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Are low-frequency data really uninformative? A forecasting combination perspective · Study

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ArticleAre low-frequency data really uninformative? A forecasting combination perspective

Feng Ma2017

Article2017

Are low-frequency data really uninformative? A forecasting combination perspective

Feng Ma · Li Yu · Li Liu · Yaojie Zhang

36 citations·0/0 files stored·The North American Journal of Economics and Finance

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Financial Risk and Volatility Modeling Forecasting Techniques and Applications Stock Market Forecasting Methods

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 2003Modeling and Forecasting Realized Volatility 1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts 1993A long memory property of stock market returns and a new model 1969The Combination of Forecasts

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2022A novel approach to using modern portfolio theory 2019Forecasting stock market returns by combining sum-of-the-parts and ensemble empirical mode decomposition 2018Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?2019Forecasting the Chinese stock volatility across global stock markets

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Year2017

Typearticle

Published2017-12-06

VenueThe North American Journal of Economics and Finance

ISSN-L1062-9408

Languageen

Citations36

Files0 public / 0

ShelfFinancial Risk and Volatility Modeling

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doi10.1016/j.najef.2017.11.006

openalex_workW2772901722

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