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Generalized Pareto processes and fund liquidity risk

Author

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  • Sascha Desmettre
  • Johan de Kock
  • Peter Ruckdeschel
  • Frank Thomas Seifried

Abstract

Motivated by the modelling of liquidity risk in fund management in a dynamic setting, we propose and investigate a class of time series models with generalized Pareto marginals: the autoregressive generalized Pareto process (ARGP), a modified ARGP and a thresholded ARGP. These models are able to capture key data features apparent in fund liquidity data and reflect the underlying phenomena via easily interpreted, low-dimensional model parameters. We establish stationarity and ergodicity, provide a link to the class of shot-noise processes, and determine the associated interarrival distributions for exceedances. Moreover, we provide estimators for all relevant model parameters and establish consistency and asymptotic normality for all estimators (except the threshold parameter, which is to be estimated in advance). Finally, we illustrate our approach using real-world fund redemption data, and we discuss the goodness-of-fit of the estimated models.

Suggested Citation

  • Sascha Desmettre & Johan de Kock & Peter Ruckdeschel & Frank Thomas Seifried, 2018. "Generalized Pareto processes and fund liquidity risk," Quantitative Finance, Taylor & Francis Journals, vol. 18(8), pages 1327-1343, August.
    • Handle: RePEc:taf:quantf:v:18:y:2018:i:8:p:1327-1343
    DOI: 10.1080/14697688.2017.1410214
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    Cited by:

    1. Darolles, Serge & Fol, Gaëlle Le & Lu, Yang & Sun, Ran, 2019. "Bivariate integer-autoregressive process with an application to mutual fund flows," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 181-203.

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