A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study analyzes the correlation structure of global agricultural futures markets using RMT.
Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.
The paper examines spillovers between agriculture, crude oil, carbon, and climate markets.
Study reveals dynamic linkage between Peanut and Soybean Oil futures markets.
This study examines local co-movements in energy, agriculture, and metal markets using copulas.
In this paper we investigate the adaptive market efficiency of the agricultural commodity futures market, using a sample of eight futures contracts. Using a battery of nonlinear tests, we uncover the nonlinear serial dependence in the returns series. We run the Hinich portmanteau bicorrelation test to uncover the momen…
Study measures risk spillovers between US and China's agricultural futures markets.
Risk is an inherent feature of agricultural production and marketing and accurate measurement of it helps inform more efficient use of resources. This paper examines three tail quantile-based risk measures applied to the estimation of extreme agricultural financial risk for corn and soybean production in the US: Value …
Study finds multifractal cross-correlations between agricultural markets and external uncertainties.
In this article, we analyze the application of options contract in special commodity supply chain such as fresh agricultural products. This problem is discussed in the point of the retailer. When spot market and future market are both available, we discuss how the retailer chooses the optimal production. Furthermore, o…
Favorit strategy helps farmers mitigate market price fluctuations.
In developing countries like India agriculture plays an extremely important role in the lives of the population. In India, around 80\% of the population depend on agriculture or its by-products as the primary means for employment. Given large population dependency on agriculture, it becomes extremely important for the …
Study analyzes climate impact on agricultural prices, offering insurance solutions.
Deep learning predicts crop prices with improved accuracy.
We investigate an economic system in which one large agent - the Japan government changes the environment of numerous smaller agents - the Japan agriculture producers by indirect regulation of prices of agriculture goods. The reason for this intervention was that before the oil crisis in 1974 Japan agriculture producti…
SAGDA generates synthetic agricultural datasets to improve ML in African farming.
We present the results of study of a possible relationship between the space weather and terrestrial markets of agricultural products. It is shown that to implement the possible effect of space weather on the terrestrial harvests and prices, a simultaneous fulfillment of three conditions is required: 1) sensitivity of …
We introduce a multi-factor stochastic volatility model for commodities that incorporates seasonality and the Samuelson effect. Conditions on the seasonal term under which the corresponding volatility factor is well-defined are given, and five different specifications of the seasonality pattern are proposed. We calcula…
Project forecasts crop prices to help Indian farmers choose optimal crops for better ROI.
We analyze the market efficiency of 25 commodity futures across various groups -- metals, energies, softs, grains and other agricultural commodities. To do so, we utilize recently proposed Efficiency Index to find that the most efficient of all the analyzed commodities is heating oil, closely followed by WTI crude oil,…
This thesis applies RL to market making in China's commodity market.
Study improves paddy rice yield predictions in Peru using sparse regression and climatic variables.
Precision farming uses data analysis to optimize crop management.
Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts th…
Study finds farmers are willing to pay higher premiums for higher coverage in agricultural insurance.
Python tool assesses European agricultural production resilience.
This paper uses Monte Carlo simulation to value quality options in agricultural futures contracts.
Recent introduction of ICT in agriculture has brought a number of changes in the way farming is done. This means use of Internet of Things(IoT), Cloud Computing(CC), Big Data (BD) and automation to gain better control over the process of farming. As the use of these technologies in farms has grown exponentially with ma…
Study reveals clusters of resilient and vulnerable Spanish agri-food firms post-Ukraine-Russia war.
Botswana's agricultural credit scheme is unsustainable and needs reform.
Modeling dynamic groundwater markets with price formation and trading strategies.
This paper studies the market phenomenon of non-convergence between futures and spot prices in the grains market. We postulate that the positive basis observed at maturity stems from the futures holder's timing options to exercise the shipping certificate delivery item and subsequently liquidate the physical grain. In …
Develops adaptive algorithms for sustainable fertilizer use in agriculture.
We propose a stylized model of production and exchange in which long-term investors set their production decision over a horizon τ , the "time to produce", and are liquidity constrained, while financial investors trade over a much shorter horizon δ (<< τ ) and are therefore more duly informed on the exogenous shocks af…
Digital technologies ignited a revolution in the agrifood domain known as precision agriculture: a main question for enabling precision agriculture at scale is if accurate product quality control can be made available at minimal cost, leveraging existing technologies and agronomists' skills. As a contribution along thi…
Weather is a key production factor in agricultural crop production and at the same time the most significant and least controllable source of peril in agriculture. These effects of weather on agricultural crop production have triggered a widespread support for weather derivatives as a means of mitigating the risk assoc…
We introduce an agent-based model, in which agents set their prices to maximize profit. At steady state the market self-organizes into three groups: excess producers, consumers and balanced agents, with prices determined by their own resource level and a couple of macroscopic parameters that emerge naturally from the a…
What are East Africa's industrial opportunities? In this article we explore this question by using the Product Space to study the productive structure of five south-east African countries: Kenya, Mozambique, Rwanda, Tanzania and Zambia. The Product Space is a network connecting products that tend to be exported by the …
In this paper, we provide an integrated systems modeling approach to analyzing global externalities from a microeconomic perspective. Various forms of policy (fiscal, monetary, etc.) have addressed flaws and market failures in models, but few have been able to successfully eliminate modern externalities that remain an …
The primary goal of this study is doing a meta-analysis research on two groups of published studies. First, the ones that focus on the evaluation of the United States Department of Agriculture (USDA) forecasts and second, the ones that evaluate the market reactions to the USDA forecasts. We investigate four questions. …
This study assesses how economic shocks affect the efficiency and robustness of international pesticide trade networks.
Study forecasts vegetable prices in Nepal using a novel index and ensemble model.
Data mining involves the systematic analysis of large data sets, and data mining in agricultural soil datasets is exciting and modern research area. The productive capacity of a soil depends on soil fertility. Achieving and maintaining appropriate levels of soil fertility, is of utmost importance if agricultural land i…
The paper presents a method to improve trust in deep learning models for crop and weed classification.
Crop yield forecasting is the methodology of predicting crop yields prior to harvest. The availability of accurate yield prediction frameworks have enormous implications from multiple standpoints, including impact on the crop commodity futures markets, formulation of agricultural policy, as well as crop insurance ratin…
Agricultural research has been profited by technical advances such as automation, data mining. Today, data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft wares are available, but data mining in agricultural soil datasets is a relatively…
Volatility is a key measure of risk in financial analysis. The high volatility of one financial asset today could affect the volatility of another asset tomorrow. These lagged effects among volatilities - which we call volatility spillovers - are studied using the Vector AutoRegressive (VAR) model. We account for the p…
The effects of weather on agriculture in recent years have become a major global concern. Hence, the need for an effective weather risk management tool (i.e., weather derivatives) that can hedge crop yields against weather uncertainties. However, most smallholder farmers and agricultural stakeholders are unwilling to p…