Integral Calculus and Differential Equations › Separation of Variables and Financial Models
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For , separation writes on intervals where . Integrating that branch does not recover excluded equilibrium solutions, which must be checked in the original equation.
Geometric Brownian Motion (Deterministic Part)
The deterministic component of the geometric Brownian motion model for stock prices is:
where:
- : stock price at time
- : drift rate (expected return per unit time)
- : rate of change of stock price
For the positive-price branch , the proportionality to means percentage changes are constant: a $100 stock and a $200 stock both grow at the same percentage rate . Percentage returns make changes comparable across scales, while dollar profit and loss still matter for position sizing, liabilities, and risk limits. A $10 gain on a $100 stock is a 10% return, whereas the same dollar gain on a $1,000 stock is a 1% return. Separating variables:
Integrating:
Under the geometric Brownian motion convention , this deterministic solution equals the conditional mean . It is not a path that individual prices oscillate or mean-revert around. Log-price variance is , and variance relative to the squared mean is , so these measures increase for nonzero . Absolute price variance need not increase for negative . Deviations do not experience a restoring force.

Decay Models: Asset Depreciation
Many vehicles and pieces of equipment lose economic value with age and use, although some physical assets can appreciate. A simple depreciation model assumes value declines in proportion to its current level:
where:
- : asset value at time
- : constant continuous decay rate, in inverse years; its finite one-year loss fraction is
- : negative because value decreases over time
Assets sharing a proportional decay rate lose the same percentage over a fixed interval, while a larger starting value gives a larger dollar loss. A discrete 15% annual loss removes $4,500 from $30,000 or $2,250 from $15,000. In the continuous model that annual loss corresponds to per year, not . The latter gives a one-year loss of about 13.93%. The continuous ODE has solution:
where is the initial asset value at .
For and , the asset value decays exponentially, with half-life . The half-life formula follows directly from solving , which gives and thus . Zero value is a separate equilibrium; zero decay has no finite halving time. The following rates are illustrative continuous parameters, not estimated asset-class depreciation rates.
import numpy as np
# Compare depreciation rates
V0 = 100000 # Initial asset value ($100,000)
depreciation_rates = {
"Equipment (20%/year)": 0.20,
"Vehicle (15%/year)": 0.15,
"Building (3%/year)": 0.03,
}
t = np.linspace(0, 30, 300)
asset_values = {}
for name, delta in depreciation_rates.items():
asset_values[name] = V0 * np.exp(-delta * t)
Asset Values After 10 Years: -------------------------------------------------- Equipment (20%/year): Value: $13,533.53 Half-life: 3.5 years Vehicle (15%/year): Value: $22,313.02 Half-life: 4.6 years Building (3%/year): Value: $74,081.82 Half-life: 23.1 years