The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.

Financial development has been considered an efficient and effective mechanism for the sustainable economic growth and development of emerging markets in past decades. However, various concerns have emerged in relation to the influences of financial sector development on income inequality. It is the claim of this paper that findings from the current literature are incomplete. This is because various proxies have been utilized inconsistently for both financial development and income inequality in previous empirical studies. This study extends the current literature on this important finance–inequality nexus by examining a sample of 21 emerging countries for the period of 1961–2017. Various estimation techniques were employed with the aim of ensuring robust findings. Findings from this paper confirm the existence of an inverted U-curve relationship between financial development and income inequality, implying that income inequality may rise at the early stage of financial development and fall after a certain level is achieved. Policy implications have emerged from the findings of this study.

The current literature has generally considered prices of the agricultural commodity as an endogenous factor to crude oil price. As such, the role of the agricultural market in the energy sector has been largely ignored. We argue that the expansion of agricultural production may trigger a significant increase in oil price. In addition, the world has recently witnessed a growth in biofuel production, leading to an increase in the size of the agricultural sector. This study is conducted to examine the impact of different agricultural shocks on the oil and agricultural markets in the US for the period from 1986 to 2018. The study utilizes the Structural Vector Autoregressive (SVAR) model to estimate the relationship between the agricultural market and the crude oil market. Moreover, the variance decomposition is also used to quantify the contribution of agricultural demand shocks on oil price variations. Findings from this paper indicate that different agricultural shocks can have different effects on oil price and that corn use in ethanol plays an important role in the impact of corn demand shocks on oil price. We find evidence that the agricultural market can have an impact on oil prices through two main channels: indirect cost push effect and direct biofuel effect. Of these, the biofuel channel unexpectedly suggests that the expansion of bioethanol may in fact foster the dependency of the economy on fossil fuel use and prices.

Previous empirical studies have generally considered biofuel as a main factor in changes in the relationship between oil and agricultural prices because these changes happened after U.S. biofuel policies were implemented. However, it has been argued that other economic factors can trigger the correlation of these two markets. This study was conducted to examine the transmission mechanisms that influence the relationship between oil and agricultural prices. This paper used the interacted panel vector autoregressive framework, which allowed us to investigate the effect of biofuel production under different regimes of exchange rates and global economic activities. The responses of agricultural prices to oil prices at different levels of biofuel production, global economic activity, and exchange rates were examined in this paper. Data on prices for 10 agricultural commodities—barley, beans, corn, cotton, oats, rice, sorghum, soybean, sunflower, and wheat—from January 2000 to May 2019, were used in this study. Our findings indicate that oil prices can affect agricultural prices through biofuel and exchange rates. Moreover, the effect of biofuel depends on the level of global economic activity and exchange rates. We offer some policy implications on the basis of our findings in this study.

The cornerstone of the capital asset pricing model (CAPM) lies with its beta. The question of whether or not beta is dead has attracted great attention from academics and practitioners in the last 50 years or so, and the debate is still ongoing. Many empirical studies have been conducted to test the validity of beta within the framework of CAPM. However, it is a claim of this paper that beta at the industry level has been largely ignored in the current literature. This study is conducted to examine if beta, proxied for a systematic risk, should be considered valid in the application of the CAPM at the industry level for Australia using daily data on 2200 stocks listed on the Australian Securities Exchange from January 2007 to 31 December 2016. Various portfolio formations are utilized in this paper. General economic conditions such as interest rate, inflation, and GDP are examples of systematic risk. Findings from this study indicate that the selection of portfolio construction, estimation technique, and news about economic conditions significantly affects the view whether or not beta should be considered as a valid measure of systematic risk.