Dynamic Autoregressive Liquidity (DArLiQ)

Linton, O. B. Wang, L.

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: 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 GMM estimator based on conditional moment restrictions and an efficient semiparametric ML estimator based on an i.i.d. assumption. We derive large sample properties for our estimators. We further develop a methodology to detect the occurrence of permanent and transitory breaks in the illiquidity process. Finally, we demonstrate the model performance and its empirical relevance on two applications. First, we study the impact of stock splits on the illiquidity dynamics of the five largest US technology company stocks. Second, 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.

Keywords: Kernel; Nonparametric Estimation; Semiparametric Model; Stock Splits; Structural Change (search for similar items in EconPapers)
JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 2022-02-23
New Economics Papers: this item is included in nep-cwa, nep-ecm, nep-mst and nep-ore
Note: obl20
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Dynamic Autoregressive Liquidity (DArLiQ) (2022)
Working Paper: Dynamic Autoregressive Liquidity (DArLiQ) (2022)
Working Paper: Dynamic Autoregressive Liquidity (DArLiQ) (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2214

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