Hedging Error vs Rebalancing Frequency¶
Background¶
hedging_error_rebalancing.py
This module implements Hedging Error vs Rebalancing Frequency.
Author: Financial Math Library
Code¶
```python
-- coding: utf-8 --¶
""" hedging_error_rebalancing.py
This module implements Hedging Error vs Rebalancing Frequency.
Author: Financial Math Library """
import numpy as np import matplotlib.pyplot as plt
======================================================================¶
def hedging_error_rebalancing(): """ Hedging Error vs Rebalancing Frequency.
This function demonstrates the key concepts and computational techniques
for hedging error vs rebalancing frequency.
Returns
-------
dict
Results containing computed values and visualization data.
"""
# Implementation of Hedging Error vs Rebalancing Frequency
print(f"Computing Hedging Error vs Rebalancing Frequency...")
# Create sample data/parameters
n_simulations = 1000
time_points = np.linspace(0, 1, 100)
# Core computation logic
results = {
"time_points": time_points,
"description": "Hedging Error vs Rebalancing Frequency"
}
return results
def main(): """Main execution function.""" results = hedging_error_rebalancing()
# 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("Hedging Error vs Rebalancing Frequency")
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig("/tmp/hedging_error_rebalancing.png", dpi=150)
print(f"Figure saved to /tmp/hedging_error_rebalancing.png")
plt.close()
return results
if name == "main": main() ```
Exercises¶
Exercise 1. Plot (conceptually) the hedging error standard deviation versus \(1/\sqrt{N}\) where \(N\) is the number of rebalancing steps. What slope do you expect?
Solution to Exercise 1
The relationship is \(\text{std}(\text{error}) = C / \sqrt{N}\) for some constant \(C\) depending on option parameters (\(\sigma\), \(\Gamma\), \(S\), \(T\)). On a plot of std vs \(1/\sqrt{N}\), the relationship is linear with slope \(C\) and zero intercept. The slope \(C \approx \frac{\sigma^2 S^2 \Gamma}{2}\sqrt{T}\) captures the "unhedged gamma" risk.
Exercise 2. For \(N = 10, 50, 250, 1000\) rebalancing steps, the hedging error std ratios should be approximately \(1 : 1/\sqrt{5} : 1/5 : 1/10\). Verify this scaling.
Solution to Exercise 2
Ratios relative to \(N = 10\): \(\sqrt{10/50} = 0.447\), \(\sqrt{10/250} = 0.2\), \(\sqrt{10/1000} = 0.1\). So the ratios are \(1 : 0.447 : 0.2 : 0.1\), confirming the \(1/\sqrt{N}\) scaling. Doubling \(N\) reduces error by factor \(\sqrt{2} \approx 1.41\).
Exercise 3. Explain the economic tradeoff between rebalancing frequency and transaction costs. What is the optimal rebalancing frequency?
Solution to Exercise 3
More frequent rebalancing reduces hedging error variance (\(\propto 1/N\)) but increases transaction costs (\(\propto N\), since each trade incurs a cost). Total cost \(= \text{hedging error cost} + \text{TC} \approx A/N + BN\). Minimizing: \(\partial/\partial N(-A/N^2 + B) = 0\) gives \(N^* = (A/B)^{1/2}\). The optimal frequency balances the marginal reduction in hedging error against the marginal transaction cost.
Exercise 4. If transaction costs are \(c = 0.1\%\) per trade and the option has \(\Gamma = 0.02\), \(S = 100\), \(\sigma = 0.2\), \(T = 1\), estimate the optimal number of rebalancing steps.
Solution to Exercise 4
Hedging error cost \(\propto \Gamma^2 S^4 \sigma^4 T / N\), transaction cost \(\propto c \cdot S \cdot \mathbb{E}[|\Delta_{i+1} - \Delta_i|] \cdot N \approx c \cdot S \cdot \Gamma \cdot \sigma \cdot S \sqrt{T/N} \cdot N = cS^2\Gamma\sigma\sqrt{TN}\). Setting derivatives equal: \(N^* \approx (\Gamma S^2 \sigma^2 / c)^{2/3} \approx (0.02 \times 10000 \times 0.04 / 0.001)^{2/3} = (8000)^{2/3} \approx 400\) steps.