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Predicting daily probability distributions of S&P500 returns
Author(s) -
Weigend Andreas S.,
Shi Shanming
Publication year - 2000
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/1099-131x(200007)19:4<375::aid-for779>3.0.co;2-u
Subject(s) - autoregressive conditional heteroskedasticity , hidden markov model , econometrics , markov chain , conditional probability distribution , computer science , conditional probability , competitor analysis , series (stratigraphy) , statistics , sample (material) , mathematics , artificial intelligence , machine learning , economics , volatility (finance) , paleontology , chemistry , management , chromatography , biology
This paper presents ‘hidden Markov experts’, a framework for predicting conditional probability distributions of future values of a time series. On daily S&P500 data, the out‐of‐ sample performance is compared to several baselines including GARCH and ‘gated experts’. The evaluation of the full density shows improvement over all competitors. Since the performance for point‐predictions is comparable to the other methods, the main advantage of hidden Markov experts is their use for conditional density forecasting. Copyright © 2000 John Wiley & Sons, Ltd.