Predicting the equity risk premium using the smooth cross-sectional tail risk: the importance of correlation
I provide a new monthly cross-sectional measure of stock market tail risk, SCSTR, defined as the average of the daily cross-sectional tail risk, rather than the tail risk of the pooled daily returns within a month. Through simulations, I find that SCSTR better captures monthly tail risk rather than...
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| Format: | article |
| Language: | eng |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10400.14/38328 |
| Country: | Portugal |
| Oai: | oai:repositorio.ucp.pt:10400.14/38328 |
| Summary: | I provide a new monthly cross-sectional measure of stock market tail risk, SCSTR, defined as the average of the daily cross-sectional tail risk, rather than the tail risk of the pooled daily returns within a month. Through simulations, I find that SCSTR better captures monthly tail risk rather than merely the tail risk on specific days within a month. In an extended period from 1964 until 2018, this difference is important in generating strong in- and out-of-sample predictability and performs better than the historical risk premium and other commonly-used predictors for short- and long-term horizons. |
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