MIDAS Regressions: Further Results and New Directions
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Abstract
We explore mixed data sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Volatility and related processes are our prime focus, though the regression method has wider applications in macroeconomics and finance, among other areas. The regressions combine recent developments regarding estimation of volatility and a not-so-recent literature on distributed lag models. We study various lag structures to parameterize parsimoniously the regressions and relate them to existing models. We also propose several new extensions of the MIDAS framework. The paper concludes with an empirical section where we provide further evidence and new results on the risk-return trade-off. We also report empirical evidence on microstructure noise and volatility forecasting.
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The record
- Venue
- Econometric Reviews
- Topic
- Financial Risk and Volatility Modeling
- Field
- Economics, Econometrics and Finance
- Canadian institutions
- —
- Funders
- MitacsUniversity of Hong KongAcademia SinicaCity University of Hong Kong
- Keywords
- EconometricsStatisticsEconomicsMathematics
- Has abstract in OpenAlex
- yes