Conditional Variance Forecasts for Long-Term Stock Returns
In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step procedur...
Main Authors: | Enno Mammen, Jens Perch Nielsen, Michael Scholz, Stefan Sperlich |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-11-01
|
Series: | Risks |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-9091/7/4/113 |
Similar Items
-
Longer-Term Forecasting of Excess Stock Returns—The Five-Year Case
by: Ioannis Kyriakou, et al.
Published: (2020-06-01) -
Application of the GARCH Model in Forecasting the Volatility of Stock Returns in the Infrastructure, Utility, and Transportation Sector
by: Faizul Mubarok, et al.
Published: (2021-02-01) -
Interday drifts in opening stock returns
by: Andrey KUDRYAVTSEV
Published: (2013-11-01) -
The Impact of Stock Overvaluation on Stock’s
Abnormal Returns and their Volatility over Time
by: Ali Ghasemi, et al.
Published: (2015-12-01) -
The Impact of Stock Overvealuation on Abnormal Stock Returns and their Volatility over Time
by: علی قاسمی, et al.
Published: (2015-12-01)