GARHCX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables
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- Fryzlewicz, Piotr & Sapatinas, Theofanis & Subba Rao, Suhasini, 2008. "Normalized least-squares estimation in time-varying ARCH models," LSE Research Online Documents on Economics 25187, London School of Economics and Political Science, LSE Library.
- Clark, Todd E. & West, Kenneth D., 2007.
"Approximately normal tests for equal predictive accuracy in nested models,"
Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
- Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
- Karmakar, Sayar & Gupta, Rangan & Cepni, Oguzhan & Rognone, Lavinia, 2023.
"Climate risks and predictability of the trading volume of gold: Evidence from an INGARCH model,"
Resources Policy, Elsevier, vol. 82(C).
- Sayar Karmakar & Rangan Gupta & Oguzhan Cepni & Lavinia Rognone, 2022. "Climate Risks and Predictability of the Trading Volume of Gold: Evidence from an INGARCH Model," Working Papers 202241, University of Pretoria, Department of Economics.
- Sucarrat, Genaro, 2020. "garchx: Flexible and Robust GARCH-X Modelling," MPRA Paper 100301, University Library of Munich, Germany.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Chen, Yufeng & Xu, Jing & Miao, Jiafeng, 2023. "Dynamic volatility contagion across the Baltic dry index, iron ore price and crude oil price under the COVID-19: A copula-VAR-BEKK-GARCH-X approach," Resources Policy, Elsevier, vol. 81(C).
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- Marek Chudy & Sayar Karmakar & Wei Biao Wu, 2020. "Long-term prediction intervals of economic time series," Papers 2002.05384, arXiv.org.
- Karmakar, Sayar & Demirer, Riza & Gupta, Rangan, 2021.
"Bitcoin mining activity and volatility dynamics in the power market,"
Economics Letters, Elsevier, vol. 209(C).
- Sayar Karmakar & Riza Demirer & Rangan Gupta, 2021. "Bitcoin Mining Activity and Volatility Dynamics in the Power Market," Working Papers 202166, University of Pretoria, Department of Economics.
- Sayar Karmakar & Marek Chudý & Wei Biao Wu, 2022. "Long‐term prediction intervals with many covariates," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 587-609, July.
- Francq, Christian & Thieu, Le Quyen, 2019.
"Qml Inference For Volatility Models With Covariates,"
Econometric Theory, Cambridge University Press, vol. 35(1), pages 37-72, February.
- Francq, Christian & Thieu, Le Quyen, 2015. "Qml inference for volatility models with covariates," MPRA Paper 63198, University Library of Munich, Germany.
- Kejin Wu & Sayar Karmakar, 2023. "A model-free approach to do long-term volatility forecasting and its variants," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
- Kejin Wu & Sayar Karmakar, 2021. "Model-Free Time-Aggregated Predictions for Econometric Datasets," Forecasting, MDPI, vol. 3(4), pages 1-14, December.
- M. Chudý & S. Karmakar & W. B. Wu, 2020. "Long-term prediction intervals of economic time series," Empirical Economics, Springer, vol. 58(1), pages 191-222, January.
- Emrah Gulay & Hamdi Emec, 2018. "Comparison of forecasting performances: Does normalization and variance stabilization method beat GARCH(1,1)†type models? Empirical evidence from the stock markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(2), pages 133-150, March.
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This paper has been announced in the following NEP Reports:- NEP-ECM-2023-09-25 (Econometrics)
- NEP-ETS-2023-09-25 (Econometric Time Series)
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