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Comparing Asset Pricing Models Using Quantile Regressions for Distance-based Metrics
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wang, Ziwen |
| Copyright Year | 2019 |
| Abstract | This thesis compares the performance of ten well-known asset-pricing models for cross-sectional returns of various portfolios from January 1967 to December 2016. We rely on the distance-based metrics as the primary performance measure and use quantile regressions to compare models at a wide range of quantiles of the asset return distribution. The model performance is examined from both statistical and economic perspectives. We find that the Fama and French (2018) six-factor model reliably outperforms other competing models in pricing the selected portfolios. In particular, both the momentum factor and the value factor are necessary in asset-pricing models to explain the return variations in different quantiles. We also find that the performance of Barilla and Shanken (2018) six-factor model exhibits strong explanatory power in medium to high quantiles, despite some existing findings that their model performs poorly in OLS regressions. Overall, we show that the distance-based metrics coupled with quantile regressions provide a consistent and robust model-comparison methodology that largely enhances the existing OLS-based statistical |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://dr.library.brocku.ca/bitstream/handle/10464/14409/Brock_Wang_Ziwen_2019.pdf?isAllowed=y&sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
