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- W1505618429 abstract "We introduce the class of FIR-GARCH models in this paper. FIR-GARCH models provide a parsimonious joint model for low-frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a dataset composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FIR-GARCH models in terms of in-sample fit, out-of-sample fit, and forecasting accuracy compared to classical and Realized GARCH models." @default.
- W1505618429 created "2016-06-24" @default.
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- W1505618429 date "2015-01-01" @default.
- W1505618429 modified "2023-09-23" @default.
- W1505618429 title "Flogarch: Realizing Long Memory and Asymmetries in Returns Volatility" @default.
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- W1505618429 doi "https://doi.org/10.2139/ssrn.2559052" @default.
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