arXiv 24 May 2024 · Econometrics
arXiv:2405.15721 · PDF · DOI · OpenAlex · Extracted main text
We develop novel estimation procedures with supporting econometric theory for a dynamic latent-factor model with high-dimensional asset characteristics, that is, the number of characteristics is on the order of the sample size. Utilizing the Double Selection Lasso estimator, our procedure employs regularization to eliminate characteristics with low signal-to-noise ratios yet maintains asymptotically valid inference for asset pricing tests. The crypto asset class is well-suited for applying this model given the limited number of tradable assets and years of data as well as the rich set of available asset characteristics. The empirical results present out-of-sample pricing abilities and risk-adjusted returns for our novel estimator as compared to benchmark methods. We provide an inference procedure for measuring the risk premium of an observable nontradable factor, and employ this to find that the inflation-mimicking portfolio in the crypto asset class has positive risk compensation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bai (2003) Inferential theory for factor models of large dimensions | 1.000 | 14 | 4 | 100% |
| 2 | Bai and Ng (2013) Principal components estimation and identification of static factors | 1.000 | 5 | 3 | 100% |
| 3 | Giglio and Xiu (2021) Asset pricing with omitted factors | 0.986 | 24 | 5 | 96% |
| 4 | Bai and Ng (2002) Determining the number of factors in approximate factor models | 0.928 | 4 | 3 | 100% |
| 5 | Belloni, Chernozhukov, and Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 0.909 | 8 | 4 | 75% |
| 6 | Kelly, Pruitt, and Su (2019) Characteristics are covariances: A unified model of risk and return | 0.874 | 6 | 2 | 100% |
| 7 | Gu, Kelly, and Xiu (2020) Empirical asset pricing via machine learning | 0.843 | 3 | 3 | 100% |
| 8 | Chen, Pelger, and Zhu (2020) Deep learning in asset pricing | 0.811 | 4 | 2 | 100% |
| 9 | Giglio, Xiu, and Zhang (2021) Test assets and weak factors | 0.811 | 4 | 2 | 100% |
| 10 | Bianchi, Guidolin, and Pedio (2022) The dynamics of returns predictability in cryptocurrency markets | 0.737 | 3 | 2 | 100% |
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