Combining long memory and level shifts in modelling and forecasting the volatility of asset returns

DOI10.1080/14697688.2017.1329591zbMath1406.62151OpenAlexW3125360608MaRDI QIDQ4554429

Pierre Perron, Rasmus T. Varneskov

Publication date: 14 November 2018

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://hdl.handle.net/2144/26709

zbMATH Keywords

stochastic volatilityKalman filterlong memory processesforecastingstructural changestate space modelling

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Economic time series analysis (91B84)

Related Items (6)

A multivariate test against spurious long memory ⋮ An integrated heteroscedastic autoregressive model for forecasting realized volatilities ⋮ Modified local Whittle estimator for long memory processes in the presence of low frequency (and other) contaminations ⋮ Consistent inference for predictive regressions in persistent economic systems ⋮ Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model ⋮ Testing for parameter instability and structural change in persistent predictive regressions

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