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Modelling persistent stationary processes in continuous time
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DOI：10.1016/j.econmod.2022.105776.png)
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摘要
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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.
Keyword:
Persistency
Stationarity
Diffusion
Markov chain
Forecasting
VaR
期刊
IF:
4.7
论文数:
6.6K
被引数:
1.6W
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