Modeling Price Volatility Linkages between Corn and Wheat: A Multivariate GARCH Estimation

The present paper attempts an empirical investigation on price volatility linkages between two important agricultural commodities: corn and wheat, by using a multivariate GARCH-BEKK model. Evidence of bidirectional linkages were found between corn and wheat in terms of returns and volatility. Multivariate conditional Student’s-t distribution results show a unidirectional volatility transmission from corn to wheat. Diagnostic tests reveal that the Student’s-t distribution will better model the volatility of returns when compared to the Gaussian distribution. Overall, the results show that the conditional variances and covariances between agricultural commodity market returns exhibit significant changes over time.     

Twenty years of jumps in commodity markets

In this article, we provide statistical evidence around jumps affecting commodity returns. Using nearly 20 years of daily data, we use Laurent, Lecourt, and Palm's (2011) methodology to jump extraction, and discuss various aspects of the estimated jump activity. On average across various commodity markets, we find a high number of days for which returns exhibit the presence of jumps, consistently with the intuition that commodities are affected by large price fluctuations. We emphasize that the post-jump average return depends on the commodity sector considered (e.g. agriculture, energy, or metals). We also show evidence of a jump-to-volatility channel for commodities (similar to the effect usually found for equities). Finally, we diagnose around 40 dates during which commodity indices, stocks, bonds and currencies `co-jump’, revealing a tail dependence between standard and alternative assets.     

How did the demise of international commodity agreements affect volatility of primary commodity prices?

This article has used the T-GARCH and E-GARCH models to answer the following two questions regarding the pattern of volatility of commodity prices following the demise of international commodity agreements (hereafter ICAs) in five key commodities: firstly, the question of whether the collapse or suspension of ICAs contributed to asymmetry in price volatility and secondly, the question of whether the collapse or suspension of ICAs led to an increase in persistence in price volatility. The results show that breakdown of ICAs was generally followed by higher degree asymmetry in price volatility but lower persistence in volatility.     

Conditional price volatility,speculation, and excessive speculation in commodity markets: sheep or shepherd behaviour?

The present study aims to investigate the dynamics of primary commodity spot prices and the role of speculation for the period 1995–2012. Using a linear and nonlinear Granger causality analysis, the relationship between speculation and GARCH conditional price volatility on the one side, and the linkage between excessive speculation and GARCH conditional price volatility on the other side, is carefully examined with the scope to establish whether volatility drives speculation or speculation drives price volatility, or whether there are no linkages between the two variables. The results show that excessive speculation leads conditional price volatility, and that bilateral relationships often exist between price volatility and speculation. In addition, the lead-lag relationships are not found for the entire sample period, but rather when small sub-periods are taken into account. It turns out, in fact, that excessive speculation has driven price volatility for maize, rice, soybeans, and wheat in particular time frames, but the relationships are not always overlapping for all considered commodities. Generally, the results under linear causality tests are in agreement with those obtained under nonlinear counterparts.     

Price Discovery and Efficiency of Indian Agricultural Commodity Futures Market: An Empirical Investigation

This study examines the price discovery process and relative efficiency of ten most liquid agricultural commodities’ futures contracts, traded on the largest agricultural commodity exchange of India (National Commodity and Derivative Exchange Limited). Three different common factor methodologies—component share method (Gonzalo and Granger in J Bus Econ Stat 13:27–35, 1995), information share method (Hasbrouck in J Financ 50:1175–1199, 1995), and modified information share method (Lien and Shrestha in J Futures Mark 29:377–395, 2009)—have been employed to determine the extent of price discovery contribution by spot and futures markets. The sample consists of daily data for the period from January 1, 2009 to October 20, 2015. Stationarity and Cointegration test results reveal that spot and futures prices are integrated and cointegrated for all commodities. The price discovery results show that the futures market leads the spot market in case of six commodities, i.e., castor seed, coriander, cottonseed oilcake, soy oil, sugarM and turmeric. Whereas, in the case of four commodities (chana (chickpea), guar seed, jeera, and mustard seed), price discovery takes place in the spot market. Therefore, it could be inferred that futures market is more efficient in price discovery of agricultural commodities. Policymakers could use these results to design futures contracts on other commodities or to plan concrete policies to curb speculation without hampering the efficiency of the agricultural commodity derivatives market.

     

A frequency domain causality investigation between futures and spot prices of Indian commodity markets

This paper examines the direction, strength and extent of causal relationship between futures and spot prices of Indian commodity markets using frequency domain approach of Breitung and Candelon (2006). Frequency domain analysis offers an effective alternative tool by examining the causality in frequency domain, whereas in traditional econometric causality analysis tools focus only on the time domain. Daily futures and spot price series on eight commodities from the Indian commodity exchanges (MCX and NCDEX) were examined for the period 3rd January, 2008 to 31st December, 2012. The results of frequency domain analysis suggest that there is a strong uni-directional relationship from futures to spot in almost all the selected commodities. This indicates that futures market has a powerful price discovery function in all the selected commodities; which in turn indicates the efficiency of Indian commodity futures market.     

金融资产收益率波动预测——基于我国股票市场跳跃行为研究

金融资产收益率波动是资产定价和金融风险管理的核心部分,而跳跃是收益率波动中的重要组成部分。基于修正Z-检验,本文检测识别我国股市波动中跳跃行为,并且研究了跳跃的时序特征,统计结果表明,在市场大波动时期,和连续成份相比,跳跃对于波动率具有极其重要的贡献。建立包含跳跃的已实现波动率非齐次自回归模型,在波动模型中纳入滞后绝对日收益率和杠杆效应预测股指收益率波动。实证分析结果显示,对于短期的波动预测,包含跳跃和两种影响因素的波动模型表现最好,然而对于提前1月的长期预测,跳跃和连续波动成份分离模型预测明显优于其它模型,这些事实说明跳跃对股指波动率预测具有重要的影响,好坏消息对波动率非对称性具有短期显著影响,而对长期水平的波动率预测影响不显著。     

Correlated jumps in crude oil and gasoline during the Gulf War

This article employs a bivariate poisson jump model to investigate the relationship between the volatility of crude oil and gasoline especially during the period of the Gulf War. We find that greater jumps occurring in crude oil returns will appear in gasoline returns at the same time, but the magnitude of the co-movements in volatility falls. The covariance is relatively smaller in the Second Gulf War vs. the first conflict. The volatility of crude oil is of significantly high levels during periods of the war, yet the volatility of gasoline is not as sensitive as crude oil, particularly in the second conflict. Furthermore, the jump that occurred by the war did not lead both spot prices to a high persistent level for a long period, which fits the feature of the jump models. All these findings are important to market traders and hedging strategies.     

Correlations and volatility spillovers across commodity and stock markets: Linking energies,food, and gold

This paper employs a VAR-GARCH model to investigate the return links and volatility transmission between the S&P 500 and commodity price indices for energy, food, gold and beverages over the turbulent period from 2000 to 2011. Understanding the price behavior of commodity prices and the volatility transmission mechanism between these markets and the stock exchanges are crucial for each participant, including governments, traders, portfolio managers, consumers, and producers. For return and volatility spillover, the results show significant transmission among the S&P 500 and commodity markets. The past shocks and volatility of the S&P 500 strongly influenced the oil and gold markets. This study finds that the highest conditional correlations are between the S&P 500 and gold index and the S&P 500 and WTI index. We also analyze the optimal weights and hedge ratios for commodities/S&P 500 portfolio holdings using the estimates for each index. Overall, our findings illustrate several important implications for portfolio hedgers for making optimal portfolio allocations, engaging in risk management and forecasting future volatility in equity and commodity markets.     

Price volatility spillovers between supply chain and innovation of financial pledges in China

This study investigates the price volatility spillover of commodities in China using the Granger causality test and the variable structure Copula model, by employing data covering the daily average transaction prices of silver, copper, aluminum, rebar, and fuel oil from 2009 to 2017. The results show that the causal direction of price volatility spillover is uncertain, and that the impact from the correlation coefficients of copper and aluminum is the highest. Hence, there is a clear long-run price volatility spillover between silver and fuel oil as well as between silver and aluminum. Macroeconomic influences on different combinations of commodities present similar changing structure points, such as the debt crisis in Europe, the reduction of the quantitative easing monetary policy proposed by the U.S. Federal Reserve, and the promotion of RMB internationalization. Overall, our findings provide a theoretical reference for commercial banks to make full use of the volatility spillover effect between commodity pledges and their combinations, in order to achieve the goal of avoiding any price volatility risk.     

Distributional Consequences of Commodity Price Shocks: Australia Over A Century

This paper studies the distributional impact of commodity price shocks over the short and the very long run. Using a GARCH model, we find that Australia experienced more volatility than many commodity exporting developing countries over the periods 1865–1940 and 1960–2008. We conduct cointegration tests to assess the commodity price shock inequality nexus. A single equation error correction model suggests that commodity price shocks increase the income share of the top 1, 0.05, and 0.01 percent in the short run. The very top end of the income distribution benefits from commodity booms disproportionately more than the rest of the society. The short run effect is mainly driven by wool and mining and not agricultural commodities. A sustained increase in the price of renewables (wool) reduces inequality whereas the same for non‐renewable resources (minerals) increases inequality. We expect that the initial distribution of land and mineral resources explains the asymmetric result.     

Commodity market risk from 1995 to 2013: an extreme value theory approach

In this article we examine whether extreme risk has increased in the agricultural commodity market during the period 1995–2013. We add to the literature on food price volatility by analysing the tail segment of futures price return distributions. Food price variability is a concern for governments and regulators worldwide, as most nations trade in food. High food price variability can contribute to poverty and malnourishment, in particular for people in less economically developed economies. We find no indications of systematically increasing tail-risk for the commodities in our sample. Analysis of estimated shape-parameters of the Generalized Extreme Value distribution further supports the conclusion that there is no general systematic change in the extreme risk associated with these commodity investments.     

Futures basis,inventory and commodity price volatility: An empirical analysis

We employ a large dataset of physical inventory data on 21 different commodities for the period 1993–2011 to empirically analyze the behavior of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we analyze the relationship between inventory and the shape of the forward curve. Low (high) inventory is associated with forward curves in backwardation (contango), as the theory of storage predicts. Second, we show that price volatility is a decreasing function of inventory for the majority of commodities in our sample. This effect is more pronounced in backwardated markets. Our findings are robust with respect to alternative inventory measures and over the recent commodity price boom.     

Interdependencies in the energy–bioenergy–food price systems: A cointegration analysis

The present paper studies the interdependencies between the energy, bioenergy and food prices. We develop a vertically integrated multi-input, multi-output market model with two channels of price transmission: a direct biofuel channel and an indirect input channel. We test the theoretical hypothesis by applying time-series analytical mechanisms to nine major traded agricultural commodity prices, including corn, wheat, rice, sugar, soybeans, cotton, banana, sorghum and tea, along with one weighted average world crude oil price. The data consists of 783 weekly observations extending from January 1994 to December 2008. The empirical findings confirm the theoretical hypothesis that the prices for crude oil and agricultural commodities are interdependent including also commodities not directly used in bioenergy production: an increase in oil price by 1 $/barrel increases the agricultural commodity prices between 0.10 $/tonne and 1.80 $/tonne. Contrary to the theoretical predictions, the indirect input channel of price transmission is found to be small and statistically insignificant.     

Forecasting the realized volatility in the Chinese stock market: further evidence

In this study, the impact of noise and jump on the forecasting ability of volatility models with high-frequency data is investigated. A signed jump variation is added as an additional explanatory variable in the volatility equation according to the sign of return. These forecasting performances of models with jumps are compared with those without jumps. Being applied to the Chinese stock market, we find that the jump variation has a significant in-sample predictive power to volatility and the predictive power of the negative one is greater than the positive one. Furthermore, out-of-sample evidence based on the fresh model confidence set (MCS) test indicates that the incorporation of singed jumps in volatility models can significantly improve their forecasting ability. In particular, among the realized variance (RV)-based volatility models and generalized autoregressive conditional heteroscedasticity (GARCH) class models, the heterogeneous autoregressive model of realized volatility (HAR-RV) model with the jump test and a decomposed signed jump variation have better out-of-sample forecasting performance. Finally, the use of the decomposed signed jump variations in predictive regressions can improve the economic value of realized volatility forecasts.     

异质信念、投机均衡与农产品期货定价*

农产品期货市场套利并不充分,交易者也不是完全理性的。本文假设农产品期货市场有限套利、交易者异质信念并遵循“经验法则”预期,构建了农产品期货投机均衡定价模型,并认为集中竞价规则下产生的农产品期货价格是由交易者的预期决定的;前期期货价格水平、现货价格和前期期货价格的变动趋势、不同类型交易者的比例结构及其预期模式共同影响农产品期货价格的形成与波动;基本分析法交易者占主导地位的农产品期货市场具有更高的套期保值与价格发现效率。针对中国七种主要农产品期货的实证结果显示,农产品期货投机均衡定价模型对解释中国农产品期货价格的形成与波动是有效的。这意味着在期货行情系统中实时披露现货价格信息,培育和引导交易者运用基本分析法预测期货价格走势,有助于提升农产品期货市场的效率。     

Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models

We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.     

Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil

This study explores the relationship between Google search activity and the conditional volatility of oil and gold spot market returns. By aggregating the volume of queries related to the two commodity markets in the spirit of Da et al. ( 2015 ), we construct a weekly Searching Volume Index (SVI) for each market as proxy of households and investors information demand. We employ a rolling EGARCH framework to reveal how the significance of information demand has evolved through time. We find that higher information demand increases conditional volatility in gold and oil spot market returns. Information flows from Google SVI's reduce the proportion of the significant volatility asymmetry produced by negative shocks in both commodity markets. The latter is more profound in the gold market.     

商品期货有助于预测通货膨胀率吗

本文通过对我国22种商品期货提取隐含的便利收益率时间序列,发现商品期货在样本内和样本外可以显著预测未来通货膨胀率,在控制了利率、货币增速影响后,这一结果仍然成立。进一步,采用南华商品期货综合指数、商品期货各品种指数、商品期货大类指数,以及美国商品研究局商品期货指数对通货膨胀率进行了样本外预测,结果表明这些指数对于预测我国通货膨胀率效果仍然显著。基于商品期货价格的预测模型都优于本文的基准模型和朗润预测指数,这表明商品期货市场包含了与通货膨胀率相关的重要信息,可以作为未来一般物价走势的重要参考。     

Volatility transmission in agricultural futures markets

After the huge rise and fall of agricultural commodity spot and futures prices between 2007 and 2008, the potential reasons for and the impact of the strong rise in volatility provoked an intensive debate in the media as well as in the academic literature. However, owing to the increasing interdependence of global markets, an isolated examination of single futures markets does not seem to be appropriate. Therefore, the aim of this study is to investigate the volatility spillover between various agricultural futures markets from a new perspective. To do this, we use data for the prices of first nearby futures contracts for corn, cotton, and wheat and estimate GARCH-in-mean VAR models in the tradition of Elder (2003). Our results provide evidence in favor of an existing short-run volatility transmission process in agricultural futures markets.