Why RangeSight Uses Real Market Volatility

The Problem With Constant Volatility

Most risk-of-ruin calculators assume each trade has the same probability distribution — constant win rate, constant loss size. This is convenient but empirically wrong. The Nobel Prize-winning work of Robert Engle (ARCH model, *Econometrica*, 1982) and Tim Bollerslev (GARCH model, *Journal of Econometrics*, 1986) proved that financial markets exhibit volatility clustering: high-volatility periods follow high-volatility periods, and low-volatility follows low-volatility.

Real markets have:

• Volatility clustering: Periods of high volatility persist for weeks or months
• Fat tails: Extreme moves happen 4–6x more often than normal distributions predict (Mandelbrot, *The Misbehavior of Markets*, 2004)
• Regime changes: A strategy that works in low-vol markets may fail catastrophically in high-vol

How RangeSight Handles This

When you select a ticker in RangeSight, the simulation incorporates that stock's actual historical volatility profile. This means:

• Your Monte Carlo paths reflect real market conditions, not textbook assumptions
• Drawdown estimates account for volatility clustering
• Survival probability is calibrated to actual risk, not theoretical risk

Why This Matters

A strategy tested with constant 1% daily volatility looks very different when tested against TSLA's actual volatility profile (which regularly exceeds 3–4% daily moves). According to CBOE data (2023), the average annualized realized volatility for S&P 500 stocks is approximately 25%, but individual stocks range from 15% to over 80%.

The difference between assumed and real volatility can change your ruin probability by 10–20 percentage points.

Try It Yourself

Run the same strategy against SPY and TSLA in RangeSight to see how ticker selection changes your risk profile. For a simplified version, try the free Risk of Ruin Calculator.