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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.