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…
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48 results for “Financial Commodities”
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…
In this paper, we reveal the attenuation mechanism of anchor of the commodity money from the perspective of logistics warehousing costs, and propose a novel Decayed Commodity Money (DCM) for the store of value across time and space. Considering the logistics cost of commodity warehousing by the third financial institut…
Study volatility spillovers among many financial assets using a t-distributed VAR model.
problem Understanding volatility spillovers among multiple financial assets.
method Used a large t-Vector AutoRegressive (VAR) model with t-distributed errors for a large number of assets.
result Revealed bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities.
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 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.
Nowadays, when crashes and crises are rather frequent events, an effective monitoring system for the international financial market is needed. Modern nonlinear methods, such as Recurrence Quantification Analysis (RQA), demonstrate the ability to reveal the regularities of the system behavior. Thus, they can be useful f…
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.
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…
Optimizes financial auditor schedules to reduce time and costs.
problem Efficiently scheduling financial auditors with multiple constraints.
method Used Integer Linear Programming and compared two exact formulations.
result Multi-commodity network flow formulation is 24 times faster.
Study forecasts commodity options' implied volatility using Nelson-Siegel factors.
problem Predicting implied volatility in commodity markets.
method Rolling out-of-sample forecasting with Nelson-Siegel factors.
result Nelson-Siegel factors improve forecasting accuracy for energy and precious metals options.
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.
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.
Deep reinforcement learning boosts commodities trading performance.
problem Improving algorithmic trading performance in commodities markets.
method Formulated as a stochastic dynamical system, employed actor-based and actor-critic-based policy gradient algorithms with CNN and LSTM function approximators.
result DRL models increase Sharpe ratio by 83% compared to buy-and-hold.
Improved financial performance through better regime prediction.
problem Predicting financial market regimes for profitable trading.
method A novel method combining contrarian trading and frequent short positions.
result Significant performance improvements over four years across three asset classes.
Proposes a new model to describe positive volatility-price correlation in commodity markets.
problem Negative correlation between volatility and asset prices in commodity markets.
method Deduced a variable volatility elasticity (VVE) model from the CEV model.
result The VVE model can describe positive correlation in commodity markets.
Study reveals dynamic linkage between Peanut and Soybean Oil futures markets.
problem Exploring interdependence between Peanut and other agricultural commodities in Chinese futures market.
method Constructed multivariate linear regression models and used VAR and DCC-EGARCH models for dynamic relationships. Applied MLP, CNN, and LSTM neural networks for price prediction.
result Significant dynamic linkage between Peanut and Soybean Oil futures markets through DCC-EGARCH, limited influence from other futures markets through VAR model.
In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST n…
The paper proposes pricing methods for multi-asset generalized variance swaps.
problem Hedging risk in financial markets with complex asset structures.
method Proposes pricing methods for two new measures of generalized variance (maximum eigen-value and trace of covariance matrix) under Markov-modulated volatilities.
result Demonstrates pricing results for three stocks, highlighting the usefulness of these swaps in commodity risk management.
Deep reinforcement learning improves trading performance in volatile energy markets.
problem Volatility and low signal-to-noise ratios in energy markets.
method Formalized trading as a stochastic system, developed reactive and adaptive algorithms, used deep neural networks.
result Deep reinforcement learning models outperform buy-and-hold strategy with an 83% higher Sharpe ratio.
Information content of financial markets: a practical approach based on Bohmian quantum mechanicsq-fin.GN
The Bohmian quantum approach is implemented to analyze the financial markets. In this approach, there is a wave function that leads to a quantum potential. This potential can explain the relevance and entanglements of the agent's behaviors with the past. The light is shed by considering the relevance of the market cond…
In two previous papers the author developed a second-order price adjustment (tâtonnement) process. This paper extends the approach to include both quantity and price adjustments. We demonstrate three results: a analogue to physical energy, called "activity" arises naturally in the model, and is not conserved in general…
Prices of commodities or assets produce what is called time-series. Different kinds of financial time-series have been recorded and studied for decades. Nowadays, all transactions on a financial market are recorded, leading to a huge amount of data available, either for free in the Internet or commercially. Financial t…
Modeling producer and consumer interactions in commodity markets with risk aversion.
problem Analyzing the impact of risk aversion on producer-consumer interactions in commodity markets.
method Linear-quadratic McKean-Vlasov stochastic differential game, martingale optimality principle, BSDEs.
result Characterization of Nash equilibrium and indifference prices.
BERT models outperform GPT in financial engineering sentiment analysis.
problem Using sentiment from news events for commodity trading.
method Benchmarked Transformer models (BERT, GPT) on financial engineering task.
result CopBERT models outperform GPT and vanilla BERT models.
Model predicts risk-adjusted returns across various financial markets.
problem Stationary models fail in predicting risk-adjusted returns due to market regime changes.
method Asset-independent regime-switching model using hidden Markov models.
result Accurately detects bull, bear, and high volatility periods for improved risk-adjusted returns.
This article presents an empirical study of thirteen derivative markets for commodity and financial assets. It compares the statistical properties of futures contracts's daily returns at different maturities, from 1998 to 2010 and for delivery dates up to 120 months. The analysis of the fourth first moments of the dist…
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.
Paper introduces MADL loss function for better AIS model optimization.
problem Optimizing machine learning models for AIS construction.
method Proposes Mean Absolute Directional Loss (MADL) function.
result MADL function improves hyperparameter selection and investment strategy efficiency.
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.
Paper proposes novel hedging strategies using LSTM models for diversified investment portfolios.
problem Hedging risky asset portfolios in turbulent financial markets.
method Four diverse models (LSTM, ARIMA-GARCH, momentum, contrarian) generate price forecasts for diversified AIS.
result LSTM-based strategies outperform other models, with Bitcoin being the best diversifier for S&P 500 index.
Pricing commodity index optionsq-fin.PR
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.
FinCast is a foundation model for financial time-series forecasting that outperforms existing methods.
problem Challenges in financial time-series forecasting due to temporal non-stationarity, multi-domain diversity, and varying temporal resolutions.
method FinCast is a foundation model specifically designed for financial time-series forecasting, trained on large-scale financial datasets.
result FinCast exhibits robust zero-shot performance, effectively capturing diverse patterns without domain-specific fine-tuning.
Multifractal Properties of Price Fluctuations of Stocks and Commoditiescond-mat.stat-mech
We analyze daily prices of 29 commodities and 2449 stocks, each over a period of 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…
Optimizes U.S. stock portfolios with natural gas and crude oil to reduce risk and enhance returns.
problem Reduces portfolio risk and enhances returns by diversifying with natural gas and crude oil.
method Uses time-varying multivariate copula analysis and variance regimes to handle structural changes in asset prices.
result Minimizes portfolio variance, semi-variance, and tail risk with or without return constraints.
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,…
Although portfolio management didn't change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in the deepening of the relationship between risk and asset management. Risk parity then became a popular financial model of in…
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.
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.
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.
Paper proposes a CNN model for improved multi-asset portfolio risk prediction.
problem Challenges in risk management of multi-asset portfolios due to limited correlation capture.
method Uses CNN and image processing to convert financial data into images for enhanced feature extraction.
result CNN model significantly outperforms traditional methods in risk prediction accuracy.
Study uses LLMs to simplify financial regulation interpretation.
problem Complex financial regulations are hard to interpret and implement.
method Developed prompts to guide LLMs in extracting key information from regulations.
result GPT-4 outperforms other LLMs in processing and executing regulatory requirements.
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…
Two centuries of trend followingq-fin.PM
We establish the existence of anomalous excess returns based on trend following strategies across four asset classes (commodities, currencies, stock indices, bonds) and over very long time scales. We use for our studies both futures time series, that exist since 1960, and spot time series that allow us to go back to 18…