Do liquidity variables improve out-of-sample prediction of sovereign spreads during crisis periods?
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DOI: 10.1016/j.frl.2016.11.006
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Cited by:
- Kinateder, Harald & Papavassiliou, Vassilios G., 2019.
"Sovereign bond return prediction with realized higher moments,"
Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 53-73.
- Harald Kinateder & Vassilios G. Papavassiliou, 2019. "Sovereign bond return prediction with realized higher moments," Open Access publications 10197/11286, Research Repository, University College Dublin.
- Apergis, Nicholas, 2022. "COVID-19 and cryptocurrency volatility: Evidence from asymmetric modelling," Finance Research Letters, Elsevier, vol. 47(PA).
- Narayan, Paresh Kumar & Ahmed, Huson Ali & Narayan, Seema, 2017. "Can investors gain from investing in certain sectors?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 160-177.
- Fan He & Xuansen He, 2019. "A Continuous Differentiable Wavelet Shrinkage Function for Economic Data Denoising," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 729-761, August.
- Shahzad, Syed Jawad Hussain & Mensi, Walid & Hammoudeh, Shawkat & Balcilar, Mehmet & Shahbaz, Muhammad, 2018. "Distribution specific dependence and causality between industry-level U.S. credit and stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 114-133.
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More about this item
Keywords
EMU sovereign debt; Market liquidity; Out-of-sample prediction; Predictability of yield spread changes;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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