Search

We use a robust measure of non-linear dependence, the Gerber cross-correlation statistic, to study the cross-dependence between the returns on Bitcoin and a set of commodities, namely wheat, gold, platinum and crude oil WTI. The Gerber statistic enables us to obtain a more robust co-movement measure since it is neither affected by extremely large nor small movements that characterise financial time series; thus, it strips out noise from the data and allows us to capture effective co-movements between series when the movements are “substantial”. Focusing on the period 2014–2022, we construct the bootstrapped confidence intervals for the Gerber statistic and test the null that all the Gerber cross-correlations up to lag kmax are zero. Our results indicate a low degree of dependence between Bitcoin and commodities prices, both when we consider contemporaneous correlation and when we employ correlations between current Bitcoin and lagged (one day, one week, or one month) commodities returns. Further, the cross-correlation between Bitcoin and commodities’ returns, although scanty, shows an increasing trend during periods of economic, health and financial turbulence. This increased cross-correlation of returns during hectic market periods could be due to the contagion effect of some markets by others, which could also explain the strong dependence across volatilities we detected. Based on our results, Bitcoin cannot be considered the “new digital gold”.

Starting from the late 1950s, mainly as a reaction to Leontief’s paradoxical results, a large amount of research has underlined the great importance of technology and human capital in explaining international trade in manufactures. This study examines the patterns of comparative advantages for 42 countries and 91 manufactured classes of final and intermediate products and their changes between 2001 and 2019. The dynamics and the effects of the international fragmentation of production processes are also considered. Comparative advantages in each class of products are related to three different measures of a country’s human capital or technology endowment: the cost of labour, the level of formal education and the number of patents per capita. An indicator of home market size enters the model as a control variable. The econometric analysis reveals that in 2019, human capital or technology endowments explain comparative advantages in 70 out of 91 products. In particular, 31 products are positively and significantly associated with at least 1 of the 3 human capital or technology indicators, 34 products are negatively and significantly linked to at least 1 indicator, 5 products show contrasting results, 21 products are not related to any human capital or technology indicator. Between 2001 and 2019, comparative advantages for 15 classes of products shifted towards lower-technology countries and for 18 productions towards higher-technology countries. There were no shifts for 51 products. From a policy perspective, the distinction between high and low-technology productions could have some interesting implications. Since high-technology productions are characterised by a higher learning-by-doing rate, governments should foster domestic firms to upgrade the human capital or technology intensity of their productions.

This study presents a set of integer-valued generalised autoregressive conditional heteroskedastic models to identify possible transmission channels of joint extreme price moves (coexceedances) across a group of agricultural commodities. These models are very useful to identify factors affecting joint tail events and they are superior in terms of goodness of fit to models without autoregressive components. Emerging market demand, crude oil, exchange rate, stock market conditions and credit spread explain extreme joint returns. Psychological factors and the Monday effect play a role in affecting extreme events, while weather anomalies (El Niño and La Niña episodes) do not have explanatory power.

The present study investigates the relation between different measures of price volatility (conditional, historical and implied) and different types of speculation (short-run, long-run and excessive) in futures commodity markets for the period 2000–2015. To this purpose, we first use a pairwise Granger causality analysis for 28 individual commodities belonging to energy, agricultural and metal markets. Then, we implement a novel combination of combinations of p-values test to assess whether lead-lag relations exist between speculation and price volatility for broad categories of commodities. The results of both testing procedures show that the majority of significant relations refer to agricultural commodities and that tendentially short-run speculation leads volatility. This means that noise trading has a leading power on market volatility and scalpers are a class of volatility-drivers.

The present study empirically investigates market efficiency and the potential factors of inefficiency in the main non-energy commodity futures markets: maize, soybeans and wheat. With efficient markets, futures prices can be used as commodity price forecasts, with inefficient markets futures prices are not able to predict spot prices. Technically, exploring the drivers of inefficiency means to assess the determinants of forecast errors, i.e. the factors affecting the difference between realized spot prices and predicted future spot prices. The analysis is carried out using first a traditional test of efficiency for spot and futures prices and then implementing a more sophisticated GARCH analysis at a daily and weekly frequency. The results show that maize, soybeans and wheat markets are not informationally efficient. The realized price volatility of futures markets, the time to maturity, open interest, trading volumes and speculative measures are significant factors explaining forecast errors. In particular, short-term speculation (scalping activity) increases the noise in the information formation process. Similarly, price volatility, lack of liquidity and long contract maturity horizon raise forecast errors, while long-term speculation and speculative pressure reduce errors. The analysis suggests that knowing the causes of forecast errors, would allow to control for such factors and therefore to ameliorate the forecast accuracy of commodity prices.

This study investigates the main determinants of opera attendance in Italy over the period 2006-2018 using monthly data. To this purpose, a novel Unobserved Component Model that captures tastes and habits, in addition to the traditional demand drivers, is adopted. The results show that opera is a superior good, with an inelastic price and cross-price response. In addition, supply factors and education affect opera participation. Tastes, habits and other latent factors, encapsulated in the unobserved component, play a relevant role in driving opera demand. Strong seasonal effects characterise opera attendance and a declining growth participation over time is recorded.

This study examines the prices of options contingent on electricity futures traded on the European Energy Exchange, with the aim to recover the probability density functions and risk premia. After we extract the risk-neutral probability density functions from prices of such options, we transform the risk-neutral densities into real-world densities using both parametric and non-parametric statistical calibration methods and investigate the evolution of risk premia and pricing kernels. We find that both risk-neutral and real-world option-implied densities accurately forecast realized futures electricity prices. Positively skewed densities suggest that there is an inverse (or positive) leverage effect in the electricity market, meaning that a higher probability of large price increases in electricity has been incorporated in the traded option prices. In addition, we find that the state price densities are mostly increasing, implying that investors are more risk-averse to high electricity prices. Over a period of 15 years, our results provide evidence of negative market price of risk and risk premia in this new market.

This study aims at investigating the impact of cash-flow, the main components of the capital structure, and RD the availability of long-term bank loans, a significant export propensity and a greater firm size facilitate and stimulate firms to introduce new products and production processes. Some policy implications conclude the study.

This study investigates the determinants of competitive advantages in tourism services for the EU-28 countries over the period 2000–2013. After having extended the Balassa methodology to measure competitive advantages, a dynamic panel data model is implemented to explain their drivers. The econometric analysis indicates that specific factor-proportions variables, as well as variables related to the new trade theory, contribute significantly to the explanation of international competitive advantages in tourism. At the same time, factors related to the new trade theory help strengthening these advantages. Particularly interesting seems to be our finding about the negative statistical association between competitive advantages in tourism and the overall efficiency of the country, measured by per capita GDP. Some policy and managerial implications conclude the work.

This study assesses the degree of vertical price transmission along the wheat-bread value chain in Ethiopia. This is pursued by applying a vector error correction model and an impulse response analysis using monthly price data for the period 2000–2015. Our analysis considers transmission of price shocks across different market levels, including from the international and domestic wheat grain markets at the upstream to the domestic wheat bread market at the downstream of the value chain. The empirical findings indicate that significant cointegration exists across prices of the different market stages. There is a transmission from international prices to domestic prices at downstream markets, in particular to flour and bread prices. Prices at upstream markets are largely influenced by the domestic wholesale market. In general, the speed of adjustment is quite slow with a half-life of about one year for restoring the equilibrium price relationship. As price margins between the different market stages in the value chain have substantially decreased in the last 15 years, higher transmission, and thus exposure to international market shocks, can be expected in the future. The results also show that causal relationships exist between prices at different market stages—with the wholesale market identified as the key market level where prices and price expectations are formed.

The substantial booms and busts in agricultural prices marked by extreme events across commodities lead to heated debates about the effects of speculative trading on commodity price fluctuations. This study proposes a new approach to understanding extreme events and boom-bust processes in agricultural markets. Using weekly futures data for twelve indexed agricultural commodities during 2006 to 2016, we find that extreme price changes, located in the 10% tails of the distribution, cluster across agricultural markets. We then implement a multinomial logit model to investigate which factors are associated with the propagation of extreme events. Specifically, we disentangle three transmission conduits. (1) The macroeconomic conduit captures the possibility that the synchronized extreme price events are generated by business-cycle driven demand shifts mainly in emerging economies. (2) The financial conduit refers to potential links between extreme returns and the increasing flow of money from financial participants into agricultural futures markets. (3) Finally, the energy conduit accounts for possible spillover effects due to oil price shocks. Our results indicate an important role of managed money positions and oil prices while the real demand channel remains mostly insignificant. (C) 2017 Elsevier Ltd. All rights reserved.

The present study examines the drivers of Italian exports via an export equation with regional and time-varying impacts of local financial development. To this purpose, two-way fixed effects regression models with lagged variables and a system Generalized-Methods-of-Moments have been adopted to account for potential endogeneity problems and dynamic trade patterns. The analysis covers the period 2000–2013 and the sub-period 2000–2007. The results show that a mix of factors contributing to lift exports, including financial development, exerts a positive impact on trade flows. In particular, a rise in credit intensity and a reduction in financial risk push export propensity. The results further point to the relevant effect of non-price competitiveness factors, namely R&D and investments, in influencing the export behaviour of the Italian regions. The results hold for both the whole and the pre-crisis period, but the effects are generally stronger during the pre-crisis years.

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 parti...

The present study examines the dynamics and regulatory regimes of commodity derivatives markets through time. The historical perspective allows to identify the reasons behind the use of derivatives and the impact of changing rules on financial systems. It further permits to highlight the weaknesses and the strengths of derivatives markets and provides valuable lessons to tackle challenges, replicate practices, and prevent failures. The analysis shows that derivatives markets have a long history and have facilitated trading across time and geographical areas. The results of a quasi-experiment conducted for Japan and the US reveal that commodity price fluctuations were higher before the establishment of futures markets. The analysis further indicates that the unprecedented inflow of liquidity in derivatives markets was mainly facilitated by the deregulation policies adopted in the US, EU and elsewhere and was intensified by an increasing interest of investors in alternative asset classes. In the new millennium many product innovations flooded the market, reducing transparency and increasing market uncertainty. The study indicates that improved data quality and quantity are necessary conditions to enhance the understanding of derivatives markets. In addition, a sound legal and financial system is a must for thriving financial markets. Such a system creates a framework of checks and balances for the market, it contributes significantly to meaningful regulations and vibrant policies and helps to prevent or eradicate market manipulations.

The objective of this study is to explore empirical evidence on the quantitative importance of supply, demand, and market shocks for price changes in international food commodity markets. To this end, it distinguishes between root, conditional, and internal drivers of price changes using three empirical models: (1) a price spike model where monthly food price returns (spikes) are estimated against oil prices, supply and demand shocks, and excessive speculative activity; (2) a volatility model where annualized monthly variability of food prices is estimated against the same set of variables plus a financial crises index; and (3) a trigger model that estimates extreme values of price spikes and volatility using quantile regressions. The results point to the increasing linkages among food, energy, and financial markets, which explain much of the observed food price spikes and volatility. While financial speculation amplifies short-term price spikes, oil price volatility intensifies medium-term price volatility.

Over the last decade, commodity prices have registered substantial booms and busts marked by extreme volatility. Wheat in particular, one of the main nonoil commodities, has registered a roller coaster in price levels which seems to be inconsistent with supply and demand fundamentals. To acutely investigate the drivers of wheat prices and quantify their impact, a vector error correction model (VECM) has been used. The exogenous variables have been distinguished into four groups: market-specific factors, broad macroeconomic determinants, speculative components, and weather variables. The quadriangulation of the determinants will enable us to better understand the movements in wheat price and identify the specific role of each component. The results show that a mix of factors are contributing to wheat price movements, including speculation, global demand, and real effective exchange rate.

This study investigates the drivers of export demand in the peripheral economies of the Euro Area, namely Greece, Ireland, Italy, Portugal, and Spain (GIIPS), for the period between 1980 and 2012. Recently, several authors have pointed out that changes in trade export shares are not associated with major terms of trade disturbances; rather, they are the result of other underlying factors commonly defined as ‘non-price competitiveness’. Starting from this premise, the study extends the traditional imperfect substitute trade model to include a measure of non-price competitiveness: real capital stock. The latter is a measure of a country's total resource base and captures the presence of product differentiation and product innovation. The results show a significant link between export demand and cumulative investments. In the short-term, GIIPS exports are dominated by the movements of worldwide real income, while changes in price and non-competitiveness take longer to affect export performance. In the long-run, all three variables play a significant role in pushing exports.

We use a set of conditional auto-regressive logit (CARL) models to predict tail probabilities for returns calculated from futures of four energy commodities. We show that CARL models are very useful to forecast the probability of tail events in energy markets and the forecasting ability of the models generally increases when commodity implied volatility is added as a predictor. We further present new bivariate models to jointly forecast the probabilities that returns from a given commodity and from the S&P 500 index are on the left tail and models for the coexceedances. We find that CARL family models have always a better forecasting performance than GARCH and Quantile-Augmented Volatility models in a univariate and multivariate setting. Conversely, when modelling coexceedances, CARL models exhibit a better predictive capacity only for Brent and heating oil.

This article examines the main determinants of the Russian real effective exchange rate (REX) movements over the transition period started in the early 1990s. To understand the forces that drive exchange rate dynamics, five strands of the empirical literature have been combined ina time series dimension. The results suggest a positive long-run cointegration relationship between the REX, oil price, productivity and government financial position and a negative relation with international reserves. Managing international reserves and fiscal policies have therefore, the effect of mitigating the impact of oil/terms of trade and productivity shocks on the REX.

The present study investigates the causality relationship between the external (trade and current account) balance and government budget balance for five countries of the euro area's Mezzogiorno, namely Greece, Ireland, Italy, Portugal, and Spain. These countries, due to their weak economic and financial performances, have been labelled the GIIPS group. The analysis is implemented using two methodologies: the traditional Granger test and the approach developed by Toda–Yamamoto. The results reveal homogeneity in using both approaches and give support to the Ricardian theory, according to which there is no clear nexus between budget-current account balances and budget-trade balances. This implies that fiscal austerity could help the five peripheral countries to conform to the budget deficit criteria as established by the Stability and Growth Pact, but would not be effective in restraining external deficits.

This study examines the key drivers of real house prices in the five main Euro area countries (Germany, France, Italy, Spain, and the Netherlands) and the Anglo–Saxon economies (the United Kingdom and the United States) from 1970 to 2010. Estimating the determinants of house prices is very important, as they significantly influence economic activity and financial stability. Therefore, a multivariate unobserved component model, introduced by Harvey, has been used to model house price fluctuations. This technique allows us to catch those price movements that are not fully explained by economic fundamentals. The empirical results, in fact, indicate that in addition to changes in real income, long-run interest rates, stock prices and inflation, the latent component has a significant role in explaining real house prices. The latent component reflects those factors that are not specifically observed, such as structural changes in markets and changing preferences.

The present study provides an analysis of the Italian photovoltaic sector (PV). To this purpose, we first evaluate the main characteristics of the Italian PV incentive scheme by focusing on its costs and benefits. The analysis then turns to the evolution of PV demand over recent years, the key features of the Italian PV supply and its performances by identifying the strengths, limitations and growth prospects of the sector. The investigation relies on accounting data of the Italian producers collected from the Bureau van Dijk's Amadeus database, over the 2000- 2010 years. Some industrial policy implications conclude the work.

Over the past decades, university-industry relationships have become an important subject due to the essential role played by technological progress in the economic development of countries. From a theoretical point of view, several studies have shown the close relationship between investments in research and innovative activities of universities and the economic growth of specific territories. Indeed, the strong linkages between universities and a country’s production system encourage the process of technology transfer and the commercial use of the research results. For this reason, the European Union has implemented a series of measures to promote the adoption of research findings in the real economic and social context, strengthening the linkages between universities, industries and government. As a starting point for enhancing this link, specific mechanisms have been devised by universities. In particular, technology transfer offices (TTOs) have been created to stimulate and encourage the dissemination of the research outcomes, translate them into practise, and facilitate their interrelations with the other two agents of the innovation systems: industries and government. Within this context, the present paper aims to gain knowledge on the determinants of spin-off creation in Italy with special attention to the role played by university TTOs. Specifically, an econometric probability model has been built merging the extant literature into four distinct strands. The analysis, based on the NetVal indicators and primary data survey, has allowed us to assess the Italian experience at an aggregate and disaggregate level.

The present study aims at providing a comprehensive analysis of trade flows and the domestic value creation of the major solar photovoltaic industry at the world level. Solar technologies convert light and heat from the sun into useful energy. The use of the sun's energy can not only reduce the consumption of conventional fuels, thus reducing the emission of detrimental greenhouse gases, but it can also enable a gain in enhanced fuel and energy security along with lessening costs. In addition, green technologies and industries can promote economic growth and international competitiveness, and can offer new business and employment opportunities. It becomes, therefore, extremely important to deeply explore the dynamics of the solar photovoltaic sector. Specifically, the present work analyses the main global trends of this sector and sketches the key players on the world market, including producers, installers, and top traders. Based on an analysis of trade flows at the 6-digit level, the international specialisation patterns are investigated, and the role of various market and trade drivers, including subsidies in the uptake of solar technologies, is identified and examined.

The present study examines whether the Russian economy exhibits the symptoms of the Dutch Disease over the transition period begun in the early 1990s. Five warning signs have been detected, namely, a real exchange rate appreciation (1); a flourishing economic situation pushed by higher oil prices (2); a relative de-industrialisation (3); an export reduction in the non-booming-sector (4) and a real wage growth (5). The first three symptoms are estimated simultaneously in a VECM dimension. The results suggest the existence of three long-run cointegrating vectors, thus confirming the presence of the first three symptoms. Specifically, a 10% oil price shock leads to a real appreciation by 4%, a rise in GDP by 3% and a decline in domestic manufacturing production vis-a-vis service production by another 3%. Finally, a number of manufacturing exports have been crowded out and real wages have recorded important increases. To a certain extent, this corroborates the presence of symptom 4 and 5. The paper concludes that the risk of the Dutch Disease exists, and two preventive thrusts of action could be undertaken to reduce its threat: namely to diversify the economy and to hold back the appreciation of the exchange rate through targeted fiscal and monetary policies. These instruments would render Russia less vulnerable to exogenous shocks.

This article analyses developments in the external sector for the Euro Area and its major competitors and quantifies the dynamic contributions of the key determinants of trade to export volume behaviour. In addition to the traditional variables affecting export volumes, price and foreign demand, an unobserved component in the form of a time-varying trend enters the export equations to capture underlying non-price competitiveness. The structural modelling approach used within an error correction framework allows isolating the different sources of trade fluctuations and to better assess the contribution of each set of variables to export flows. The findings confirm that stochastic trends are present as a result of technical change and other exogenous factors driving export flows, and that a failure to account for these trends will lead to biased estimates of long-run price elasticities.

Theoretical models on trade balance adjustment make a distinction between adjustment led by relative quantities (expenditure reduction) and adjustment led by relative prices (expenditure switching). Using cluster analysis on a set of over 70 current account adjustment episodes, we confirm the empirical relevance of this theoretical distinction, as quantity and price-driven adjustment cases can be distinguished in a statistically meaningful sense. We also identify a group of mixed cases, where both quantities and prices played a significant role in adjustment. Multinomial logit results suggest that economic fundamentals and business cycle positions prior to the adjustment have predictive power over the type of adjustment, i.e. whether the adjustment is quantity– or price-driven. The exchange rate regime and the level of economic development (emerging market versus advanced status) do not have significant predictive power.

The present paper analyses the dynamic behaviours of the main determinants of tourism demand for France, Greece, Spain and Australia. In addition to the traditional variables affecting the demand for exports of tourism, namely price and foreign income, an unobserved component in the form of time varying trend enters the tourism equations to capture underlying competitiveness. The structural modelling approach within an error correction framework allows us to isolate the different sources of tourism fluctuations and to better assess the contribution of each set of variables to export flows. The findings confirm that stochastic trends are present as result of consumer tastes, technical change and other exogenous factors driving tourism flows, and that a failure to account for these trends will lead to biased estimates of long-run price and income elasticities.

This paper considers trade specialisation in Russia, examining changes in trade patterns at the sectoral level over the transition period. Trade based on inter-industry specialisation and intra-industry trade (IIT) are empirically distinguished using the Aquino and Grubel–Lloyd (GL) indices. The Aquino index is applied to measure the degree of inter-industry specialisation by sector, while the GL index is used to establish the level of IIT between industries. The empirical results support recent trade theory, which predicts an increasing level of IIT with liberalisation processes. They also suggest how inter- and intra-industry trade coexist. The econometric estimation of the factor content of Russia's exports (specialisation in resource-intensive products) supports the index analysis. Comparative Economic Studies (2007) 49, 232–258. doi:10.1057/palgrave.ces.8100199

The substantial booms and busts in agricultural prices marked by extreme events across commodities lead to heated debates about the effects of speculative trading on commodity price fluctuations. This study proposes a new approach to understanding extreme events and boom-bust processes in agricultural markets. Using weekly futures data for seven indexed agricultural commodities during 2006 to 2014, we find that extreme price changes, located in the 10% tails of the distribution, cluster across agricultural markets. We then implement a multinomial logit model to investigate which factors are associated with the propagation of extreme events. Specifically, we disentangle three transmission conduits. (1) The macroeconomic conduit captures the possibility that the synchronized extreme price events are triggered by business-cycle driven demand shifts mainly in emerging economies. (2) The financial conduit refers to potential links between extreme returns and the increasing flow of money from financial participants into agricultural futures markets. (3) Finally, the energy conduit accounts for possible spillover effects due to oil price shocks and the demand for biofuels. Our results indicate an important role of managed money positions and ethanol prices while the real demand channel remains mostly insignificant.

This paper examines the determinants of tourism revenues in Russia and gauges their impact on tourism demand over the period 1993:12 to 2002:10. The demand for tourism and the choice of tourist destinations are subject to significant swings for many reasons, including variations in income, exchange rates and prices, as well as unpredicted events such as relevant political changes. Therefore, reliable estimates of the elasticities of tourism demand become important for the formulation of efficient tourism policies. In this context a cointegration analysis of the demand for tourism in Russia within a VAR framework was carried out. The results suggest a robust and significant long-run cointegration relationship between Russian tourism receipts, world GDP, real exchange rates and air transport prices. In particular, the estimated income, cost of living and airfares elasticities are statistically significant and have the expected signs, and their values are in accordance with the empirical literature.

International tourism has expanded enormously over recent decades, fuelled by changing consumer tastes, advances in transport and new holiday destinations. The present study aims at analysing the linkages between economic growth and tourism-based economies. An econometric model for a selected number of small countries has been implemented to investigate the nature, magnitude and overall significance of the demand for tourism. Countries were selected to capture regional diversity, differences in market orientation and a range of experiences, from emerging to long-standing industries. The results show that tourism can be a significant engine of economic growth, when the elasticity of substitution between manufacturing goods and tourism services is < 1. Finally two stylised facts emerged, namely: (i) countries specialised in tourism register good economic performances; (ii) these same countries have small dimensions as defined by international trade theory. Copyright © 2006 John Wiley & Sons, Ltd.

The role of futures markets in stabilizing spot prices has been extensively discussed. Nevertheless, the ability of these markets to achieve the stabilizing function significantly depends on whether they are ‘efficient’ in the sense that futures prices ‘fully reflect’ the available information. The purpose of this study is first to gauge the extent to which futures markets for a set of traded commodities can be considered efficient in predicting spot prices. We then go beyond traditional analyses of efficiency and assess the relative forecasting performance of futures markets; i.e., the difference between the realization and prediction of future spot prices, and what factors affect these forecast errors. The results of the analysis show that maize, soybeans, and wheat markets are not informationally efficient, so that investors can make outsize profits. We find that short-term speculation, measured by the scalping index, increases the noises in the information formation process, thus increasing forecast errors. Conversely, long-term speculation, proxied by the Working-T index and the speculative pressure index, reduces forecast errors although their quantitative effect is negligible. Other relevant factors that drive forecast errors up are a high level of realized price volatility, the lack of liquidity in the market, and a longer contract maturity horizon.

Biofuels production has experienced rapid growth worldwide as one of several strategies to promote green energy economies. Indeed, climate change mitigation and energy security have been frequent rationales behind biofuel policies, but biofuels production could generate negative impacts, such as additional demand for feedstocks, and therefore for land on which to grow them, with a consequent increase in food commodity price. In this context, this paper examines the effect of biofuels and other economic and financial factors on daily returns of a group of commodity futures prices using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models in univariate and multivariate settings. The results show that a complex of drivers are relevant in explaining commodity futures returns; more precisely, the Standard and Poor’s (S&P) 500 positively affects commodity markets, while the US/Euro exchange rate brings about a decline in commodity returns. It turns out, in addition, that energy market returns are significant in explaining commodity returns on a daily basis, while monetary liquidity does not. Finally, the GARCH model has shown that current variance is influenced more by its past values than by the previous day’s shocks, and there is high persistence, meaning that variance slowly decays and prompts a sluggish “revert to the mean.” The multivariate BEKK framework confirms the results of the univariate setting.

Over the last decade, commodity prices have registered substantial booms and busts marked by extreme volatility. Wheat in particular, one of the main non-oil commodities, has registered a roller-coaster in price levels which seems to be inconsistent with supply and demand fundamentals. To acutely investigate the drivers of wheat prices and quantify their impact, a Vector Error Correction Model (VECM) has been used. The exogenous variables have been distinguished into four groups: market-specific factors, broad macroeconomic determinants, speculative components, and weather variables. The quadriangulation of the determinants will enable us to better understand the movements in wheat price and identify the specific role of each component. The results show a mix of short and long term factors that are contributing to wheat price movements, and their effect should be taken into account in designing proper policy intervention to mitigate the negative impact of price shocks.