User profiles for Eric Ghysels
Eric GhyselsBernstein Distinguished Professor of Economics and Professor of Finance, UNC Chapel Hill Verified email at unc.edu Cited by 30504 |
MIDAS regressions: Further results and new directions
E Ghysels, A Sinko, R Valkanov - Econometric reviews, 2007 - Taylor & Francis
We explore mixed data sampling (henceforth MIDAS) regression models. The regressions
involve time series data sampled at different frequencies. Volatility and related processes are …
involve time series data sampled at different frequencies. Volatility and related processes are …
Ex ante skewness and expected stock returns
We use option prices to estimate ex ante higher moments of the underlying individual securities’
risk‐neutral returns distribution. We find that individual securities’ risk‐neutral volatility, …
risk‐neutral returns distribution. We find that individual securities’ risk‐neutral volatility, …
Stock market volatility and macroeconomic fundamentals
We revisit the relation between stock market volatility and macroeconomic activity using a
new class of component models that distinguish short-run from long-run movements. We …
new class of component models that distinguish short-run from long-run movements. We …
Macroeconomics and the reality of mixed frequency data
E Ghysels - Journal of Econometrics, 2016 - Elsevier
Many time series are sampled at different frequencies. When we study co-movements between
such series we usually analyze the joint process sampled at a common low frequency. …
such series we usually analyze the joint process sampled at a common low frequency. …
Regression models with mixed sampling frequencies
We study regression models that involve data sampled at different frequencies. We derive the
asymptotic properties of the NLS estimators of such regression models and compare them …
asymptotic properties of the NLS estimators of such regression models and compare them …
5 Stochastic volatility
E Ghysels, AC Harvey, E Renault - Handbook of statistics, 1996 - Elsevier
Publisher Summary The class of stochastic volatility (SV) models has its roots in both,
mathematical finance and financial econometrics. In fact, several variations of SV models …
mathematical finance and financial econometrics. In fact, several variations of SV models …
There is a risk-return trade-off after all
This paper studies the intertemporal relation between the conditional mean and the
conditional variance of the aggregate stock market return. We introduce a new estimator that …
conditional variance of the aggregate stock market return. We introduce a new estimator that …
Alternative models for stock price dynamics
This paper evaluates the role of various volatility specifications, such as multiple stochastic
volatility (SV) factors and jump components, in appropriate modeling of equity return …
volatility (SV) factors and jump components, in appropriate modeling of equity return …
Predicting volatility: getting the most out of return data sampled at different frequencies
We consider various mixed data sampling (MIDAS) regressions to predict volatility. The
regressions differ in the specification of regressors (squared returns, absolute returns, realized …
regressions differ in the specification of regressors (squared returns, absolute returns, realized …
The MIDAS touch: Mixed data sampling regression models
We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions
involve time series data sampled at different frequencies. Technically speaking MIDAS …
involve time series data sampled at different frequencies. Technically speaking MIDAS …