Dynamic Autoregressive Liquidity (DArLiQ)
提出一类半参数动态自回归模型来刻画Amihud非流动性指标，同时捕捉长期趋势和短期动态，并开发了GMM和半参数ML估计量，应用于S&P 500指数研究流动性成分与股票风险溢价的关系。
We introduce a new class of semiparametric dynamic autoregressive models for the Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a generalized method of moments (GMM) estimator based on conditional moment restrictions and an efficient semiparametric maximum likelihood (ML) estimator based on an iid assumption. We derive large sample properties for our estimators. Finally, we demonstrate the model fitting performance and its empirical relevance on an application. We investigate how the different components of the illiquidity process obtained from our model relate to the stock market risk premium using data on the S&P 500 stock market index.