Class AhatEstimation


  • public class AhatEstimation
    extends Object
    Estimates the coefficient of a VAR(1) model by penalized maximum likelihood. The vector autoregressive process is: \[ S_{t}=S_{t-1}A+Z_{t} \] \(S_{t}\) is the portfolio process, \(A\) the coefficient matrix, and \(Z_{t}\) a vector of i.i.d. Gaussian noise. The penalized maximum likelihood problem is (eq. 15 in the reference): \[ a_{i}=\arg\min_{x} ||S_{it}-S_{t-1}x||^{2}+\gamma ||x||_{1} \] where \(a_{i}\) is the i-th column of \(A\).
    • Constructor Detail

      • AhatEstimation

        public AhatEstimation​(Matrix St,
                              Matrix St_m1,
                              double gamma)
        Estimates the coefficient matrix of a vector autoregressive process of order 1.
        Parameters:
        St - the portfolio price process
        St_m1 - the lagged portfolio price process
        gamma - the penalty
    • Method Detail

      • compute

        public Matrix compute()