Estimation of Continuous-Time Markov Processes Sampled at Random Time Intervals
提出一类广义矩估计方法，用于估计在随机时间间隔观测的连续时间马尔可夫过程的参数，证明了强相合性和渐近正态性，并应用于金融中的逐笔交易采样、跳跃扩散、机制转换扩散和反射扩散。
We introduce a family of generalized-method-of-moments estimators of the pa-rameters of a continuous-time Markov process observed at random time intervals. The results include strong consistency, asymptotic normality, and a characterization of standard errors. Sampling is at an arrival intensity that is allowed to depend on the underlying Markov process and on the parameter vector to be estimated. We focus on financial applications, including tick-based sampling, allowing for jump diffusions, regime-switching diffusions, and reflected diffusions.