The model specifies that the instantaneous interest rate follows the stochastic differential equation
$$\mathrm{d}r_t = a(b-r_t)\: \mathrm{d}t + \sigma \: \mathrm{d}W_t$$
where $W_{t}$ is a Wiener process under the risk neutral framework modelling the random market risk factor, in that it models the continuous inflow of randomness into the system. The standard deviation parameter, $\sigma$, determines the Volatility (finance) of the interest rate and in a way characterizes the amplitude of the instantaneous randomness inflow. The typical parameters b, a and $\sigma$, together with the initial condition $r_0$, completely characterize the dynamics, and can be quickly characterized as follows, assuming a to be non-negative:
- $b$: "long term mean level". All future trajectories of $r$ will evolve around a mean level b in the long run;
- a: "speed of reversion". a characterizes the velocity at which such trajectories will regroup around b in time;
- $\sigma$: "instantaneous volatility", measures instant by instant the amplitude of randomness entering the system. Higher $\sigma$ implies more randomness
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What is the mathematical reasoning behind this formula for the finance professional to introduce this?