The study finds that the export shares of machinery and food/crude materials are significantly correlated with GDP.
problem Understanding the relationship between export shares and GDP across different commodity sectors.
method Analysis of GDP and international trade data using the SITC classification from 1962 to 2000.
result The export shares of machinery and food/crude materials are significantly correlated with GDP, following a power-law relationship.
Study uses network analysis to rank countries based on food production diversity and specialization.
problem Ranking countries based on their food production diversity and specialization.
method Network analysis of country-food production data, transforming into overlap matrices, identifying subsets, and ranking based on fitness and specialization.
result Countries with high fitness produce highly specialized food commodities, while those with low fitness produce diverse but low specialized food products.
Paper analyzes patterns in African exports using network analysis.
problem Limited data on African countries in trade networks.
method Defined two bipartite networks and their minimal spanning trees.
result Emerging patterns reveal important characteristics of African exports.
Wavelet analysis reveals financialization effects on oil-food price correlation.
problem Investigating the correlation between oil and food prices and their determinants.
method Wavelet analysis and energy-based measures to differentiate high and low frequency movements.
result Significant local correlation between food and oil is due to financialization and emerging economies' demand.
This paper evaluates heuristics and hyperparameters in weight-sharing NAS methods.
problem Improving the performance of weight-sharing NAS methods.
method Systematic evaluation of heuristics and hyperparameters in weight-sharing NAS algorithms.
result Some heuristics negatively impact super-net and stand-alone performance correlation.
We study here numerically the behavior of an ideal gas like model of markets having only one non-consumable commodity. We investigate the behavior of the steady-state distributions of money, commodity and total wealth, as the dynamics of trading or exchange of money and commodity proceeds, with local (in time) fluctuat…
Generic model for commodity derivatives pricing.
problem Modeling forward curves in commodity derivatives.
method Theoretical demonstration of multiple components driving commodity prices; empirical validation.
result Model accurately prices commodity derivatives, close to market prices.
Model predicts commodity futures and options prices with a fast calibration.
problem Calibrate commodity derivatives with limited market data.
method Stochastic-local volatility model with parsimonious parametrization.
result Model accurately describes forward-curve and smile dynamics.
Model prices commodity futures and index options.
problem Deriving accurate prices for derivative contracts on commodity futures and indices.
method Stochastic local volatility model for commodity futures.
result Model accurately recovers prices of derivative claims.
Study applied stochastic spread pairs trading on Indian commodities.
problem Finding profitable trading pairs in Indian commodity market.
method Applied Johanssen Cointegration tests, selected cointegrated pairs, used single-factor stochastic model, optimized parameters using differential evolution and backtesting.
result Found 12 cointegrated pairs with a Sharpe ratio above 1.4.
This study examines how economic policy uncertainty impacts commodity prices across different crises.
problem Impact of economic policy uncertainty on commodity prices during various crises.
method Wavelet coherence analysis of time series data.
result Commodity prices are more correlated during global financial and Covid-19 crises.
We analyze daily prices of 29 commodities and 2449 stocks, each over a period of ≈15 years. We find that the price fluctuations for commodities have a significantly broader multifractal spectrum than for stocks. We also propose that multifractal properties of both stocks and commodities can be attributed mainl…
In this model study of the commodity market, we present some evidence of competition of commodities for the status of money in the regime of parameters, where emergence of money is possible. The competition reveals itself as a rivalry of a few (typically two) dominant commodities, which take the status of money in turn…
We study the topological properties of the multinetwork of commodity-specific trade relations among world countries over the 1992-2003 period, comparing them with those of the aggregate-trade network, known in the literature as the international-trade network (ITN). We show that link-weight distributions of commodity-s…
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,…
The paper analyzes the crash of stock and commodity markets during COVID-19 using Topological Data Analysis.
problem Identifying and understanding the dynamics and interdependence of stock and commodity markets during the COVID-19 crash.
method Topological Data Analysis (TDA) and Wasserstein Distance (WD) to identify crashes and compare market dynamics.
result Significant topological differences and interdependence between stock and commodity markets during the crash period.
Generative models improve commodity hedging using deep learning.
problem Improving risk management in commodity markets.
method Four state-of-the-art generative models adapted for commodity time series.
result Deep hedging of commodity options trained on generated time series shows promising results.
This study examines the tracking errors of commodity leveraged ETFs, finding many underperform significantly.
problem Tracking errors of commodity leveraged ETFs over longer horizons.
method Constructed a benchmark process accounting for volatility decay and used it to examine ETFs' performance.
result Many commodity leveraged ETFs underperform significantly against a benchmark, quantified via realized effective fee.
Model shows how market structure affects commodity prices.
problem Understanding how market structure influences commodity prices.
method Agent-based model with profit-maximizing agents, self-organizing into groups.
result Prices rise sharply when resources are scarce, marked by a turning point.
The paper develops a new model for rough volatility in commodity markets.
problem Calibration of rough volatility models for commodity futures prices.
method Developed a general rough volatility model with automatic calibration and treatment of the Samuelson effect.
result Calibrated rBergomi and rHeston models to WTI Crude Oil futures options data.
We find a rank effect in commodity prices that yields higher returns.
problem Understanding the pricing dynamics of commodities over time.
method Nonparametric econometric methods to demonstrate the rank effect as a consequence of stationary relative asset price distribution.
result A portfolio of lower-ranked, lower-priced commodities yields 23% higher annual returns than a portfolio of higher-ranked, higher-priced commodities.
Extends Black model to include commodities with potential negative prices.
problem Modeling commodities with the possibility of negative prices due to delivery failures.
method Integrates a `delivery liability' option into the Black model.
result Validates the approach through a simple generalization of the Black model.
Study finds commodity and energy futures prices are nonlinearly determined rather than stochastic.
problem Determining the nature of futures prices in energy and commodity markets.
method MLE and determinism test based on phase space reconstruction, estimating κ for determinism rate.
result Futures prices show a reliability level κ near 1 and positive MLE, indicating nonlinear determinism.
The paper examines how realized and implied volatilities predict future commodity quantiles.
problem Estimating and predicting the Value-at-Risk (VaR) of commodities.
method Panel quantile regression framework.
result Future quantile returns of commodities depend on both ex-post and ex-ante volatilities.
We detect and quantify asymmetries in volatility spillovers using the realized semivariances of petroleum commodities: crude oil, gasoline, and heating oil. During the 1987--2014 period we document increasing spillovers from volatility among petroleum commodities that substantially change after the 2008 financial crisi…
In this paper we analyzed dependencies in commodity markets investigating correlations of future contracts for commodities over the period 1998.09.01 - 2007.12.14. We constructed a minimal spanning tree based on the correlation matrix. The tree provides evidence for sector clusterization of investigated contracts. We a…
We consider a market model that consists of financial investors and producers of a commodity. Producers optionally store some production for future sale and go short on forward contracts to hedge the uncertainty of the future commodity price. Financial investors take positions in these contracts in order to diversify t…
It is commonly accepted that Commodities futures and forward prices, in principle, agree under some simplifying assumptions. One of the most relevant assumptions is the absence of counterparty risk. Indeed, due to margining, futures have practically no counterparty risk. Forwards, instead, may bear the full risk of def…
A new currency DCM reduces logistics costs and preserves wealth.
problem High logistics costs associated with commodity money.
method Introducing Decayed Commodity Money (DCM) with an attenuation coefficient based on logistics costs.
result DCM offers a cost-effective and wealth-preserving alternative to traditional currency.
Commodity ETFs' portfolio optimization under heavy-tailed returns.
problem Optimizing commodity ETF portfolios under heavy-tailed return behavior.
method Passive buy-and-hold vs. rolling-window optimized portfolios.
result Improved risk-adjusted performance with minimum-risk and CVaR-based portfolios.
Hierarchical graph learning for calendar spread strategies in commodity futures markets
problem Developing machine-learning methods for calendar spread strategies in commodity futures markets
method Proposing a hierarchical graph learning approach
result Outperforming benchmark models in both prediction and trading performance
Trend following in cryptocurrencies yields high returns, similar to commodities.
problem Investing in cryptocurrencies using trend following strategies.
method A decade of data analysis on cryptocurrency markets and trend following strategies.
result Cryptocurrencies offer strong returns and diversification against traditional equities.
The study explains why signature methods work in commodity futures term structure classification.
problem Lack of interpretability in signature methods for term structure classification.
method Introducing signature perturbations to explain the success of signature-based classification.
result The volatility of the convenience yield is the major discriminant for commodity markets classification.
Model for commodity forward prices with stochastic volatility and decorrelation.
problem Capturing dynamics of commodity forward prices and volatility.
method Two-factor model with stochastic volatility and decorrelation, numerical and Monte Carlo methods.
result Efficient pricing of various derivative payoffs.
Study shows post-COVID commodity futures returns and volatility changed for different products.
problem Analyzing how the pandemic affected Chinese commodity futures markets.
method Empirical analysis of commodity futures returns and cointegration before and after the pandemic.
result Post-COVID, some commodity futures returns increased significantly, while others saw higher volatility.
We have studied here the self-organising features of the dynamics of a model market, where the agents `trade' for a single commodity with their money. The model market consists of fixed numbers of economic agents, money supply and commodity. We demonstrate that the model, apart from showing a self-organising behaviour,…
We study valuation of swing options on commodity markets when the commodity prices are driven by multiple factors. The factors are modeled as diffusion processes driven by a multidimensional Lévy process. We set up a valuation model in terms of a dynamic programming problem where the option can be exercised continuousl…
Investment decision in commodity reserves is modeled with uncertainty and learning.
problem Uncertainty in commodity reserve levels and price uncertainty.
method Continuous-time Markov chain model to value investment option.
result Learning about reserve levels improves investment decision.
A new HOM model improves forecasting of Indian base metal prices.
problem Improving accuracy in predicting base metal prices in the Indian market.
method A Higher Order Markovian (HOM) model with varying order based on market delay.
result The HOM model consistently outperforms the standard Markovian model in forecasting.
In commodity markets the convergence of futures towards spot prices, at the expiration of the contract, is usually justified by no-arbitrage arguments. In this article, we propose an alternative approach that relies on the expected profit maximization problem of an agent, producing and storing a commodity while trading…
Study clusters time series of commodity prices using threshold autoregressive models.
problem Grouping time series of commodity prices based on their dynamics.
method Threshold autoregressive models to capture nonlinear and time-varying properties of commodity prices.
result Determined temporal clusters of commodity prices.
This thesis applies RL to market making in China's commodity market.
problem Leverage RL for market making in China's commodity market.
method Developed an automatic trading system using RL.
result RL is feasible for market making in China's commodity market.
This study analyzes relationships between factor endowments and commodity outputs in a trade model.
problem Analyzing factor endowment-commodity output relationships in a trade model.
method Developed a method to estimate the position of the EWS-ratio vector and derived sufficient conditions for specific sign patterns.
result Derived sufficient conditions for extreme factors to be complements and for specific Stolper-Samuelson sign patterns.
Study improves prediction of commodity futures using multi-factor model.
problem Improving accuracy in predicting commodity futures prices.
method State-space functional regression model incorporating yield curve dynamics.
result Functional regression model outperforms Schwartz-Smith model in estimating short-end of futures curve.
Study strategic competition in commodity markets using impulse-switching controls.
problem Strategic competition between upstream and downstream firms in commodity markets.
method Non-zero-sum stochastic differential game with mixed impulse/switching controls.
result Multiple Nash equilibria found, depending on the number of switches by the downstream firm.
The paper models natural gas futures prices and volatility, using Monte Carlo and reinforcement learning.
problem Hedging and selecting delivery strategies in natural gas markets.
method Dynamical model for futures prices, least-square Monte Carlo simulation, reinforcement learning.
result Calibrated futures price quotes and implied volatility smiles for different delivery periods.
Price fluctuations of commodities like cotton and wheat are thought to display probability distributions of returns that follow a Lévy stable distribution. Recent analysis of stocks and foreign exchange markets show that the probability distributions are not Lévy stable, a plausible result since commodity markets have …
A new multi-factor model improves commodity pricing accuracy.
problem Enhancing accuracy in commodity pricing by integrating multiple risk factors.
method A four-factor model using Kalman filter for simultaneous estimation and state variable filtering.
result The four-factor model outperforms existing models in capturing futures term structures and crude oil pricing.