Generalized Pareto processes and fund liquidity risk

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 cap...

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Bibliographic Details
Published inQuantitative finance Vol. 18; no. 8; pp. 1327 - 1343
Main Authors Desmettre, Sascha, de Kock, Johan, Ruckdeschel, Peter, Seifried, Frank Thomas
Format Journal Article
LanguageEnglish
Published Routledge 03.08.2018
Subjects
ARGP process
Data features
GPD
Liquidity risk
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Summary: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.
ISSN:1469-7688
1469-7696
DOI:10.1080/14697688.2017.1410214