Deep Learning Based Models: Basic LSTM, Bi LSTM, Stacked LSTM, CNN LSTM and Conv LSTM to Forecast Agricultural Commodities Prices

The literature argues that an accurate price prediction of agricultural goods is a quintessence to assure a good functioning of the economy all over the world. Research reveals that studies with application of deep learning in the tasks of agricultural price forecast on short historical agricultural prices data are very scarce and insist on the use of different methods of deep learning to predict and to this reaction of filling the gap, this study employs five versions of LSTM deep learning techniques for the task of five agricultural commodities prices prediction on univariate time series dataset of Rice, Wheat, Gram, Banana, and Groundnut spanning January 2000 to July 2020. The study obtained good forecasting results for all five commodities employing all the five LSTM models. The study validated the results with lower values of error metrics, MAE, MAPE, MSE, and RMSE and two paired t-test with hypothesis and confidence level of 95% as a measure of robustness. The study predicted the one month ahead future price for all the five commodities and compared it with actual prices using said LSTM models and obtained promising results.

Modeling Price Volatility for Selected Agricultural Commodities in Ethiopia: The Application of GARCH Models

The purpose of this study was to model and forecast volatility of returns for selected agricultural commodities prices using generalized autoregressive conditional heteroskedasticity (GARCH) models in Ethiopia. GARCH family models, specifically GARCH, threshold generalized autoregressive conditional heteroskedasticity (TGARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) were employed to analyze the time varying volatility of selected agricultural commodities prices from 2011to 2021. The data analysis results revealed that, out of the GARCH specifications, TGARCH model with Normal distributional assumption of residuals was a better fit model for the price volatility of Teff and Red Pepper in which their return series reacted differently to the good and the bad news. The study indicated the presence of leverage effect which implied that the bad news could have a larger effect on volatility than the good news of the same magnitude, and the asymmetric term was found to be significant. Also, TGARCH model was found to be the accurate model for forecasting price return volatility of the same commodities, namely Teff and Red Pepper. In short, the study concludes that TGARCH was to be the best fit to model and forecast price return volatility of Teff and Red Pepper in the Ethiopian context.

Financialization of Commodity Market

The aim of the article is to present possible consequences caused by the development of commodity market financialization understood by the influence of financial investor’s speculation. Also the task of elaboration is to outline the existence of financial factors in the price creation process of commodities. The existing impact of financialization on the volatility of commodity prices significantly modifies the market. The results of the research and analyzes carried out indicate a similarity in the behavior of the markets of commodities. The situation results from the redistribution of the risk of financial investors who having a few goods in the investment portfolio, next to large transaction volumes affect the unification of price trends. Price shaping factors are being transformed. The decrease importance of supply or consumption in the context of the commodities market changes its form. The growing influence of investors who create numerous speculations transforms the market. Trade in futures contracts affects the level of commodities prices.

The Developmentalist Project of the PT Governments: An Economic and Political Assessment

An analysis of the endogenous and exogenous political and economic factors that conditioned the Partido dos Trabalhadores’s (PT) social-developmentalist project in 2003–2016 in the light of financialization and the “confidence game” conditioned by the volatility of external liquidity and commodities prices concludes that the first Lula administration faced the problem of a crisis of confidence and adopted orthodox policies but was able, with the improvement of international conditions, to launch policies of a more interventionist and distributive trend. Dilma Rousseff, facing a downright unfavorable international context, explicitly broke with the confidence game by applying the policy set of the new macroeconomic matrix. In her second term she radically reversed the policy orientation, moving toward a strong fiscal adjustment and monetary orthodoxy, and this eventually undermined her few sources of political support. The economic crisis from the second half of 2014 on undoubtedly contributed to the political crisis, which in turn made infeasible any attempt to implement policies to reverse the situation of economic crisis. Dilma’s impeachment finally interrupted the PT’s developmentalist project, allowing the emergence of new political actors. Uma análise dos fatores endógenos e exógenos, políticos e econômicos que condicionaram o projeto social-desenvolvimentista do Partido dos Trabalhadores (PT) em 2003–2016 à luz da financeirização e do “confidence game” condicionado pela volatilidade dos ciclos externos de liquidez e preços de commodities conclui que o primeiro governo Lula enfrentou o problema de crise de confiança e adotou políticas ortodoxas, mas pôde, com a melhoria nas condições internacionais, adotar políticas de perfil mais intervencionista e redistributivista. Já Dilma Rousseff, embora enfrentando contexto internacional francamente desfavorável, rompe explicitamente com o “confidence game” ao assumir o conjunto de políticas da Nova Matriz Macroeconômica. Na transição do primeiro para o segundo mandato, Dilma inverteu radicalmente a orientação das políticas, partindo para um forte ajuste fiscal e a ortodoxia monetária, o que acabou minando os poucos focos de sustentação política com os quais contava na sociedade. A crise econômica a partir do segundo semestre de 2014 sem dúvida contribuiu para dar origem à crise política, e esta por sua vez inviabilizou qualquer tentativa de implementação de políticas para reverter o quadro de crise econômica. O impeachment de Dilma, por fim, interrompe o projeto desenvolvimentista do PT, permitindo a emergência de novos atores políticos.

Potato price analysis of Delhi market through ensemble empirical mode decomposition

Agricultural commodities prices depends on production, unnecessary demand, production uncertainty, market flaws etc. Due to these factors agricultural price series are non-stationary and non-linear in nature. Therefore analyzing agricultural commodities prices is considered as a challenging task. The traditional stationary approach of time series is unable to capture non-stationary and non-linear properties of agricultural price series. Non-stationary and non-linear properties present in the price series may be accurately analyzed through empirical mode decongation (EMD). In this technique, the original time series decomposed into intrinsic mode functions and residue. One of the major limitation of EMD is the presence of the mode mixing. To overcome this limitation of the EMD, we use ensemble empirical mode decomposition (EEMD). Using this technique in this study, Delhi market potato prices have been analyzed.

Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes

In the described research three agricultural commodities (i.e., wheat, corn and soybean) spot prices were analyzed. In particular, one-month ahead forecasts were built with techniques like dynamic model averaging (DMA), the median probability model and Bayesian model averaging. The common features of these methods are time-varying parameters approach toward estimation of regression coefficients and dealing with model uncertainty. In other words, starting with multiple potentially important explanatory variables, various component linear regression models can be constructed. Then, from these models an averaged forecast can be constructed. Moreover, the mentioned techniques can be easily modified from model averaging into a model selection approach. Considering as benchmark models, time-varying parameters regression with all considered potential price drivers, historical average, ARIMA (Auto-Regressive Integrated Moving Average) and the naïve forecast models, the Diebold–Mariano test suggested that DMA is an interesting alternative model, if forecast accuracy is the aim. Secondly, the interpretation of time-varying weights ascribed to component models containing a given variable suggested that economic development of emerging BRIC economies (Brazil, Russia, India and China) is recently one of the most important drivers of agricultural commodities prices. The analysis was made on the monthly data between 1976 and 2016. The initial price drivers were various fundamental, macroeconomic and financial factors.