. The exogenous shocks not only exhibit considerable information spillovers on copper futures markets but also enhance information transmission among them, including bi‐directional mean and volatility spillovers and long‐run equilibrium. Moreover, our results shed light on the growing importance of the Shanghai Copper futures market in information transmission.

Modeling Copper Prices: Ornstein-Uhlenbeck Process and Jump-Diffusions

In this paper we focus on parameter estimation of the futures price processes with a Ornstein-Uhlenbeck process and jump-diffusions. We use the generalized moment method to derive the OU process. Afterwards, we fit a jump diffusions model to Copper prices from Shanghai Copper futures market.

Price discovery for copper futures in informationally linked markets

The purpose of this article is to contribute to the research on informationally linked markets by investigating the relationships between the Chinese copper futures market and its London counterparts. There is a long run relationship between the Shanghai Futures Exchanges (SFE) with London Metals Exchange (LME) copper futures prices. Furthermore, we find that three regime Markov switching model with changing intercept and variance turns out to be good description of the data. The influence of LME on SFE is bigger than that of SFE on LME.

Price linkages between Chinese and world copper futures markets

The purpose of this paper is to investigate the time varying relationships between the Chinese copper futures market and its London counterparts. Rolling correlation and rolling Granger causality test show that with the development of the Shanghai copper futures markets, it has stronger connections with its London counterpart and it plays more and more important role in the price discovery process. There is a long run relationship between the Shanghai futures exchanges (SHFE) and London Metals exchanges (LME) copper futures prices. The influence of LME on SHFE is greater than that of SHFE on LME. The research will shed light on the openness of the Chinese copper commodity markets and on the nature of cross-market information transmission.

An empirical study on information spillover effects between the Chinese copper futures market and spot market

This study employs a parametric approach based on TGARCH and GARCH models to estimate the VaR of the copper futures market and spot market in China. Considering the short selling mechanism in the futures market, the paper introduces two new notions: upside VaR and extreme upside risk spillover. And downside VaR and upside VaR are examined by using the above approach. Also, we use Kupiec’s [P.H. Kupiec, Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives 3 (1995) 73–84] backtest to test the power of our approaches. In addition, we investigate information spillover effects between the futures market and the spot market by employing a linear Granger causality test, and Granger causality tests in mean, volatility and risk respectively. Moreover, we also investigate the relationship between the futures market and the spot market by using a test based on a kernel function. Empirical results indicate that there exist significant two-way spillovers between the futures market and the spot market, and the spillovers from the futures market to the spot market are much more striking.

Detecting and modeling changing volatility in the copper futures market

Copper futures returns are characterized by negative skewness and excess kurtosis. Research has not yet examined this nonnormality, which contributes to their volatility. To date little attention has been paid to the modeling of these series. Therefore, the purpose of this paper is to (i) detect alternating subperiods of volatility by using a method that uses an iterated cumulative sum of squares (ICSS) algorithm to identify breakpoints in the series; and (ii) compare the ability of five models (the random walk, GARCH, EGARCH, AGARCH, and the GJR model) to capture the volatility within each ICSS identified subperiod. These tests were applied to two copper futures series (open to close and close to close prices). Results indicate that the ranking (in terms of the root mean square error) is similar for both series. That is, the GARCH or EGARCH model rank first and second, depending on the series, followed by the GJR model. AGARCH and the random walk models perform poorly.© 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 79–100, 1999

Detecting and modeling changing volatility in the copper futures market

Detecting and modeling changing volatility in the copper futures market