Statistical interpretation of stress-related indicators collected through wearable biosensors often relies on benchmarking, especially in the context of stress management interventions. However, it remains unclear how to construct stress level benchmarks for group stress-related indicators using limited historical data. This study examines whether the method of numerical simulation of stress-related responses could contribute to constructing benchmark curves. Experimental data consists of physiological and non-physiological signals of 18 Swiss public servants collected through wearable biosensors. This study draws upon Stress Pattern Recognition algorithm and Markov Chain modeling for simulating emotional responses according to specified data-driven scenarios of high and low stress. Proposed method allows constructing benchmark curves for an Overarousal Index. Results demonstrate that numerical simulation based on small datasets can be used effectively for constructing stress level benchmarks. The findings contribute to methodological knowledge in statistical learning on Stress Pattern Recognition algorithms and Markov Chains modeling by expanding their application to a new field of emotional response simulation according to scenarios.