<![CDATA[Dependent Tail Value-at-Risk (DTVaR) is a measure that quantifies expected loss between two quantiles while accounting for dependence among risk variables. Optimizing DTVaR remains challenging due to the complexity of its objective function. This study compares Differential Evolution (DE) and Particle Swarm Optimization (PSO) for DTVaR optimization using return data of Propane Mont Belvieu price as the target risk variable and Brent crude oil price as the risk variable over 2020–2025. Both algorithms are evaluated using objective values, convergence, and validation tests. The results show that both algorithms converge and produce DTVaR estimates. DE achieves the smallest objective function value. For simulated data, the combination (α= 0,95; δ= 0,8) produces the smallest difference for both algorithms, with PSO yielding the smaller value. For real data, DE achieves the smallest difference at (α= 0,95; δ= 0,8), whereas PSO performs best at (α= 0,95; δ= 0,8). These findings indicate that algorithm performance depends on evaluation criteria and data characteristics.]]>
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