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Identification of volatility proxies as expectations of squared financial returns

Sucarrat, Genaro - In: International journal of forecasting 37 (2021) 4, pp. 1677-1690

Persistent link: https://www.econbiz.de/10013274330

Investigating seasonality, policy intervention and forecasting in the Indian gold futures market : a comparison based on modeling non-constant variance using two different methods

Nargunam, Rupel; Wei, William W. S.; Anuradha, N. - In: Financial innovation : FIN 7 (2021), pp. 1-15

This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form. The trade of gold futures relates to seasons, festivity, and...

Persistent link: https://www.econbiz.de/10012617371

Selecting volatility forecasting models for portfolio allocation purposes

Becker, Ralf; Clements, Adam; Doolan, M. B.; Hurn, Stan - In: International journal of forecasting 31 (2015) 3, pp. 849-861

Persistent link: https://www.econbiz.de/10011474597

Accelerating GARCH and score-driven models : optimality, estimation and forecasting

Blasques, Francisco F.; Gorgi, Paolo; Koopman, Siem Jan S.J. - 2017

allows for a more flexible weighting of financial squared-returns for the filtering of volatility. The parameter for the …-return replaced by the product of the volatility innovation and its lagged value. This local estimate of the first order … autocorrelation of volatility innovations acts as an indicator of the importance of the squared-return for volatility updating. When …

Persistent link: https://www.econbiz.de/10011688512