Cox-Ingersoll-Ross (CIR) Model

class torchquantlib.models.interest_rate.cir.CIR(kappa_init=0.1, theta_init=0.05, sigma_init=0.01, r0_init=0.03)

Bases: StochasticModel

Cox-Ingersoll-Ross (CIR) interest rate model.

This model describes the evolution of interest rates using the following stochastic differential equation: dr = κ(θ - r)dt + σ√r dW

where: r is the interest rate κ (kappa) is the speed of mean reversion θ (theta) is the long-term mean level σ (sigma) is the volatility W is a Wiener process

__init__(kappa_init=0.1, theta_init=0.05, sigma_init=0.01, r0_init=0.03)

Initialize the CIR model.

Parameters:

• kappa_init (float) – Initial value for speed of mean reversion.

• theta_init (float) – Initial value for long-term mean level.

• sigma_init (float) – Initial value for volatility.

• r0_init (float) – Initial interest rate.

simulate(S0, T, N, steps=100)

Simulate interest rate paths using the CIR model.

Parameters:

• S0 (float) – Initial asset price (not used in this model, included for consistency).

• T (float) – Time horizon for simulation.

• N (int) – Number of simulation paths.

• steps (int) – Number of time steps in each path.

Returns:

Simulated interest rates at time T.

Return type:

torch.Tensor

_apply_constraints()

Apply constraints to model parameters to ensure they remain in valid ranges.

simulate(S0, T, N, steps=100)

Simulate interest rate paths using the CIR model.

Parameters:

• S0 (float) – Initial asset price (not used in this model, included for consistency).

• T (float) – Time horizon for simulation.

• N (int) – Number of simulation paths.

• steps (int) – Number of time steps in each path.

Returns:

Simulated interest rates at time T.

Return type:

torch.Tensor

Class Methods

__init__(kappa_init=0.1, theta_init=0.05, sigma_init=0.01, r0_init=0.03)

Initialize the CIR model.

param float kappa_init:

Initial value for speed of mean reversion.

param float theta_init:

Initial value for long-term mean level.

param float sigma_init:

Initial value for volatility.

param float r0_init:

Initial interest rate.

simulate(S0, T, N, steps=100)

Simulate interest rate paths using the CIR model.

param float S0:

Initial asset price (not used in this model, included for consistency).

param float T:

Time horizon for simulation.

param int N:

Number of simulation paths.

param int steps:

Number of time steps in each path.

return:

Simulated interest rates at time T.

rtype:

torch.Tensor

_apply_constraints()

Apply constraints to model parameters to ensure they remain in valid ranges.