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Earnings announcements are full of surprises

Brandt, Michael W.; Kishore, Runeet; Santa-Clara, Pedro; … - 2008 - This version: January 22, 2008

Persistent link: https://www.econbiz.de/10003768329

Flexible multivariate GARCH modeling with an application to international stock markets

Ledoit, Olivier (contributor); … - 2001 - [Elektronische Ressource]

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Flexible multivariate GARCH modeling with an application to international stock markets

Ledoit, Olivier; Santa-Clara, Pedro; Wolf, Michael - In: The review of economics and statistics 85 (2003) 3, pp. 735-747

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The presidential puzzle : political cycles and the stock market

Santa-Clara, Pedro; Valkanov, Rossen - In: The journal of finance : the journal of the American … 58 (2003) 5, pp. 1841-1872

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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...

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Forecasting stock market returns : the sum of the parts is more than the whole

Ferreira, Miguel A.; Santa-Clara, Pedro - 2008

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Crashes, volatility, and the equity premium : lessons from S&P 500 options

Santa-Clara, Pedro; Yan, Shu - In: The review of economics and statistics 92 (2010) 2, pp. 435-451

Persistent link: https://www.econbiz.de/10008737706

Forecasting stock market returns : the sum of the parts is more than the whole

Ferreira, Miguel A.; Santa-Clara, Pedro - In: Journal of financial economics 100 (2011) 3, pp. 514-537

Persistent link: https://www.econbiz.de/10009242099

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

Predicting Volatility : Getting the Most Out of Return Data Sampled at Different Frequencies

Ghysels, Eric - 2012

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...

Persistent link: https://www.econbiz.de/10012755731