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I'm new to Quantitative Finance. I've data which I need to fit a CIR model and estimate its parameters.

$ dX_{t+1} = a(b-X_{t})dt + \sigma \sqrt{X_t}dW_{t} $

While I can fit and obtain parameparameterates using Euler-maurayana discretization and then linear regression closed form solution, I'd like to explore better approaches. This method was quite easy as after discretizing it, it became:

$ \frac{S_{t+1}-S{t}}{\sqrt S_{t}} = \frac{ab\Delta t}{\sqrt{S_{t}}} - a\sqrt{S_{t}}\Delta t + \sigma \sqrt{\Delta t} \epsilon_{t} $

where $ \epsilon_{t}$ is Normal(0,1)

I estimated a and b through OLS and got sigma estimate through error variance. I'd like to estimate parameters of this CIR model through better discretizing approaches. I tried discretizing the model using Milstein and Prediction and Error Correction Method. But both aren't in traditional linear regression form (either there are nonlinear terms or error terms aren't just normal). I'm not aware of how to proceed from here. Since there's stochastic component here, I don't know how to estimate these parameters (including sigma) with approaches (gradient descent or something).

Could someone kindly please help?

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  • $\begingroup$ Have you tried simple maximum likelihood? $\endgroup$
    – Kevin
    Commented Oct 30, 2022 at 10:45
  • $\begingroup$ I tried for euler maurayana discretization and got mle estimates. However, I found the math behind it a little intimidating for my level. $\endgroup$
    – Vignesh
    Commented Oct 30, 2022 at 12:23
  • $\begingroup$ Use the transformation $y =\sqrt{x}$ and obtain the transformed SDE for which Milstein and Euler schemes are the same. $\endgroup$
    – Nick
    Commented Nov 20, 2022 at 9:28

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