Research
His most recent research focuses on Mixed data sampling (MIDAS) regression models and filtering methods with applications in finance and other fields. He has also worked on diverse topics such as seasonality in economic times series, Generalized Method of Moment estimation and testing of asset pricing models, time varying betas, estimation of risk neutral and objective probability measures for the purpose of option pricing, among many other topics.
Mixed data sampling or MIDAS regressions are econometric regression models can be viewed in some cases as substitutes for the Kalman filter when applied in the context of mixed frequency data.
Recent work on Mixed data sampling (MIDAS) includes:
Andreou, E, Eric Ghysels and A. Kourtellos (2010) Should macroeconomic forecasters use daily financial data and how?, Discussion Paper UNC.
Andreou, E, Eric Ghysels and A. Kourtellos (2010) Forecasting with mixed-frequency data, Chapter prepared for Oxford Handbook on Economic Forecasting edited by Michael P. Clements and David F. Hendry
Chen, Xilong, Eric Ghysels and Fangfang Wang, (2010), The HYBRID GARCH Class of Models, Discussion Paper UNC.
Bai, J., Eric Ghysels and Jonathan Wright (2010), State Space Models and MIDAS Regressions, Discussion Paper UNC.
Eric Ghysels and J. Wright (2009), Forecasting Professional Forecasters, Journal of Business and Economic Statistics
Anderson, E., Eric Ghysels and J. Juergens (2009) The Impact of Risk and Uncertainty on Expected Returns, Journal of Financial Economics
Eric Ghysels and B. Sohn (2009) Which Power Variation Predicts Volatility Well? Journal of Empirical Finance
Andreou, E, Eric Ghysels and A. Kourtellos (2010) "Regression Models With Mixed Sampling Frequencies", Journal of Econometrics (Article in Press)
Eric Ghysels, Santa-Clara, P. and Valkanov, R. (2005), There is a Risk-return Trade-off After All, Journal of Financial Economics, 76, 509-548.
Eric Ghysels, Santa-Clara, P. and Valkanov, R. (2006) Predicting volatility: How to get most out of returns data sampled at different frequencies Journal of Econometrics 131, 59-95
Eric Ghysels, Sinko, A., Valkanov, R. (2007) MIDAS Regressions: Further Results and New Directions. Econometric Reviews, 26 (1), 53–90
Riccardo Colacito, Robert Engle and Eric Ghysels A Component Model of Dynamic Correlations, Journal of Econometrics (Article in Press)
He has also published several books, including a monograph with Denise Osborn (University of Manchester) on the Econometric Analysis of Seasonal Time Series.
Representative set of other publications:
Ghysels, E. (1988) A Study Towards a Dynamic Theory of Seasonality for Economic Time Series, Journal of the American Statistical Association. Reprinted in Modelling Seasonality, S. Hylleberg (ed.), Oxford University Press, 181-192.
Ghysels, E. and A. Hall, (1990), Are Consumption-Based Intertemporal Capital Asset Pricing Models Structural?, Journal of Econometrics 45, 121-139.
Ghysels, E. and A. Hall, (1990), A Test for Structural Stability of Euler Conditions Parameters Estimated Via the Generalized Method of Moments Estimator, International Economic Review 31, 355-364.
Ghysels, E. (1994), On the Economics and Econometrics of Seasonality.” Invited paper, 1990 World Congress of the Econometric Society, August 1990, in Advances in Econometrics I, C.A. Sims (ed.), Cambridge University Press, 257-316.
Ghysels, E., C.W.J. Granger and P. Siklos (1995), Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Transformation? Invited JBES paper, Journal of Business and Economic Statistics 14, 139-152. Reprinted in Newbold, P. and S.J. Leybourne (2003) Recent Developments in Time Series, Edward Elgar. Reprinted in Essays in Econometrics: collected Papers of Clive W.J. Granger: Vol. I, Cambridge University Press
Ghysels, E., A. Harvey and E. Renault, (1995), Stochastic Volatility, in Handbook of Statistics 14, Statistical Methods in Finance, G.S. Maddala and C.R. Rao (eds.), North Holland, Amsterdam.
Ghysels, E. (1998), On Stable Factor Structures in the Pricing of Risk: Do Time-Varying Betas Help or Hurt? Journal of Finance 53, 549-573.
Cao, C., Ghysels, E. and F. Hatheway, (2000), Price Discovery without Trading: The Case of the Nasdaq Pre-opening (NYSE Best Paper Award – Western Finance Association Meetings 1999, Santa Monica), Journal of Finance 55, 1339-1366.
Chernov, M. and E. Ghysels, (2000), A Study Towards a Unified Approach to the Joint Estimation of Objective and Risk Neutral Measures for the Purpose of Options Valuation, Journal of Financial Economics 56, 407-458, Reprinted in Stochastic Volatility: Selected Readings, N. Shephard (ed.), Oxford University Press, 398-448, (All-Star JFE paper selection based on average yearly citations).
Andreou, E. and E. Ghysels, (2002) Detecting multiple breaks in financial market volatility dynamics, Journal of Applied Econometrics 17, 579-600.
Anderson, E., E. Ghysels and J. Juergens (2005), Do Heterogeneous Beliefs Matter for Asset Pricing?, Review of Financial Studies, 18, 875-924.
Eriksson, A., E. Ghysels and F. Wang (2009), The Normal Inverse Gaussian Distribution and the Pricing of Derivatives, Journal of Derivatives, Spring
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