Commodity Index Research Articles

Diversification, hedging, and safe-haven characteristics of cryptocurrencies: A structural change approach

This study investigates the influence of structural change on the diversification, hedging, and safe-haven characteristics of Bitcoin and Ethereum against various financial assets such as gold, the US Dollar Index, stock indices, oil, and commodity indices from August 7, 2015, to August 15, 2022, using the DCC–ARMA–GARCH models with the CUSUM test. Our results indicate that cryptocurrencies have the same characteristics vis-à-vis financial markets during the entire sample period and periods tied to the date of major international events (COVID-19 and the early-2022 Russia–Ukraine War). However, we find that cryptocurrencies play different roles against specific asset markets in different periods separated by structural change models. Our findings suggest that incorporating structural changes into a model accounts for higher volatility and may better describe the real-world capabilities of cryptocurrencies against financial assets.

Insights From Commodity Markets Reaction to the Russia–Ukraine War

The article aims to investigate how the Russia–Ukraine war has affected India’s base metal, energy, and bullion markets using short-term event technique. The official website of Multi Commodity Exchange of India Limited (MCX) has provided the daily prices for nine specific commodities and selected commodity indexes. The study discovered that the two countries’ conflict had no discernible effect on the Cumulative Abnormal Returns (CARs) of a few selected indices. However, more in-depth examination of specific commodities showed that the CAR of just the base metal index was considerably impacted. The study also observed that the war had an impact on returns of investors who experienced positive returns only in case of nickel whereas negative returns in case of copper, lead and zinc. However, analysis of the cumulative average abnormal return (CAAR) of the selected indices has revealed a consistent and significant negative impact which could be due to other external factors influencing prices of the commodity index and individual indices. The present study is quite useful to investors, traders, analysts, policy makers and risk managers as it assists them to make informed investment decisions, manage risk, identify potential opportunities in the market and develop trading strategies.

The dynamic volatility nexus of geo-political risks, stocks, bond, bitcoin, gold and oil during COVID-19 and Russian-Ukraine war.

We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.

Did Covid-19 Impacted Market Arrivals and Prices of Major Food Commodities in India: Evidence from Extended Time Series Analysis

Did Covid-19 Impacted Market Arrivals and Prices of Major Food Commodities in India: Evidence from Extended Time Series Analysis

Navigating Choppy Waters: Interplay between Financial Stress and Commodity Market Indices

Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16 June 2016 to 9 July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.

Forecasting the Nexus and Impact of News Sentiment on NYSE, Gold Prices, and WTI Oil Using The Neural Network Approach

The study aims to determine the impact of news sentiment during COVID-19 and the nexus between the NYSE, Gold prices, and WTI oil prices. For this purpose, we use hourly data of news sentiment and commodity prices hourly data from February 10, 2020, hours 1000 to March 06, 2020 hours 1600 hours, with the help of a multilayer artificial neural network. Further, We use the connection weight approach to observe the relative importance of input variables that help predict output variables. The results suggest that all the indexes are interconnected, where the increase/decrease of any index impacts the other commodity index. Further, the news index remains the second highest predictor for gold price and WTI oil price indexes. In addition, the news index also seems to be an essential predictor of these commodities.
 Keywords: NYSE, WTI Oil, Gold, Covid-19, Neural Network, Connection Weight

Do shipping freight markets impact commodity markets?

The Baltic Dry Index (BDI) is mostly perceived as the leading indicator of shipping freight market and economic activity. In this study, we investigate the dependence structure and distributional predictability between BDI and nineteen major commodity investable indices to offer insights on the predictive ability of BDI for commodity assets. In addition, we explore the effects of policy uncertainty and geopolitical risk on the dynamic dependence structure between BDI and commodities futures. To this end, we apply time-varying Markov switching copula models and non-parametric causality in quantile approach to daily closed price indices of commodities futures running from 23rd May 2006 to 20th November 2020. We provide for the first-time empirical evidence of time-varying Markov tail dependence structure dynamics between marine activities and commodities futures. The nature of the dependence is positives except for petroleum, all metals, livestock wheat, cattle and softs markets. Furthermore, we show the significant effects of global uncertainties, policy uncertainty and geopolitical risk on the dependence between sea transport and commodity assets. Finally, we note from the causality-in-mean and causality-in-variance analysis that the daily change of BDI is an efficient predictor (informational efficiency) of the daily changes of most of the commodity indices. Finally, we show that markets.

Financial Market Stress and Commodity Returns: A Dynamic Approach

This paper examines the relationship between commodity index returns and the Office of Financial Research Financial Stress Index (OFR FSI). Utilizing the S&P GSCI and its five sub-indices (agriculture, livestock, energy, industrial metals, and precious metals), we find that the causal relationship between financial market stress and commodity index returns is conditional on the sample period examined and the methodology employed. We also note that stress in financial markets has a negative relationship with commodity index returns during low commodity return states; however, during high commodity return states, financial market stress exhibits a positive relationship with commodity index returns. Our findings highlight the importance of considering a time-varying framework for analyzing commodity return dynamics.

Exploring the dynamic connectedness and portfolio diversification strategies between energy and non-energy commodities in India

Exploring the dynamic connectedness and portfolio diversification strategies between energy and non-energy commodities in India

Volatility spillovers and nexus among agridex and non-agri indexes in Indian commodity markets

Volatility spillovers and nexus among agridex and non-agri indexes in Indian commodity markets

TVP-VAR based time and frequency domain food & energy commodities connectedness an analysis for financial/geopolitical turmoil episodes

Amidst the current global inflationary challenges, the concurrent rise of energy and agricultural commodity prices, which constitute the primary components of consumer prices, has emerged as a matter of significant interest among both scholars and policymakers. To this end, this study examines the dynamic interlinkages between food and energy commodity indexes from 2005:1 to 2023:3, covering turmoil episodes such as the Global Financial Crisis (GFC), the COVID-19 pandemic, and the Russia-Ukraine Conflict (RUC). Additionally, following Broadstock et al. (2022), we perform dynamic portfolio analyses to determine portfolio performances under 3 different portfolio construction approaches. The empirical results presented in this paper allow for a number of important findings. First, both the time and frequency-domain connectedness indexes associate with major financial/geopolitical stress events. Second, the fuel energy and the crude oil price indexes are the largest propagators and recipients of spillovers Third, the cumulative portfolio returns exhibit significant growth during the early phase of the COVID-19, declining during the RUC, and a notable upswing during the GFC. Finally, our findings for frequency-dependent connectedness networks indicate that the market is particularly susceptible to short-term shocks. This paper has significant ramifications for investors, market players, and policymakers.

How Does the Russian-Ukrainian War Rock Stock and Commodity Markets? Fresh Insights from Joint Network-Connectedness Analysis

ABSTRACT The outbreak of the war in Ukraine has had a profound and far-reaching impact on the global economy, with notable repercussions observed in stock markets, and particularly pronounced effects evident in commodities markets. This paper examines the connectedness network among 27 NATO stock markets, Russian stock market and a set of three commodity indices (energy, precious metals, and agricultural commodities) over the period 2017-2023. The empirical strategy consists of time and time-frequency connectedness metrics. The empirical results reveal that the connectedness structure has shifted during the Russian-Ukrainian conflict. Moreover, hit by a series of Western sanctions, Russia’s stock market appears to be the most isolated of the considered markets during the war period. Furthermore, the energy, agricultural and precious metals commodities seem to be efficient hedging instruments for investors in the stock markets of the NATO countries during the war period.

Energy prices volatility, natural resource policy-making and green economic recovery in post COVID-19 era: Evidence from BRICS countries

The connectedness of natural resource volatility and economic growth has been an area of central attention for academicians and researchers before and even after the COVID-19 pandemic. The global as well as the Asian financial crises have negatively impacted the natural resources and economic growth of countries. Literature lacks the impact of crises on natural resources especially in BRICS economies. Hence, the current study examines the agriculture, livestock, crude oil, metal, and, coal-based natural resource commodities in determining the sustainable economic growth of BRICS countries. To achieve the objectives, data from the GSC index (Goldman Sachs Commodity Index) and different World Bank indicators is collected over a period from 2002 to 2022 which also includes the era of Post COVID-19 pandemic. Augmented dicky fuller test is used to explore the stationarity of constructs while ECM (error correction model) and Granger causality test are used to capture the connectedness between the explanatory and dependent variables. The empirical results infer that prices of commodities based on agriculture, livestock, oil, coal, and valuable metals are positively linked with sustainable economic performance in BRICS Countries. The results may be supportive of accelerating economic growth by improving the extraction and consumption of natural resources, livestock, agriculture, precious metals, oil, and coal.

Dependency and causal relationship between ‘Bitcoin’ and financial asset classes: A Bayesian network approach

AbstractThis study employs the Bayesian Networks (BN) and the wavelet coherence approaches to invest the relationship between Bitcoin volatility and financial asset classes (MSCI world equity index, S&P Goldman Sachs Commodity Index [GSCI], US index and Investment Grade Corporate Bond Index ETF [PIMCO]) using daily data for the period from August 2011 to October 2021. The results show that the causal relationship between Bitcoin and other financial assets varies depending on the market states. During the low volatility periods, Bitcoin has a stronger impact on the GSCI, while during the stability periods, it has a direct effect on the US index and the MSCI world index. In contrast, during high volatility periods, Bitcoin has a direct impact on both the GSCI and PIMCO indices. The key findings enabled us to provide implications for US investors to promote asset allocation and risk management covering both Bitcoin and traditional financial markets. The results suggest that policymakers should watch Botcoin closely to preserve financial stability.

Using the Ordered Weighted Average Operator to Gauge Variation in Agriculture Commodities in India

Agricultural product prices are subject to various uncertainties, including unpredictable weather conditions, pest infestations, and market fluctuations, which can significantly impact agricultural yields and productivity. Accurately assessing and understanding price is crucial for farmers, policymakers, and stakeholders in the agricultural sector to make informed decisions and implement appropriate risk management strategies. This study used the ordered weighted average (OWA) operator and its extensions as mathematical aggregation techniques incorporating ordered weights to capture and evaluate the factors influencing price variation. By generating different vectors related to different inputs to the traditional formulation, it is possible to aggregate information to calculate and provide a new view of the outcomes. The results of this research can help enhance risk management practices in agriculture and support decision-making processes to mitigate the adverse effects of price.

An innovative high-frequency statistical arbitrage in Chinese futures market

The primary use of futures is hedging risk. Traders in the spot market can hedge certain risks through the futures market. With the development of the futures market, the arbitrage transactions around futures have attracted increasingly attention. The aim of this paper is to establish an innovative and unique pairs trading framework, and use it to test the effectiveness of China's futures market. The framework for pairs trading is based on cointegration test, Kalman filter and Hurst index filtering. We use the data of 47 commodities with relatively good liquidity in the Chinese commodity futures market. We apply the representative index of China's commodity futures market, "Wenhua Commodity Index" as the benchmark, to evaluate the performance of the strategy and compare it with the benchmark model. This study found that, according to the pairs trading framework, after considering transaction costs, the cumulative return in the sample reached 81%, the cumulative return out of the sample was 21%. It is worth noting that the out-of-sample maximum drawdown achieved excellent results of no more than 1%. In the same period, trading the Wenhua Commodity Index with a "buy and hold" strategy achieved a gain of 31%, but with maximum drawdown reached 15%. The values of our paper are, first it proves that Chinese commodity futures market is not a weak-form efficient market, because technical analysis based on machine learning could obtain excess returns. Second, this research combines the theory and practice of statistical arbitrage, which also provides guiding significance for investment practice.

The evolution of commodity market financialization: Implications for portfolio diversification

The financialization of commodity markets is a well-documented phenomenon spurred by the massive growth of institutional funds directed into commodity indices from the mid-2000s. More recent research suggests that a subsequent era of de-financialization has coincided with the retreat of institutional investors. This paper uses a latent factor model to examine the dynamic impact of commodity market financialization on spot currency, commodity and equity market linkages, focusing on countries with ‘commodity currencies’. The financialization period is characterized by increased interdependence of non-oil and oil commodity markets with each other and with other asset markets, implying reduced diversification potential. We find that commodity markets have become more highly interconnected with currency and equity markets of the large commodity exporters over the most recent sub-sample. We suggest that apparent de-financialization may be attributable to contagion effects from global crisis events, including the Great Recession and the European Debt Crisis of 2012.

Threshold Cointegration and Price Transmission in Commodity Marketsof India

The purpose of this research work is to examine the relationships and price dynamics between agricultural commodities in India, i.e.maize, wheat, barley and soybean. Our approach is to study the long-term relationship using the method of modelling the price transmission for both linear and threshold autoregressive (AR) models and vector error correction (VEC) models. Results revealed that all the price series are well integrated, and threshold error correction models prove that all price series move to restore the long-run relationship, whereas commodity stock prices respond slightly faster than market prices in the short-run. Conclusions from this study show that understanding the price transmission flow and its impact on pricing might help in making better trading strategies. It also regulates the public policy implications of the active participation of farmers in national-level commodity exchanges.

Forecasting base metal prices with exchange rate expectations

AbstractIn this paper, we show that survey‐based expectations of the Chilean exchange rate have the ability to predict the returns of the six primary non‐ferrous metals: aluminum, copper, lead, nickel, tin, and zinc. Predictability is also found for returns of the London Metal Exchange Index. Previous studies have shown that the Chilean exchange rate has the ability to predict copper returns, a world commodity index and base metal prices. Nevertheless, our results indicate that expectations about the Chilean peso have stronger predictive ability relative to the first lag of the Chilean currency. This is shown both in‐sample and out‐of‐sample. By focusing on expectations of a commodity currency, and not on the currency itself, our paper provides indirect but new and strong evidence of the ability that commodity currencies have to forecast commodity prices.

Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network

In this paper, we design and apply the Long Short-Term Memory (LSTM) neural network approach to predict several financial classes’ time series under COVID-19 pandemic crisis period. We use the S&P GSCI commodity indices and their sub-indices and consider the stock market indices for different regions. Based on the daily prices, the results show that the proposed LSTM network can form a robust prediction model to determine the optimal diversification strategies. Our prediction model achieved RMSEs and MAEs too small for the different selected financial assets, showing the predictive power of our LSTM network especially during the COVID-19 health crisis. In addition, our LSTM network outperforms ARIMA-type models for all selected assets.