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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

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

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

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

Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Duong, Diep; Swanson, Norman R. - 2013

forecasting volatility. Key papers in this area include Andersen, Bollerslev, Diebold and Labys (2003), Corsi (2004), Andersen … evidence on the predictive content of realized measures of jump power variations (including upside and downside risk, jump …

Persistent link: https://www.econbiz.de/10009771770