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• A STOCHASTIC FRAMEWORK FOR MODELING TRADERS’ COGNITIVE RISK UNDER VOLATILITY IN DECENTRALIZED FINANCIAL MARKETS

  D. G. Veselova , N. Е. Sergeev

  189-199

  2025-12-30

  Abstract ▼

  This study is devoted to the development of a stochastic model of traders’ cognitive risk as a core component of an intelligent decision support system (DSS) for decentralized cryptocurrency markets.
  The relevance of the research is determined by the specific characteristics of the DeFi environment, which include high and nonstationary volatility, the absence of centralized stabilization mechanisms, information asymmetry, and a strong influence of behavioral factors on trading decisions. Under these conditions, traditional deterministic and static DSS frameworks demonstrate limited effectiveness, as they fail to account for the dynamic perception of risk by market participants and the associated cognitive biases.
  The objective of this research is to formalize traders’ cognitive risk as a memory-dependent stochastic process and to integrate the proposed model into the architecture of an adaptive DSS for risk management. To achieve this objective, a stochastic differential equation is developed to describe the dynamics of cognitive risk as a function of market volatility and prevailing market regimes. In addition, a probabilistic transition kernel is introduced to link objective market characteristics with the subjective perception of risk. For parameter estimation, an identification framework based on the Expectation–Maximization algorithm combined with particle filtering is proposed, enabling robust inference in the presence of nonlinear dynamics and latent state variables. The research methodology includes numerical simulations on synthetic data, parameter estimation using real cryptocurrency time series, and validation of the proposed approach through walk-forward and purged K-fold schemes. The quality of probabilistic forecasts is evaluated using the Negative Log-Likelihood (NLL), Brier Score, and Expected Calibration Error (ECE) metrics. Experimental results demonstrate that incorporating the stochastic cognitive layer improves probabilistic forecasting performance by an average of 10–15%, reduces NLL by approximately 8%, decreases the Brier Score by about 11%, and lowers ECE by nearly 35%. Furthermore, the accuracy of predicting key transitions between market regimes increases by 5–7 percentage points. The obtained results confirm the effectiveness of the proposed stochastic cognitive-risk model and demonstrate its applicability for the development of adaptive DSS solutions in the DeFi domain. The proposed framework provides a foundation for further research on predictive models of trader behavior and the design of intelligent risk-management systems for decentralized financial ecosystems.