Soft commodities and the global financial crisis: Implications for the economy,resources and institutions

Over the past decade, soft commodities have been subjected to increasing speculative price fluctuations. Following the 2008 financial crisis, most studies have highlighted causal relationships between price volatility, derivative and future markets for underlying financial assets as well as agricultural and mineral commodities. This article investigates the multifaceted effects of unrestrained financialization of the resources and goods markets and its implications for agricultural markets and soft commodities for purposes other than direct human consumption. We place a particular emphasis on the process of commodification of food and non-food crops and their use as green source of liquid fuels (i.e. soy, sugar cane, palm oil, jatropha, and canola). It is argued that speculation in financial markets has led to spillover effects across commodity and resource markets. More importantly, speculation and price volatility in the commodity markets has had a direct bearing on the resource markets and organization and appropriation of common-pool resources. The article sheds further light on the causal relationship between derivative markets, hedging techniques, financial yields and price volatility and spillover effects in the market for food and soft commodities.     

Commodity markets through the business cycle

From 2008 to 2011, commodity markets experienced growing attention from the banking industry for various reasons: the summer 2008 oil price swing, the price surge in an ounce of gold, or sharp variations in agricultural prices. As a consequence, can we hypothesize the existence of a global connection between commodities and economic cycles? If these recent events suggest that commodity markets are strongly related to the business cycle, this evidence goes nevertheless against the widespread intuition that commodity markets are a strong source of diversification in a standard cash–bond–equity portfolio. Based on a data-set from 1990 to present, this paper investigates this issue by (i) looking at the reaction of commodity markets to economic news, and (ii) using a Markov regime-switching model to analyse economic regimes and commodity markets as an asset class.     

2007年中国金融市场运行分析报告

2007年,我国金融市场继续保持了快速健康的发展势头,货币市场成交继续放大,市场利率短期波动加剧;债券市场运行平稳,债券指数走势较为疲弱;股票市场成交大幅放大,市场指数创出新高;人民币继续走强,外汇市场成交保持稳定;黄金市场成交量快速增长,黄金价格创出新高;期货市场成交活跃,商品期货价格全面上涨。总体而言,我国金融市场规模不断拓展、市场功能明显提升、市场结构持续优化、金融创新稳步推进,市场制度建设日益完善,对外开放程度逐步加深,多层次金融市场建设取得新进展,在我国金融体系和经济发展中的地位和作用进一步提高。     

原油价格与经济政策不确定性对大宗商品市场非对称冲击效应研究

利用面板分位回归模型,考量不同市场环境下原油价格与经济政策不确定性对大宗商品市场非对称性冲击效应。结果表明：油价冲击对中国大宗商品收益的影响具有非对称性,正负油价冲击对其均有促进作用,但随着市场环境好转,正油价冲击的作用逐渐增强,负油价冲击则逐渐减弱;政策不确定性对大宗商品收益有促进作用,但在牛市环境下有抑制作用;且危机前后,油价冲击对大宗商品收益的影响存在非对称性效应。     

Do commodity markets catch a cold from stock markets? Modelling uncertainty spillovers using Google search trends and wavelet coherence

We investigate stock market uncertainty spillovers to commodity markets using wavelet coherence and a general stock market-related Google search trends (GST)-based index to proxy for uncertainty. GST reflect stock market uncertainty over short-, medium- and long-term horizons. Periods of association between GST and the VIX, a widely used proxy for stock market uncertainty, coincide with economic, financial, and geopolitical events. The association between the VIX and GST has grown over time. In line with economic psychology, this implies that during times of heightened uncertainty investors increasingly search for stock market-related information. Our analysis further reveals that some commodities are more susceptible to uncertainty spillovers from stock markets, notably energy commodities. We demonstrate how GST may be used to isolate the impact of specific events and show that COVID-19 had a disproportionate impact on commodity price volatility. We also find that energy, livestock and precious metals are increasingly integrated with stock markets. Spillover analysis repeated using the VIX produces similar results and reflects information that is also reflected in GST, confirming an uncertainty narrative. The use of wavelet analysis and GST to proxy for general and event specific uncertainty offers an alternative perspective to traditional econometric approaches and may be of interest to econometricians, analysts, investors and researchers.     

The Relationship Between Energy Commodity Prices and Electricity and Market Index Performances: Evidence from an Emerging Market

We investigate the effect of energy commodity price movements on market and electricity index returns in Turkey for the periods before, during, and after the year 2008. Although the Turkish economy is highly reliant on oil, we find that oil price does not lead either electricity or market indexes. This might be attributable to sluggish integration of financial markets in Turkey compared to developed markets. Natural gas price leads electricity index in the pre-2008 period. Its significance is reduced following the decline in natural gas usage in electricity production. This suggests that commodity dependence may be driving the link between commodity and asset prices in related sectors.     

Multiscale information transmission between commodity markets: An EMD-Based transfer entropy network

In the context of the growing financialisation of commodity markets, debate on how they interact with each other has returned to centre stage. The main motivation of this study is to research the price interactions of international commodities from the perspective of information transmission by proposing an innovative transfer entropy network based on empirical mode decomposition. We also identify core commodities with the strongest transmission intensity in information transmission networks at different time scales. The empirical results demonstrate that the network transmission structure and core varieties change based on the time scale. In the short term, metals have the strongest transmission intensity, whereas, in the medium and long term, the energy sector has the strongest transmission intensity. These findings should allow regulators and market participants to better understand the essential characteristics and internal structures of international commodity markets.     

Financialization in commodity markets: A passing trend or the new normal?

In this paper, we show that large inflows into commodity investments, a recent phenomenon known as financialization, has changed the behavior and dependence structure between commodities and the general stock market. The common perception is that the increase in comovements is the result of distressed investors selling both assets during the 2007–2009 financial crisis. We show that financial distress alone cannot explain the size and persistence of comovements. Instead, we argue that commodities have become an investment style for institutional investors. Given that institutional investors continue to target funds into commodities, we predict spillovers between commodities and the stock market to remain high in the future.     

An analysis of time-varying commodity market price discovery

We propose a model of time-varying price discovery based on a rolling-window error correction framework. We show that price discovery in nine commodities is dominated by the spot market, while, in only six commodities, price discovery is dominated by the futures market. Our findings, therefore, challenge the well-established view in commodity markets that it is the futures market which dominates the price discovery process. We also show the economic significance of price discovery through a portfolio construction and hedging strategy.     

Systemic risk of commodity markets: A dynamic factor copula approach

This study aims to measure the systemic risk of commodity markets and investigate its causal relationship with the macroeconomy. First, we propose a novel measure called the joint probability of abnormal changes (JPAC) to measure the systemic risk of commodity markets. Second, we introduce two new measures, the expected proportion of commodities in abnormal price change (EPAPC) and the contribution to systemic risk (CSR), to identify the systemic importance of each commodity. Third, we examine the relationship between JPAC and some key macroeconomic variables using mixed-frequency Granger causality tests. We conduct an empirical study using 24 commodity indices from 2015 to 2021. Our results reveal that: (1) JPAC can well capture the risk events in the real world; (2) the systemic risk of commodity markets has increased significantly since the start of the US-China trade war; (3) EPAPC and CSR indicate that energy commodities have higher systemic importance than others and are most likely to cause market fluctuations; and (4) some causal relationships between the systemic risk of commodity markets and the macroeconomy are identified. Overall, this study improves our understanding of systemic risk in commodity markets and provides important implications for policymakers in China and the US.     

Predictability of structural co-movement in commodity prices: the role of technical indicators

This paper investigates the financialization and structural co-movement of several commodity futures using factor variance decomposition and predictability of technical indicators and macro variables. We find that financialization is still a dominant player in the commodity market and that recent commodity price fluctuations can be significantly and robustly forecasted by technical analyses of commodity index investments. Moreover, the co-movement of commodities is demonstrated by variance decomposition and explained as commodity index investment, which provides evidence of financialization. The overall empirical analysis reveals that technical indicators and macro variables can statistically and economically forecast the indexed investment and off-index trading, respectively, which indicates that they are suitable predictors of the commodity markets.     

Do liquidity or credit effects explain the behavior of the BKBM–LIBOR differential?

This paper examines the evolution of the relationship between the onshore and offshore benchmarks for New Zealand dollar funding during the global financial crisis. In August 2007 the BKBM–LIBOR differential switched from positive to negative and then widened considerably following the collapse of Lehman Brothers in September 2008, before narrowing gradually as the turmoil in financial markets subsided. Our structural regression model and decomposition analyses show that changes in liquidity, proxied by bid/ask spreads, largely explain the changes in the BKBM–LIBOR differential over this period and that credit risk factors only played a minor role. However our analysis also shows that bid/ask spreads in the offshore market price information regarding counterparty credit risk, suggesting that our initial results could understate the role played by credit risk factors.     

An analysis of commodity markets: What gain for investors?

In this paper we study whether the commodity futures market predicts the commodity spot market. Using historical daily data on four commodities—oil, gold, platinum, and silver—we find that they do. We then show how investors can use this information on the futures market to devise trading strategies and make profits. In particular, dynamic trading strategies based on a mean–variance investor framework produce somewhat different results compared with those based on technical trading rules. Dynamic trading strategies suggest that all commodities are profitable and profits are dependent on structural breaks. The most recent global financial crisis marked a period in which commodity profits were the weakest.     

Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models

This paper examines the dynamic relationship between the oil market and stock markets from two perspectives: dependence between the crude oil market (WTI) and stock markets of the US and China, and volatility spillovers between them during 1991–2016. We further analyze structural breaks of market dependences and consider the extent of their influence on such relationships. Our vine-copula results show that the dependences between the three paired markets, WTI-US, WTI-China and US-China, vary dynamically across the six identified structural break periods. In particular, the dependence between WTI-US is stronger and more volatile than that between WTI-China during most of the periods. The dependence between US-China remains at a lower level in the earlier periods, but increases in the final period. Our VAR-BEKK-GARCH results demonstrate distinctive volatility spillovers across these periods, with varying directionality, in response to the structural changes. Overall, our results indicate that the oil market stimulates rapid and continual fluctuations in market dependences, which become manifest most acutely in the aftermath of the Financial Crisis of 2007–08, demonstrating the increasing interdependence between the oil and stock markets. Further, the growing influence of China on the dynamics of these relationships, in the period following the Great Recession, presents evidence that it begins to assume an increasingly important role in global economic recovery.     

Dissecting hedge funds' strategies

This paper dissects the dynamics of the hedge fund industry with four financial markets, including the equity market, commodities, currencies, and debt market by employing a large number of assets from these markets. We employ four main representative hedge fund strategy indices, and a cap-weighted global index to estimate an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model using daily data from April 2003 to May 2021. We break down the performance, riskiness, investing style, volatility, dynamic correlations, and shock transmissions of each hedge fund strategy thoroughly. Further, the impact of commodity futures basis on hedge funds' return is analyzed. Comparing the dynamic correlations during the 2008 global financial crisis (GFC) with COVID-19 pandemic reveals changing patterns in hedge funds' investing styles. There are strong and pervasive shock spillovers from hedge fund industry to other financial markets, especially to futures commodities. An increase in the futures basis of several commodities drives up hedge funds' performance. While hedge fund industry underperforms compared to equity market and commodities, the risk-reward measures show that hedge funds are superior to other markets, and safer than the bond market.     

Small fish in big ponds: Connections of green finance assets to commodity and sectoral stock markets

We analyze return and volatility connectedness of the rising green asset and the well-established US industry stock and commodity markets from September 2010 to July 2021. We find that the time-varying return and volatility connectedness have exhibited serious crisis jumps. Some individual assets of both the green and commodity markets are in connection to the US sectoral stock market returns, and the volatility connections are even more common than the return connections. Furthermore, some financial and economic uncertainty indicators manifest positive impacts from the volatility of some ‘big pond’ markets for e.g. commodities, whereas some others affect the connectedness negatively. Additional analysis of financial and economic uncertainty indicators manifests positive impacts from the volatility of some ‘big pond’ markets, e.g., commodities, while others negatively affect the connectedness.     

Commodity returns co-movements: Fundamentals or “style” effect?

This paper investigates dynamic correlations both across commodities and between commodities and traditional assets, such as equities and government bonds, using the Regime Switching Dynamic Correlation (RSDC) model. There are three major findings. First, results from correlations both across commodities and between them and equities and bonds are in line with the “style” effect theoretical findings. Before the recent financial crisis, while correlations across In-index commodities started to increase from mid-2005, correlations between them and equities and bonds remained at low level. Second, all correlations increased markedly with a regime change which coincides exactly with the demise of the Lehman Brothers on September 15, 2008. We therefore suggest that the low correlation between In-index commodities and equities and bonds detected before the financial crisis should not be interpreted as a weak integration between commodity and financial markets. Integration was actually high, as revealed by the financial crisis, but was masked by the “style” effect. Finally, the new and original finding here is the temporary nature detected of the financial crisis effect on correlations which reverted to their pre-crisis level from April 2013. This highlights the impact of the financial-based factors on commodity price movements.     

Commodity market financialization,herding and signals: An asymmetric GARCH R-vine copula approach

Institutional investors have significantly increased their exposure to commodity futures after 2004 in the process of commodity market financialization, raising questions about the risk-sharing and price-discovery functions of the market. We identify some symptoms of financialization through examining S&P500, JPM bond index, and 18 S&P GSCI excess return indices, employing ARMA-GARCH R-vine copula approach that can flexibly model high-dimensional multivariate asymmetric tail dependence. We discover three trends: an increased resemblance between the news impact curve of stocks and those of commodities; an increased bi-variate stock-commodity tail dependence; and an increased multivariate tail-dependence across all commodities. We also explore the market structural change underlying these symptoms using an augmented news impact curve. We suggest and provide evidence that herding, in addiction to leverage effect, explains the observed symptoms. The findings have profound implications for commercial hedgers and financial traders, and for regulators who are concerned about the functionalities of commodity futures market.     

Computing the market price of volatility risk in the energy commodity markets

In this paper, we demonstrate the need for a negative market price of volatility risk to recover the difference between Black–Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654]/Black [Black, F., 1976. Studies of stock price volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177–181] implied volatility and realized-term volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10 year period, we estimate the volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher volatility risk premiums. Computing such a negative market price of volatility risk highlights the importance of volatility risk in understanding priced volatility in these financial markets.     

Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method

This paper analyzes dynamic volatility spillovers between four major energy commodities (i.e., crude oil, gasoline, heating oil and natural gas) in the oil-natural gas future markets. We construct a time-varying spillover method by combining the TVP-VAR-SV model and the spillover method of Diebold and Yilmaz (2009, 2012, 2014). We use the spillover method to obtain time-varying total, directional and pairwise volatility spillover indices. Our results summarize as follows: (1) The volatility spillover indices present peaks and troughs during some periods, such as shale gas revolution, financial crisis, and oil price crash; (2) After the U.S. shale gas revolution, the size of volatility spillover from natural gas future market has reduced sharply, but volatility doesn't decouple from the other three oil future markets; (3) The directional spillover is asymmetric. The crude oil and heating oil futures market are main net transmitter of volatility risk information, while the gasoline and natural gas futures markets are the net receiver; (4) For natural gas future market, the pairwise volatility spillover from crude oil future market has the most significant influence.