Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters

Peter Nystrup, Henrik Madsen, Erik Lindström

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

27 Citeringar (SciVal)

Sammanfattning

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time-varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations.

Originalspråkengelska
Sidor (från-till)989-1002
TidskriftJournal of Forecasting
Volym36
Utgåva8
Tidigt onlinedatum2016 sep. 13
DOI
StatusPublished - 2017 dec.

Ämnesklassifikation (UKÄ)

  • Sannolikhetsteori och statistik

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