Mota, Pedro, and Manuel L. Esquível. "
Pseudo Maximum Likelihood and Moments Estimators for Some Ergodic Diffusions."
Contributions to Statistics. Springer International Publishing, 2018. 335-343.
AbstractWhen (Xt)t≥0 is an ergodic process, the density function of Xt converges to some invariant density as t →∞. We will compute and study some asymptotic properties of pseudo moments estimators obtained from this invariant density, for a specific class of ergodic processes. In this class of processes we can find the Cox-Ingersoll & Ross or Dixit & Pindyck processes, among others. A comparative study of the proposed estimators with the usual estimators obtained from discrete approximations of the likelihood function will be carried out.
Mota, Pedro. "
On a Continuous-Time Stock Price Model with Two Mean Reverting Regimes."
Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications. Eds. João Lita da Silva, Frederico Caeiro, Isabel Natário, and Carlos A. Braumann. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. 297-305.
AbstractMotivated by the need to describe regime switching in stock prices, we introduce and study a stochastic process in continuous time with two regimes and one threshold driving the change in regimes. When the difference between the regimes is simply given by different sets of real-valued parameters for the drift and diffusion coefficients, we show that there are consistent estimators for the threshold as long as we know how to classify a given observation of the process as belonging to one of the two regimes.