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Comparing the DSGE modelwith the factor model:an out-of-sample forecasting experiment
Wang, Mu-Chun - Deutsche Bundesbank <Frankfurt, Main> / … - 2008
represent nicely the two opposing forecasting philosophies. The DSGE model on the one hand has a strong theoretical economic …
Persistent link: https://www.econbiz.de/10005866191
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Modelling and forecasting high frequency financial data
Degiannakis, Stavros; Floros, Christos - 2015
estimation and forecasting -- Multiple model comparison and hypothesis framework construction -- Realized volatility forecasting …
Persistent link: https://www.econbiz.de/10011374245
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The application of multivariate GARCH models to turbulent financial markets
Zahnd, Edy - 2002 - Als Ms. gedr.
Persistent link: https://www.econbiz.de/10001659873
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Evaluating density forecasts with an application to stock market returns
Raaij, Gabriela de; Raunig, Burkhard - 2002
Persistent link: https://www.econbiz.de/10001650402
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Improving the accuracy: volatility modeling and forecasting using high-frequency data and the variational component
Kumar, Manish - In: Journal of industrial engineering and management : JIEM 3 (2010) 1, pp. 199-220
simple and threshold jumps and continuous variation yields a substantial improvement in volatility forecasting or not. The …
Persistent link: https://www.econbiz.de/10011899155
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l 1 - penalized likelihood smoothing of volatility processes allowing for abrupt changes
Neto, David; Sardy, Sylvain; Tseng, Paul - 2009
Persistent link: https://www.econbiz.de/10003926975
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Large vector autoregressions with asymmetric priors
Carriero, Andrea; Clark, Todd E.; Marcellino, Massimiliano - 2015 - This draft: November 2015
We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...
Persistent link: https://www.econbiz.de/10011389735
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Asymmetries in conditional mean and variance : modelling stock returns by asMA-asQGARCH
Brännäs, Kurt; Gooijer, Jan G. de - 2000
variancefunctions. In a genuine out-of-sample forecasting experiment theperformance of the best fitted asMA-asQGARCH model is compared … topure asMA and no-change forecasts. This is done both in terms ofconditional mean forecasting as well as in terms of risk … forecasting. …
Persistent link: https://www.econbiz.de/10011303289
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A multiplicative error model with heterogeneous components for forecasting realized volatility
Han, Heejoon; Park, Myung D.; Zhang, Shen - In: Journal of forecasting 34 (2015) 3, pp. 209-219
Persistent link: https://www.econbiz.de/10011305259
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Estimating and forecasting large panels of volatilities with approximate dynamic factor models
Luciani, Matteo; Veredas, David - In: Journal of forecasting 34 (2015) 3, pp. 163-176
Persistent link: https://www.econbiz.de/10011305278