Similar Search Results

The impact of sampling frequency and volatility estimators on change-point tests

Andreou, Alena; Ghysels, Eric - In: Journal of financial econometrics : official journal of … 2 (2004) 2, pp. 290-318

Persistent link: https://www.econbiz.de/10002214288

Predicting volatility: getting the most out of return data sampled at different frequencies

Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen I. - In: Journal of econometrics 131 (2006) 1/2, pp. 59-95

Persistent link: https://www.econbiz.de/10003298564

The Asian financial crisis : the role of derivative securities trading and foreign investors in Korea

Ghysels, Eric; Seon, Junghoon - In: Journal of international money and finance 24 (2005) 4, pp. 607-630

Persistent link: https://www.econbiz.de/10002921318

Testing for structural change in the presence auf auxiliary models

Ghysels, Eric; Guay, Alain - In: Econometric theory 20 (2004) 6, pp. 1168-1202

Persistent link: https://www.econbiz.de/10002424914

Do heterogeneous beliefs matter for asset pricing?

Anderson, Ewan W.; Ghysels, Eric; Juergens, Jennifer L. - In: The review of financial studies 18 (2005) 3, pp. 875-924

Persistent link: https://www.econbiz.de/10003133526

Predicting volatility : getting the most out of return data sampled at different frequencies

Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen - 2004

"We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in...