- Consulting Editors
- Vars, Cointegration, and Common Cycle Restrictions
- Dynamic Factor Models
- Forecasting With Nonlinear Time Series Models
- Forecasting With DSGE Models
- Forecasting Economic Time Series Using Unobserved Components Time Series Models
- Improving the Role of Judgment in Economic Forecasting
- Forecasting with Mixed-Frequency Data
- Forecasting with Real-Time Data Vintages
- Forecasting from misspecified Models in the Presence of Unanticipated Location Shifts
- Forecasting Breaks and Forecasting During Breaks
- Forecast Combinations
- Multiple Forecast Model Evaluation
- Testing for Unconditional Predictive Ability
- Testing Conditional Predictive Ability
- Interpreting and Combining Heterogeneous Survey Forecasts
- Analyzing Three-Dimensional Panel Data of Forecasts
- Forecasting Financial Time Series
- Forecasting Volatility Using High-Frequency Data
- Economic Value of Weather and Climate Forecasts
- Long-Horizon Growth Forecasting and Demography
- Forecasting the Energy Markets
- Models for Health Care
- Election Forecasting
- Marketing and Sales
Abstract and Keywords
This article focuses on some aspects of high-frequency data and their use in volatility forecasting. High-frequency data can be used to construct volatility forecasts. The article reviews two leading approaches to this. One approach is the reduced-form forecast, where the forecast is constructed from a time series model for realized measures, or a simple regression-based approach such as the heterogeneous autoregressive model. The other is based on more traditional discrete-time volatility models that include a modeling of returns. Such models can be generalized to utilize information provided by realized measures. The article also discusses how volatility forecasts, produced by complex volatility models, can benefit from high-frequency data in an indirect manner, through the use of realized measures to facilitate and improve the estimation of complex models.
Peter Reinhard Hansen is an assistant professor of economics at Stanford University. He holds an MSc in mathematics and economics from the University of Copenhagen and a PhD in economics from the University of California, San Diego. Before joining the Department of Economics at Stanford University in 2004, he was an assistant professor of economics at Brown University (2000–2004).His research is known for the Test for Superior Predictability, the Model Confidence Set, the Realized Kernel Estimator; and the Winner's Curse of Econometric Models. He has coauthored the book Workbook on Cointegration, published by Oxford University Press in 1998 and he has published research articles on cointegration, forecasting, high-frequency data, and financial volatility. He is associate editor for the Journal of Applied Econometrics, and a research fellow at the Center for Research in Econometric Analysis of Time Series and the Volatility Institute at Stern, NYU. His current research is concerned with the estimation of financial volatility using high-frequency data, the theory behind the “winner's curse of econometric models,” and the development of GARCH models that utilize realized measures of volatility.
Asger Lunde holds a MA in mathematical economics and a PhD in economics from Aarhus University. He is a professor at the School of Economics and Mangagement, Aarhus University. He is research fellow at the Center for Research in Econometric Analysis of Time Series (CREATES) in Aarhus. He is an associate member of Oxford-Man Institute of Quantitative Finance, where he has worked on construction of the institute's realized library, a Web page that contains daily nonparametric measures of how volatile financial assets or indexes were, in the past. He is also a research affiliate at the Volatility Institute at New York University. He is a founding and a council member of the Socitey for Finanical Econometrics. He is associate editor for the International Journal of Forecasting. His current research interest addresses several aspects concerning volatility measurement and modeling. In a parallel research agenda, he investigates the effect of data mining on model evaluation and model selection. His research is published in journals such as Econometrica, Journal of Business and Economic Statistics, Journal of Financial Econometrics, and Journal of Emprical Finance.
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