Risk & Control

monte-carlo-financial-simulator

45 天前更新1 分鐘內設定完

總覽

The Monte Carlo Financial Simulator provides end-to-end stochastic modeling for financial forecasting, valuation, and risk analysis.

The Monte Carlo Financial Simulator provides end-to-end stochastic modeling for financial forecasting, valuation, and risk analysis. It fits and samples from normal, lognormal, triangular, PERT and custom or historical distributions; models variable dependence via correlation matrices, Cholesky decomposition, copulas and rank correlations; and runs efficient, convergent simulations with automated sample-size determination and stopping criteria. Outputs include Monte Carlo, parametric and historical VaR, expected shortfall (CVaR), marginal/incremental VaR, percentile/bootstrap confidence intervals, prediction/tolerance bounds, and joint regions. Typical users—risk managers, quants, portfolio managers and CFOs—use it for capital allocation, stress testing, scenario probability estimation, regulatory reporting and model validation. Integration with Crystal Ball/@RISK, model import/export and convergence diagnostics ensure robust, auditable probabilistic insights and improved decision-making under uncertainty.

Skill.md

這個 Skill 如何運作

SKILL.mdALPHIO / 已驗證

Monte Carlo Financial Simulator

Overview

The Monte Carlo Financial Simulator skill enables probabilistic financial modeling through stochastic simulation. It generates thousands of scenarios based on probability distributions to quantify risk and uncertainty in financial forecasts and valuations.

Capabilities

Probability Distribution Fitting

Normal distribution fitting
Lognormal distribution for positive values
Triangular distribution for expert estimates
PERT distribution modeling
Custom distribution creation
Historical data-based fitting

Correlation Matrix Handling

Variable correlation specification
Cholesky decomposition for correlated sampling
Copula implementation
Rank correlation (Spearman)
Correlation stability testing
Partial correlation analysis

Convergence Analysis

Sample size determination
Convergence testing
Precision metrics calculation
Stopping criteria implementation
Result stability verification
Computational efficiency optimization

Value at Risk (VaR) Calculation

Parametric VaR
Historical simulation VaR
Monte Carlo VaR
Expected shortfall (CVaR)
Marginal VaR
Incremental VaR

Confidence Interval Generation

Percentile-based intervals
Bootstrap confidence intervals
Prediction intervals
Tolerance intervals
One-sided bounds
Joint confidence regions

Crystal Ball/ModelRisk Integration

@RISK compatibility
Crystal Ball formula support
Model export capabilities
Simulation result import
Assumption synchronization
Report generation

Usage

Risk Quantification

Input: Key uncertain variables, probability distributions, correlations
Process: Run simulations, aggregate results, calculate risk metrics
Output: Probability distributions of outcomes, VaR, confidence intervals

Scenario Probability

Input: Model structure, variable ranges, target outcomes
Process: Simulate scenarios, identify conditions for targets
Output: Probability of achieving targets, key driver sensitivity