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Multivariate AutoRegressive Smooth Liquidity (MARSLiQ)

Author

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  • Hafner, C. M.
  • Linton, O. B.
  • Wang, L.

Abstract

We propose MARSLiQ (Multivariate AutoRegressive Smooth Liquidity), a new multivariate model for daily liquidity that combines slowly evolving trends with short-run dynamics to capture both persistent and transitory liquidity movements. In our framework, each asset's liquidity is decomposed into a smooth time-varying trend component and a stationary short-run component, allowing us to separate long-term liquidity levels from short-term fluctuations. The trend for each asset is estimated nonparametrically and further decomposed into a common market trend and idiosyncratic (asset-specific) trends, and seasonal trends, facilitating interpretation of market-wide liquidity shifts versus firm-level effects. We introduce a novel dynamic structure in which an asset's short-run liquidity is driven by its own past liquidity as well as by lagged liquidity of a broad liquidity index (constructed from all assets). This parsimonious specification-combining asset-specific autoregressive feedback with index-based spillovers-makes the model tractable even for high-dimensional systems, while capturing rich liquidity spillover effects across assets. Our model's structure enables a clear analysis of permanent vs. transitory liquidity shocks and their propagation throughout the market. Using the model's Vector MA representation, we perform forecast error variance decompositions to quantify how shocks to one asset's liquidity affect others over time, and we interpret these results through network connectedness measures that map out the web of liquidity interdependence across assets.

Suggested Citation

  • Hafner, C. M. & Linton, O. B. & Wang, L., 2025. "Multivariate AutoRegressive Smooth Liquidity (MARSLiQ)," Cambridge Working Papers in Economics 2569, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2569
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    References listed on IDEAS

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    1. Fabrizio Cipollini & Robert F. Engle & Giampiero M. Gallo, 2006. "Vector Multiplicative Error Models: Representation and Inference," NBER Technical Working Papers 0331, National Bureau of Economic Research, Inc.
    2. Engle, Robert F. & Campos-Martins, Susana, 2023. "What are the events that shake our world? Measuring and hedging global COVOL," Journal of Financial Economics, Elsevier, vol. 147(1), pages 221-242.
    3. Tarun Chordia, 2005. "An Empirical Analysis of Stock and Bond Market Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 18(1), pages 85-129.
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    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
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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