Enforcing an Admissible Parameter Space for Vector Multiplicative Error Models: The Fundamental Role of Matrix Inequality Constraints
推导了向量乘法误差模型的可容许参数空间，通过矩阵不等式显式表达，并采用约束最大似然估计确保参数合规，解决了以往无约束方法的局限，在金融波动建模的四个实证案例中验证了有效性。
Abstract We derive an admissible parameter space for vector multiplicative error models (vMEMs), explicitly formulating it in terms of the model’s matrix parameters through a set of matrix inequalities. Another key contribution is the adoption of constrained maximum likelihood estimation for the multivariate process, which ensures compliance with these matrix inequalities and addresses the limitations of unconstrained approaches used in previous studies. To demonstrate the effectiveness of the proposed method, we apply it to four empirical cases in financial volatility modeling, emphasizing its practical relevance.