Quantitative Finance, Risk & Stochastic Modeling
August Quant
Model risk. Simulate uncertainty.
A quantitative understanding of risk, volatility and portfolio behavior.
Monte Carlo Simulation
Illustrative SimulationP10
78.7
Median
101.9
P90
127.7
32 simulated paths over 48 periods — illustrative Monte Carlo output.
Core Capabilities
- Monte Carlo simulation & Value at Risk
- Portfolio optimization and correlation analysis
- Stochastic volatility (e.g. Heston) modeling
- Interest-rate modeling (e.g. CIR)
- Derivatives analytics
- Risk decomposition and scenario analysis
Business Problems We Solve
- I want to measure risk quantitatively, not just intuitively.
- I need quantitative portfolio analysis.
- I don't understand how exposed we are under different scenarios.
- Our risk reporting doesn't reflect the uncertainty involved.
Typical Deliverables
- Monte Carlo risk simulation
- Value-at-Risk (VaR) report
- Portfolio optimization model
- Volatility and correlation analysis
Representative Technologies
PythonNumPySciPyPandasQuantLibExcel
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