Integral Calculus and Differential Equations › Separation of Variables and Financial Models

Michael BrenndoerferOctober 28, 20255 min read
Quantitative FinanceAll chapters
Part 1 of 15Mathematical Foundations
Chapter 7Integral Calculus and Differential Equations › Separation of Variables and Financial Models
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For y′=h(t)g(y)y'=h(t)g(y), separation writes dy/g(y)=h(t) dtdy/g(y)=h(t)\,dt on intervals where g(y)≠0g(y)\ne0. 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:

dSdt=μS\frac{dS}{dt} = \mu S

where:

  • SS: stock price at time tt
  • μ\mu: drift rate (expected return per unit time)
  • dSdt\frac{dS}{dt}: rate of change of stock price

For the positive-price branch S0>0S_0>0, the proportionality to SS means percentage changes are constant: a $100 stock and a $200 stock both grow at the same percentage rate μ\mu. 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:

dSS=μ dt\frac{dS}{S} = \mu \, dt

Integrating:

ln⁡S=μt+C\ln S = \mu t + C S(t)=S0eμtS(t) = S_0 e^{\mu t}

Under the geometric Brownian motion convention dSt=μStdt+σStdWtdS_t=\mu S_tdt+\sigma S_tdW_t, this deterministic solution equals the conditional mean E[St∣S0]=S0eμt\mathbb{E}[S_t\mid S_0]=S_0e^{\mu t}. It is not a path that individual prices oscillate or mean-revert around. Log-price variance is σ2t\sigma^2t, and variance relative to the squared mean is eσ2t−1e^{\sigma^2t}-1, so these measures increase for nonzero σ\sigma. Absolute price variance need not increase for negative μ\mu. Deviations do not experience a restoring force.

Out[26]:
Visualization
Three exponential GBM conditional-mean curves from an initial price of 100; stochastic paths disperse rather than oscillate around these curves.
Deterministic ODE solutions for drift rates of 5%, 10%, and 15%. Under the GBM drift convention, each curve is the conditional mean E[S_t | S_0], not a mean-reversion path for individual prices.

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:

dVdt=−δV\frac{dV}{dt} = -\delta V

where:

  • VV: asset value at time tt
  • δ\delta: constant continuous decay rate, in inverse years; its finite one-year loss fraction is 1−e−δ1-e^{-\delta}
  • −δV-\delta V: 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 δ=−ln⁡(0.85)≈0.16252\delta=-\ln(0.85)\approx0.16252 per year, not δ=0.15\delta=0.15. The latter gives a one-year loss of about 13.93%. The continuous ODE has solution:

V(t)=V0e−δtV(t) = V_0 e^{-\delta t}

where V0V_0 is the initial asset value at t=0t = 0.

For V0>0V_0>0 and δ>0\delta>0, the asset value decays exponentially, with half-life t1/2=ln⁡(2)/δt_{1/2} = \ln(2)/\delta. The half-life formula follows directly from solving V0e−δt1/2=V0/2V_0 e^{-\delta t_{1/2}} = V_0/2, which gives e−δt1/2=1/2e^{-\delta t_{1/2}} = 1/2 and thus t1/2=ln⁡(2)/δt_{1/2} = \ln(2)/\delta. 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.

In[27]:
Code
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)
Out[28]:
Visualization
Line chart showing exponential decay of asset values over 30 years for equipment, vehicles, and buildings.
Illustrative continuous depreciation at the assumed rates in the legend. Under these parameters, equipment declines fastest and buildings retain value longest; this is not an empirical asset-class comparison.
Out[29]:
Console
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