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Modelling persistent stationary processes in continuous time

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  • Jeong, Minsoo

Abstract

This paper presents a novel approach to model continuous time processes that capture two countervailing features of many financial time series: persistency and long term stationarity. The processes introduced by our models exhibit persistent behaviors observed typically in non-stationary time series, but they remain stationary instead of tending towards explosive paths. We provide a relevant statistical theory and its implications on inference and forecasting, presenting both simulation evidence and empirical backing for the existence of, as well as the characteristic behavior for, such a series in real financial time series data.

Suggested Citation

  • Jeong, Minsoo, 2022. "Modelling persistent stationary processes in continuous time," Economic Modelling, Elsevier, vol. 109(C).
    • Handle: RePEc:eee:ecmode:v:109:y:2022:i:c:s0264999322000220
    DOI: 10.1016/j.econmod.2022.105776
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    References listed on IDEAS

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    More about this item

    Keywords

    Persistency; Stationarity; Diffusion; Markov chain; Forecasting; VaR;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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