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Eric Ghysels

## Summary

Eric Ghysels (born 1956 in Brussels) is a Belgian-American economist with particular interest in finance and time series econometrics, and in particular the fields of financial econometrics and financial technology.[1] He is the Edward M. Bernstein Distinguished Professor of Economics at the University of North Carolina[2] and a Professor of Finance at the Kenan-Flagler Business School.[3] He is also the Faculty Research Director of the Rethinc.Labs at the Frank Hawkins Kenan Institute of Private Enterprise.[4]

Eric Ghysels
Born1956 (age 65–66)
Brussels, Belgium
Spouse(s)Marianna Matinyan
ChildrenNicholas Ghysels
Jonathan Ghysels
InstitutionUniversity of North Carolina at Chapel Hill
FieldFinance
Financial econometrics
Machine Learning
Econometrics
Fintech
Alma materVrije Universiteit Brussel
Northwestern University
InfluencesRobert Engle
Clive W. J. Granger
Lars Peter Hansen
Thomas J. Sargent
Christopher Sims
Halbert White
ContributionsMixed-data sampling (MIDAS)
Seasonality
AwardsDoctor Honoris Causa
HEC, University of Liège
Websiteeghysels.web.unc.edu

## Early life and education

Ghysels was born in Brussels, Belgium, as the son of Pierre Ghysels (a civil servant) and Anna Janssens (a homemaker). He completed his undergraduate studies in economics (Supra Cum Laude) at the Vrije Universiteit Brussel in 1979. He obtained a Fulbright Fellowship from the Belgian American Educational Foundation in 1980 and started graduate studies at Northwestern University that year, finishing his PhD at the Kellogg Graduate School of Management of Northwestern University in 1984. In 2019 he was awarded an honorary doctorate (Doctor Honoris Causa) by HEC University of Liège.[5]

## Career

After graduation from the Kellogg School of Management at Northwestern University he took a faculty position at the Université de Montréal in the Department of Economics.[6] In 1996 he became a Professor of Economics at Penn State University[7] and joined the University of North Carolina at Chapel Hill in 2000. He is currently the Edward M. Bernstein Distinguished Professor of Economics at UNC Chapel Hill and a Professor of Finance and the Kenan-Flagler Business School. Since 2018 he is the Faculty Research Director, Rethinc.Labs, at the Kenan Institute for Private Enterprise at UNC Chapel Hill. Since 2020 he is also affiliated with the Department of Electrical and Computer Engineering at the North Carolina State University.[8]

Ghysels is a fellow of the American Statistical Association and co-founded with Robert Engle the Society for Financial Econometrics (SoFiE).[9][10] He was editor of the Journal of Business and Economic Statistics (with Alastair R. Hall, 2001–2004) editor of the Journal of Financial Econometrics (2012–2015).[11] He is currently co-editor of the Journal of Applied Econometrics.[12]

In 2008–2009 Ghysels was resident scholar at the Federal Reserve Bank of New York, in 2011 Duisenberg Fellow at the European Central Bank, both at the height of the Great Recession, and has since been a regular visitor of several other central banks around the world.

He has also been visiting Professor at Bocconi University (Tommaso Padoa-Schioppa Visiting Professor, 2017), the Stevanovich Center at the University of Chicago (2015), Cambridge University (INET Visiting Professor, 2014), New York University Stern School of Business (2007), among others, and holds a courtesy appointment at Louvain Finance, Université catholique de Louvain.[13]

## Books

In 2001, he published a monograph on The Econometric Analysis of Seasonal Time Series together with Denise R. Osborn.[14] In 2018, he published a textbook entitled Applied Economic Forecasting using Time Series Methods together with Massimiliano Marcellino.[15]

## Honors and awards

His honors and awards include:

## Research

Ghysels' 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, machine learning and AI applications in finance, quantum computing applications in finance, 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. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006),[25] Ghysels, Sinko and Valkanov,[26] Andreou, Ghysels and Kourtellos (2010)[27] and Andreou, Ghysels and Kourtellos (2013).[28]

A MIDAS regression is a direct forecasting tool which can relate future low-frequency data with current and lagged high-frequency indicators, and yield different forecasting models for each forecast horizon. It can flexibly deal with data sampled at different frequencies and provide a direct forecast of the low-frequency variable. It incorporates each individual high-frequency data in the regression, which solves the problems of losing potentially useful information and including mis-specification.

A simple regression example has the independent variable appearing at a higher frequency than the dependent variable:

${\displaystyle y_{t}=\beta _{0}+\beta _{1}B(L^{1/m};\theta )x_{t}^{(m)}+\varepsilon _{t}^{(m)},}$

where y is the dependent variable, x is the regressor, m denotes the frequency – for instance if y is yearly ${\displaystyle x_{t}^{(4)}}$  is quarterly – ${\displaystyle \varepsilon }$  is the disturbance and ${\displaystyle B(L^{1/m};\theta )}$  is a lag distribution, for instance the Beta function or the Almon Lag.

The regression models can be viewed in some cases as substitutes for the Kalman filter when applied in the context of mixed frequency data. Bai, Ghysels and Wright (2013)[29] examine the relationship between MIDAS regressions and Kalman filter state space models applied to mixed frequency data. In general, the latter involves a system of equations, whereas, in contrast, MIDAS regressions involve a (reduced form) single equation. As a consequence, MIDAS regressions might be less efficient, but also less prone to specification errors. In cases where the MIDAS regression is only an approximation, the approximation errors tend to be small.

The MIDAS can also be used for machine learning time series and panel data nowcasting.[30][31] The machine learning MIDAS regressions involve Legendre polynomials. High-dimensional mixed frequency time series regressions involve certain data structures that once taken into account should improve the performance of unrestricted estimators in small samples. These structures are represented by groups covering lagged dependent variables and groups of lags for a single (high-frequency) covariate. To that end, the machine learning MIDAS approach exploits the sparse-group LASSO (sg-LASSO) regularization that accommodates conveniently such structures.[32] The attractive feature of the sg-LASSO estimator is that it allows us to combine effectively the approximately sparse and dense signals.

Several software packages feature MIDAS regressions and related econometric methods. These include:

• MIDAS Matlab Toolbox[33]
• midasr, R package[34]
• midasml, R package for High-Dimensional Mixed Frequency Time Series Data[35]
• EViews[36]
• Python[37]
• Julia[38]

## References

1. ^ Eric Ghysels publications indexed by Google Scholar
2. ^ "Eric Ghysels". eghysels.web.unc.edu.
3. ^ Economics, Eric Ghysels Edward Bernstein Distinguished Professor of; Center 403A, Professor of Finance Contact Kenan; Hill, CB 3440 Chapel. "Eric Ghysels | UNC Kenan-Flagler Business School". kenan-flagler.unc.edu.
4. ^ "Rethinc".
5. ^ "Docteurs honoris causa facultaires 2019".
6. ^ "Accueil". Département de sciences économiques - Université de Montréal.
7. ^ "Welcome to the Department of Economics — Department of Economics". econ.la.psu.edu.
8. ^ "Supporting Faculty • Electrical and Computer Engineering".
9. ^ "ET Interview: Eric Ghysels" (PDF).
10. ^ "Past Presidents, Founding Council, and Founding Members | The Society for Financial Econometrics".
12. ^ "Journal of Applied Econometrics". Wiley Online Library.
13. ^ "Eric Ghysels". UCLouvain.
14. ^ Eric Ghysels and Denise Osborn (2012). The Econometric Analysis of Seasonal Time Series. Cambridge University Press. ISBN 9780521562607.
15. ^ Eric Ghysels and Massimiliano Marcellino (2018). Applied Economic Forecasting using Time Series Methods. Oxford University Press. ISBN 9780190622039.
16. ^ "ASA Fellows List". amstat.org.
17. ^ "Who's Who in Economics, Fourth Edition". ISBN 978 1 84064 992 5.
18. ^ "Program for Resident Scholars - FEDERAL RESERVE BANK of NEW YORK". newyorkfed.org.
19. ^ "Fernand Braudel Fellows". European University Institute.
20. ^ Bank, European Central (9 March 2020). "Wim Duisenberg Fellowship". European Central Bank.
21. ^ "Fellows | The Society for Financial Econometrics".
22. ^ "Professor Eric Ghysels Presents the Inaugural Gumbel Lecture | Economics Department". econ.unc.edu.
23. ^ "CORE Lecture Series, by Professor E. Ghysels". UCLouvain.
24. ^ "Fellows | International Association for Applied Economectrics". appliedeconometrics.org.
25. ^ Ghysels, Eric, Pedro Santa-Clara and Rossen Valkanov (2006) Predicting Volatility: How to Get Most Out of Returns Data Sampled at Different Frequencies, Journal of Econometrics, 131, 59–95
26. ^ Ghysels, Eric and Arthur Sinko and Rossen Valkanov (2006) MIDAS Regressions: Further Results and New Directions, Econometric Reviews, 26, 53–90.
27. ^ Andreou, Elena & Eric Ghysels & Andros Kourtellos "Regression Models with Mixed Sampling Frequencies", Journal of Econometrics, 158, 246–261.
28. ^ Andreou, Elena & Eric Ghysels & Andros Kourtellos "Should macroeconomic forecasters use daily financial data and how?", Journal of Business and Economic Statistics 31, 240–251.
29. ^ Bai, Jennie and Eric Ghysels and Jonathan Wright (2013) State Space Models and MIDAS Regressions, Econometric Reviews, 32, 779–813.
30. ^ Babii, Andrii & Eric Ghysels & Jonas Striaukas "Machine learning time series regressions with an application to nowcasting", arXiv:2005.14057.
31. ^ Babii, Andrii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas "Machine learning time series regressions with an application to nowcasting", arXiv:2005.14057.
32. ^ Simon, N., J. Friedman, T. Hastie, and R. Tibshirani (2013): A sparse-group LASSO, Journal of Computational and Graphical Statistics, 22(2), 231–245.
33. ^ "MIDAS Matlab Toolbox". mathworks.com.
34. ^ Kvedaras, Virmantas; Zemlys-Balevicius, Vaidotas (23 February 2021). "midasr: Mixed Data Sampling Regression" – via R-Packages.
35. ^ "midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data".
36. ^ "MIDAS". eviews.com.
37. ^ "sapphire921/midas_pro". 3 April 2021 – via GitHub.
38. ^ "mikemull/Midas.jl". 31 May 2019 – via GitHub.