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Stock returns and long-range dependence
Odonkor, Alexander Ayertey; Ababio, Emmanuel Nkrumah; … - In: Global business review 23 (2022) 1, pp. 37-47
Persistent link: https://www.econbiz.de/10012822065
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Stock index volatility forecasting with high frequency data
Hol Uspensky, Eugenie; Koopman, Siem Jan - 2002
Volatility (SV) and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models which are both extended to include … outperforms the GARCH model. …
Persistent link: https://www.econbiz.de/10011326944
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Bayesian model averaging and the conditional volatility process : an application to predicting aggregate equity returns by conditioning on economic variables
Nonejad, Nima - In: Quantitative finance 21 (2021) 8, pp. 1387-1411
Persistent link: https://www.econbiz.de/10012608655
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Using the conditional volatility channel to improve the accuracy of aggregate equity return predictions
Nonejad, Nima - In: Empirical economics : a quarterly journal of the … 61 (2021) 2, pp. 973-1009
Persistent link: https://www.econbiz.de/10012616915
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Three essays on predictability and seasonality in the cross-section of stock returns
Bogousslavsky, Vincent - 2017
Persistent link: https://www.econbiz.de/10011903110
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Cross-sectional factor dynamics and momentum returns
Avramov, Doron; Hore, Satadru - In: Journal of financial markets 32 (2017), pp. 69-96
Persistent link: https://www.econbiz.de/10011814969
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The real-life performance of market timing with moving average and time-series momentum rules
Zakamulin, Valeriy - In: The journal of asset management 15 (2014) 4, pp. 261-278
Persistent link: https://www.econbiz.de/10010476240
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The Role of High-Frequency Prices, Long Memory and Jumps for Value-at-Risk Prediction
Fuertes, Ana-Maria - 2012
intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information … risk. ARFIMA models produce better backtesting results than GARCH models but fare worse in terms of independence of the … hits sequence. Encompassing tests further suggest that GARCH and ARFIMA models can be fruitfully combined to produce more …
Persistent link: https://www.econbiz.de/10013105936
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Modeling and forecasting S&P 500 volatility : long memory, structural breaks and nonlinearity
Martens, Martin; Dijk, Dick van; Pooter, Michiel de - 2004
The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week...
Persistent link: https://www.econbiz.de/10011335205
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Dynamic mixture models for financial time series
Haas, Markus - 2004 - 1. Aufl.
series, namely, mixed normal and Markov-switching GARCH models. In the second part of the study, we also consider …
Persistent link: https://www.econbiz.de/10002452594