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Dynamic Autoregressive Liquidity (DArLiQ)

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  • Christian M. Hafner
  • Oliver B. Linton
  • Linqi Wang

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 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.

Suggested Citation

  • Christian M. Hafner & Oliver B. Linton & Linqi Wang, 2024. "Dynamic Autoregressive Liquidity (DArLiQ)," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 774-785, April.
    • Handle: RePEc:taf:jnlbes:v:42:y:2024:i:2:p:774-785
    DOI: 10.1080/07350015.2023.2238790
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    References listed on IDEAS

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    1. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function Is Not Smooth," Econometrica, Econometric Society, vol. 71(5), pages 1591-1608, September.
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    Cited by:

    1. Bachmair, K., 2023. "The Effects of the LIBOR Scandal on Volatility and Liquidity in LIBOR Futures Markets," Cambridge Working Papers in Economics 2303, Faculty of Economics, University of Cambridge.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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