Christian Conrad
Biographic Data
| ID | 5843101 |
|---|---|
| NAME | Christian Conrad |
| GIVEN NAMES | Christian |
| FAMILY NAME | Conrad |
| SIGNATURE | CONRAD C |
| AFFILIATIONS | Heidelberg University |
| ORCID | 0000-0001-7981-2615 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2003 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Modelling Volatility Cycles
We propose a novel multiplicative factor multi‐frequency GARCH (MF2‐GARCH) model, which exploits the empirical fact that the daily standardized forecast errors of one‐component GARCH models are predictable by a moving average of past standardized forecast errors. In contrast to other multiplicative component GARCH models, the MF2‐GARCH features stationary returns, and long‐term volatility forecasts are mean‐reverting. When applied to the S&P 500,…
Two are better than one
We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of generalized autoregressive conditional heteroskedasticity–mixed‐data sampling (GARCH‐MIDAS) models suggested in Engle, Ghysels, and Sohn ( Review of Economics and Statistics , 2013, 95 , 776–797). In those models volatility is decomposed into a short‐term GARCH component and a long‐term component that is driven by an explanat…
Testing for an Omitted Multiplicative Long-Term Component in GARCH Models
We consider the problem of testing for an omitted multiplicative long-term component in GARCH-type models. Under the alternative, there is a two-component model with a short-term GARCH component that fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange multiplier statistic for testing the null hypothesis that the variable has no explanatory power. We derive…
Modelling the Link Between US Inflation and Output
This article employs an augmented version of the UECCC GARCH specification proposed in Conrad and Karanasos (2010) which allows for lagged in‐mean effects, level effects as well as asymmetries in the conditional variances. In this unified framework, we examine the twelve potential intertemporal relationships among inflation, growth and their respective uncertainties using US data. We find that high inflation is detrimental to output growth both d…
The Dysfunctions of Unanimity
The Dysfunctions of Unanimity
Modelling the Link Between US Inflation and Output
This article employs an augmented version of the UECCC GARCH specification proposed in Conrad and Karanasos (2010) which allows for lagged in‐mean effects, level effects as well as asymmetries in the conditional variances. In this unified framework, we examine the twelve potential intertemporal relationships among inflation, growth and their respective uncertainties using US data. We find that high inflation is detrimental to output growth both d…
Two are better than one
We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of generalized autoregressive conditional heteroskedasticity–mixed‐data sampling (GARCH‐MIDAS) models suggested in Engle, Ghysels, and Sohn ( Review of Economics and Statistics , 2013, 95 , 776–797). In those models volatility is decomposed into a short‐term GARCH component and a long‐term component that is driven by an explanat…
Testing for an Omitted Multiplicative Long-Term Component in GARCH Models
We consider the problem of testing for an omitted multiplicative long-term component in GARCH-type models. Under the alternative, there is a two-component model with a short-term GARCH component that fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange multiplier statistic for testing the null hypothesis that the variable has no explanatory power. We derive…
Modelling Volatility Cycles
We propose a novel multiplicative factor multi‐frequency GARCH (MF2‐GARCH) model, which exploits the empirical fact that the daily standardized forecast errors of one‐component GARCH models are predictable by a moving average of past standardized forecast errors. In contrast to other multiplicative component GARCH models, the MF2‐GARCH features stationary returns, and long‐term volatility forecasts are mean‐reverting. When applied to the S&P 500,…
Autoregressive conditional heteroskedasticity (4 works) · Econometrics (4 works) · Market Dynamics and Volatility (4 works) · Monetary Policy and Economic Impact (4 works) · Economics (3 works) · Financial Risk and Volatility Modeling (3 works) · Mathematics (2 works) · Multiplicative function (2 works) · Statistics (2 works) · Autoregressive model (1 work)