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Volatility Misspecification Impact

Background

volatility_misspecification_impact.py

This module implements Volatility Misspecification Impact.

Author: Financial Math Library


Code

```python

-- coding: utf-8 --

""" volatility_misspecification_impact.py

This module implements Volatility Misspecification Impact.

Author: Financial Math Library """

import numpy as np import matplotlib.pyplot as plt

======================================================================

def volatility_misspecification_impact(): """ Volatility Misspecification Impact.

This function demonstrates the key concepts and computational techniques
for volatility misspecification impact.

Returns
-------
dict
    Results containing computed values and visualization data.
"""
# Implementation of Volatility Misspecification Impact
print(f"Computing Volatility Misspecification Impact...")

# Create sample data/parameters
n_simulations = 1000
time_points = np.linspace(0, 1, 100)

# Core computation logic
results = {
    "time_points": time_points,
    "description": "Volatility Misspecification Impact"
}

return results

def main(): """Main execution function.""" results = volatility_misspecification_impact()

# Create visualization
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(results["time_points"], "b-", linewidth=2)
ax.set_xlabel("Time")
ax.set_ylabel("Value")
ax.set_title("Volatility Misspecification Impact")
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig("/tmp/volatility_misspecification_impact.png", dpi=150)
print(f"Figure saved to /tmp/volatility_misspecification_impact.png")
plt.close()

return results

if name == "main": main() ```

Exercises

Exercise 1. If the true volatility is \(\sigma_{\text{true}} = 0.25\) but you hedge using \(\sigma_{\text{hedge}} = 0.20\), is the hedging P&L biased? In which direction?

Solution to Exercise 1

Yes, the P&L is biased. The hedger underestimates volatility, so they underhedge gamma exposure. The BS PDE gives the expected P&L as \(\frac{1}{2}(\sigma_{\text{true}}^2 - \sigma_{\text{hedge}}^2)S^2\Gamma\Delta t > 0\) per step. The hedger systematically profits because they sold the option at a price computed with higher \(\sigma\) than they hedge with. However, this profit comes with increased variance.


Exercise 2. Derive the instantaneous hedging P&L when the hedge volatility differs from the realized volatility.

Solution to Exercise 2

The hedging P&L over \([t, t+dt]\) is: \(d\Pi = \frac{1}{2}(\sigma_{\text{realized}}^2 - \sigma_{\text{hedge}}^2)S^2\Gamma_{\text{hedge}}\,dt\). This follows from the BS PDE: the option value satisfies the PDE with \(\sigma_{\text{realized}}\), while the hedge portfolio evolves with \(\sigma_{\text{hedge}}\). The residual is the gamma-weighted difference in variance.


Exercise 3. If \(\sigma_{\text{true}} = 0.3\) and \(\sigma_{\text{hedge}} = 0.2\), compute the expected annual P&L per unit gamma for \(S = 100\).

Solution to Exercise 3

Expected P&L \(= \frac{1}{2}(0.09 - 0.04)(10000)\Gamma \times 1 = 250\Gamma\) per year per share. For \(\Gamma = 0.02\): expected P&L \(= 250 \times 0.02 = \$5\) per year. This is the "variance risk premium" captured by selling options at implied vol (20%) and hedging while realized vol is higher (30%).


Exercise 4. Explain why selling options at high implied volatility and hedging at realized volatility is a common strategy, and what risks remain.

Solution to Exercise 4

If implied vol consistently exceeds realized vol (the "variance risk premium"), selling options and delta-hedging locks in the difference. The strategy earns \(\frac{1}{2}(\sigma_{\text{implied}}^2 - \sigma_{\text{realized}}^2)S^2\Gamma\) on average. Risks: (1) realized vol may occasionally spike above implied vol (tail events); (2) the P&L path is volatile even when the average is positive; (3) model risk from stochastic volatility not captured by BS; (4) jump risk creates sudden large losses.