Co-integration for Soft Commodities with Non Constant Volatility

Chanol, E.; Collet, O.; Kostyuchyk, Nazar; Mesbah, T.; Nguyen, Quoc Hoang Long

doi:10.7763/ijtef.2015.v6.437

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Mean-reverting diffusion models beyond the OU process are Cox-Ingersoll-Ross (CIR), inhomogeneous geometric Browian motion (IGBM), and stochastic volatility (SV) models. These models have a drift term identical to the OU process, while the diffusion term is extended -as such, the disadvantage of constant volatility is overcome (see, e.g., Stehlıková 2008, Chanol et al 2015. The relevant studies are summarized in Table 4.…”

Section: Advanced Mean-reverting Diffusion Modelsmentioning

confidence: 99%

Review of stochastic differential equations in statistical arbitrage pairs trading

Endres¹

2020

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The use of stochastic differential equations offers great advantages for statistical arbitrage pairs trading. In particular, it allows the selection of pairs with desirable properties, e.g., strong mean-reversion, and it renders traditional rules of thumb for trading unnecessary. This study provides an exhaustive survey dedicated to this field by systematically classifying the large body of literature and revealing potential gaps in research. From a total of more than 80 relevant references, five main strands of stochastic spread models are identified, covering the ‘Ornstein–Uhlenbeck model’, ‘extended Ornstein–Uhlenbeck models’, ‘advanced mean-reverting diffusion models’, ‘diffusion models with a non-stationary component’, and ‘other models’. Along these five main categories of stochastic models, we shed light on the underlying mathematics, hereby revealing advantages and limitations for pairs trading. Based on this, the works of each category are further surveyed along the employed statistical arbitrage frameworks, i.e., analytic and dynamic programming approaches. Finally, the main findings are summarized and promising directions for future research are indicated.

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Section: Advanced Mean-reverting Diffusion Modelsmentioning

confidence: 99%

Review of stochastic differential equations in statistical arbitrage pairs trading

Endres¹

2020

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A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities

Chen

Rehman

2021

Energies

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The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied to conditional volatility forecasts by trading days and individual assets or asset classes, can identify critical periods in energy-related commodity markets. Unsupervised machine learning achieves this task without rules-based or subjective definitions of crises. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–2009 and the initial stages of the COVID-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilitates the visualization of trading regimes. Temporal clustering of conditional volatility forecasts reveals unusual financial properties that distinguish the trading of energy-related commodities during critical periods from trading during normal periods and from trade in other commodities in all periods. Whereas critical periods for all commodities appear to coincide with broader disruptions in demand for energy, critical periods unique to crude oil and refined fuels appear to arise from acute disruptions in supply. Extensions of these methods include the definition of bull and bear markets and the identification of recessions and recoveries in the real economy.

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Co-integration for Soft Commodities with Non Constant Volatility

Cited by 2 publications

References 17 publications

Review of stochastic differential equations in statistical arbitrage pairs trading

Review of stochastic differential equations in statistical arbitrage pairs trading

A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities

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