The Cox-Ingersoll-Ross (CIR) model in layman’s terms…

The Cox-Ingersoll-Ross (CIR) model is a mathematical model used in finance to describe the evolution of interest rates over time. It's named after the economists John Cox, Jonathan Ingersoll, and Stephen Ross, who developed it in the early 1980s. The CIR model is a type of continuous-time model, specifically a stochastic differential equation (SDE), used for interest rate modeling.

Key features of the CIR model include:

1. Mean Reversion: The CIR model assumes that interest rates tend to revert to a long-term mean or equilibrium level over time. This feature is particularly useful for modeling short-term interest rates.

2. Volatility: The model accounts for the volatility of interest rates, which can vary stochastically over time. Volatility is a critical factor in interest rate modeling.

3. Square Root Process: The CIR model uses a square root process to describe the stochastic behavior of interest rates. This process prevents interest rates from becoming negative, which is a desirable characteristic for modeling.

Mathematically, the CIR model is represented by the following stochastic differential equation:

dr(t) = κ(θ - r(t)) dt + σ√(r(t)) dW(t)

Where:
- r(t) represents the short-term interest rate at time t.
- κ is the speed of mean reversion, determining how quickly interest rates revert to the mean.
- θ is the long-term mean or equilibrium interest rate.
- σ is the volatility parameter.
- dW(t) represents a Wiener process, which is a mathematical construct for modeling randomness.

The CIR model may be used in fixed income and options pricing to simulate interest rate movements and assess their impact on the valuation of financial instruments.

Let's assume the following parameter values for the CIR model:

- Long-term mean interest rate (θ): 5%
- Speed of mean reversion (κ): 0.1
- Volatility (σ): 0.2

We'll start with an initial short-term interest rate (r(0)) of 4%, and we want to simulate the interest rate over a short time interval, say, one year, with small time steps (e.g., daily).

Using the CIR model, you can simulate interest rate changes as follows:

1. Define the time step (Δt) and the number of time steps (N) for the simulation. For this example, let's use Δt = 1/365 (daily) and N = 365 (one year).

2. Initialize an array to store the simulated interest rates. Start with the initial rate: r(0) = 0.04.

3. For each time step (i from 1 to N):
- Calculate a random value from a standard normal distribution (dW(t)) for that time step.
- Update the interest rate using the CIR model:
r(t + Δt) = r(t) + κ(θ - r(t))Δt + σ√(r(t))dW(t)

4. Repeat step 3 for each time step.

The resulting interest rates will exhibit mean reversion and stochastic volatility, which are characteristic features of the CIR model.