Crude Oil and Agricultural Futures: An Analysis of Correlation Dynamics

Silvennoinen, Annastiina; Thorp, Susan

doi:10.1002/fut.21770

Cited by 56 publications

(14 citation statements)

References 68 publications

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“…The results suggest that the common stochastic volatilities increase to high levels when the agricultural commodity futures markets are in high‐volatility periods due to exogenous shocks. These findings are consistent with those in Silvennoinen and Thorp () and Luo, Chen, and Zhang (), who suggest the spillover effects of financial futures markets strengthen during the turbulent periods. The results also reveal that the incorporation of common stochastic volatility accommodates the large shocks and captures the spillover effects among the agricultural commodity futures.…”

Section: Full‐sample Results and Evaluationssupporting

confidence: 92%

Multivariate realized volatility forecasts of agricultural commodity futures

Luo

Chen

2019

Journal of Futures Markets

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We forecast the multivariate realized volatility of agricultural commodity futures by constructing multivariate heterogeneous autoregressive (MHAR) models with flexible heteroscedastic error structures that allow for non‐Gaussian distribution, stochastic volatility, and heteroscedastic and serial dependence. We evaluate the forecast performances of various models based on both statistical and economic criteria. The in‐sample and out‐of‐sample results suggest that the proposed MHAR models allowing for flexible heteroscedastic covariance structures outperform the benchmark MHAR models. In addition, the proposed Bayesian MHAR models allowing for t innovations improve both in‐sample and out‐of‐sample forecast performance of the corresponding MHAR models with Gaussian innovations.

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Section: Full‐sample Results and Evaluationssupporting

confidence: 92%

Multivariate realized volatility forecasts of agricultural commodity futures

Luo

Chen

2019

Journal of Futures Markets

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“…The findings that there is a strong co-movement between sugar and oil are also supported by Serra (2011). Also, Silvennoinen and Thorp (2016) found positive correlation between crude oil, grains, oilseeds and sugar, which is largely consistent with the integration between oil and biofuel feedstocks. Hence, it might be beneficial for a trader to prefer trading sugar over oil, depending on the specific market situation.…”

Section: Introductionsupporting

confidence: 66%

Sugar Futures as an Investment Alternative During Market Turmoil: Case Study of 2008 and 2020 Market Drop

Babirath¹,

Schmitl²,

Sahatqija

et al. 2020

Sugar Tech

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In times of turbulent financial markets, investors all around the globe seek for opportunities protecting their portfolios from devastating losses. Historically, commodities were regarded as a safe haven providing sound returns which offset potential losses arising from dropping equity prices in times of market turmoil. While sugar would have provided a proper hedge against crashing equity markets during the initiation of the 2007 bear market and the onset financial crisis, sugar prices dropped likewise equity during the outbreak of COVID-19 and the consequent market shock. The goal of the paper is to elaborate on the differences in sugar price dynamics during the aforementioned economic disruptions by employing a multiple linear regression approach using data from the last quarter 2007 as well as the first quarter of 2019. The findings suggest that the behavioral differences stem from the deep link between oil and sugar prices. While oil did not influence the price of sugar during the outbreak of the financial crisis, it had tremendous influence on sugar prices during the outbreak of the corona crisis. Currently, sugar provides a substantial upside for an investor's portfolio since the demand and supply-side shock on oil prices due to corona crisis as well as the Saudi-Russian oil price war drove oil prices and consequently sugar prices to a historic low. Sugar futures provide the advantage of offering a smaller contract size compared to oil futures, and even though both commodities trade in contango as of March 2020, the sugar future curve is by far not as steep as the oils. Resultingly, investors benefit from lower rollover costs while prospering from a potential surge in oil prices.

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“…• Volatility Index -VIX: Proved signifi cance of the volatility index related to possibly the most important US stock market index S & P 500 is connected with the investors' sentiment. Signifi cance of this factor is supported by the hypothesis of a closer integration of the analysed commodity index with the stock market during the period of fi nancialization of commodity markets (Tang, Xiong, 2010;Silvennoinen, Thorp, 2012) • Purchasing Managers Index: Its importance (or relatively high correlation) accents expectations of the professionals in the corporate sector regarding future economic development (which is quite diff erent from the volatility index representing subjective opinions of speculators). When using the analysed macroeconomic variables, the model could not explain sharp changes in the index in 2008, as well as minor changes in 2002 and 2005.…”

Section: Discussionmentioning

confidence: 97%

“…Stronger investor interest in commodities may create closer integration with conventional asset markets. Higher VIX may increase commodity returns correlation with equity returns (Silvennoinen, Thorp, 2012).…”

Section: • Infl Ationmentioning

confidence: 99%

How Macroecomic Factors Influence the Commodity Market in the Financialization Period: The Case of S & P GSCI Commodity Index

Smolík¹,

Karas²,

Rejnuš³

2015

Acta Univ. Agric. Silvic. Mendelianae Brun.

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In connection to the process of financialization of commodity markets which is caused by the sharp increase of money flowing into the commodity markets, the question of which factors affect commodity and commodity indices prices is discussed. In this article, the importance of chosen macroeconomic determinants to the price variability of one of the most important commodity indexes S & P GSCI by using the Boosted Trees method is quantified. The results obtained in the research show that changes in the monthly values of macroeconomic determinants reflect and can, according to the model used, explain the volatility of the monthly average index S & P GSCI Total Return to more than 75%. The most important macroeconomic determinants proved to be Nominal Effective Exchange Rate of USD or US – Short-term interest rates.

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Crude Oil and Agricultural Futures: An Analysis of Correlation Dynamics

Cited by 56 publications

References 68 publications

Multivariate realized volatility forecasts of agricultural commodity futures

Multivariate realized volatility forecasts of agricultural commodity futures

Sugar Futures as an Investment Alternative During Market Turmoil: Case Study of 2008 and 2020 Market Drop

How Macroecomic Factors Influence the Commodity Market in the Financialization Period: The Case of S & P GSCI Commodity Index

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