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Mathematics of Risk

Black-Scholes-Merton

An interactive exploration of option pricing, probability distributions, volatility, time value and the Greeks. Change the inputs and watch the mathematics change in real time.

From uncertainty to an option price

The Black-Scholes model connects probability distributions, logarithms, volatility, discounting and risk. Its central question is: what is a fair price for an option before it expires?

1. Model the stock

The future stock price is treated as a lognormally distributed random variable.

2. Define the payoff

A call benefits above the strike; a put benefits below it.

3. Price uncertainty

Risk-neutral pricing converts possible outcomes into a present value.

The assumptions

Constant volatility

Volatility is assumed known and constant.

Constant risk-free rate

The continuously compounded rate is constant.

Lognormal prices

Continuously compounded returns are normally distributed.

No arbitrage

Prices must be consistent with no-arbitrage relationships.

Continuous trading

The theoretical model permits continuous adjustment.

European exercise

The closed-form formula assumes exercise only at expiry.

The Black-Scholes equation

∂V/∂t + ½σ²S² ∂²V/∂S² + rS ∂V/∂S − rV = 0

This partial differential equation describes how the option value changes through time and with the underlying asset.

European call

C = S₀N(d₁) − Ke−rTN(d₂)

European put

P = Ke−rTN(−d₂) − S₀N(−d₁)
d₁ = [ln(S₀/K) + (r − q + ½σ²)T] / (σ√T)    d₂ = d₁ − σ√T

Interactive option-pricing laboratory

These controls drive all the visualisations on the page.

Option value€0.00
Delta0.000
Gamma0.000
Vega0.000
Theta / year0.000
Rho0.000
d₁0.000
d₂0.000

Option value versus stock price

The nonlinear curve shows the value of the option before expiry. The strike and current stock price are marked.

Payoff at expiry

At expiry there is no remaining time value. The call payoff is max(S − K, 0); the put payoff is max(K − S, 0).

Watching time value disappear

As expiry approaches, option value converges toward the expiry payoff.

The effect of volatility

Higher uncertainty increases the value of both calls and puts because the holder's downside is limited while upside remains open.

The risk-neutral distribution

The model uses a lognormal distribution for the stock price at expiry. The distribution cannot extend below zero and has a right-hand tail.

This is the risk-neutral pricing distribution, not necessarily a forecast of the stock's real-world expected return.

The Greeks

The Greeks turn one option price into a map of its sensitivities.

Delta

Gamma

Vega

Theta

Rho

Three-dimensional option-value surface

Drag the surface to rotate it. The first surface shows option value against stock price and volatility; the second replaces volatility with time.

The surface is rendered directly in the browser without an external charting library.

From Black-Scholes to Merton

The dividend-yield extension incorporates a continuously paid dividend yield q.

C = S₀e−qTN(d₁) − Ke−rTN(d₂)
The main calculator already includes q, so setting a dividend yield above zero uses the continuous-dividend Black-Scholes-Merton formulation.

Implied volatility

We can run the model backwards: given a market option price, what volatility would make the Black-Scholes-Merton price equal that market price?

Implied volatility—

Where the model breaks down

Volatility smile

Different strikes can imply different volatilities.

Changing volatility

Market volatility varies through time.

Jumps

Prices can move discontinuously after shocks.

Transaction costs

Real markets have spreads, fees and funding costs.

American exercise

Early exercise requires a different treatment.

Fat tails

Financial returns often have heavier tails than a simple normal model.

Black-Scholes is best understood as a powerful benchmark and framework rather than a literal description of every market.

The wider risk journey

Probability

Normal and lognormal distributions

Uncertainty

Volatility and correlation

Derivatives

Nonlinear payoffs and option pricing

Natural next steps include Monte Carlo option pricing, stochastic volatility, volatility surfaces and jump-diffusion models.