Dynamic Latent-Factor Model with High-Dimensional Asset Characteristics

ArXiv ID: 2405.15721 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Double Selection Lasso, latent-factor model, high-dimensional asset characteristics, inference procedure, risk premium, Crypto Assets

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper presents advanced econometric theory with double selection Lasso, high-dimensional PCA, and asymptotic inference, demanding high math sophistication. It is data- and implementation-heavy with crypto asset characteristics, out-of-sample backtests, and a GitHub repository for replication code.

  flowchart TD
    A["Research Goal: Develop a novel estimation procedure for a dynamic latent-factor model with high-dimensional asset characteristics."] --> B["Methodology: Double Selection Lasso Estimator"]
    B --> C["Data: High-Dimensional Crypto Asset Characteristics"]
    C --> D["Computation: Regularization & Asymptotic Inference"]
    D --> E{"Findings / Outcomes"}
    E --> F["1. Superior Out-of-Sample Pricing & Risk-Adjusted Returns vs. Benchmarks"]
    E --> G["2. Inference Procedure for Risk Premium of Nontradable Factors"]
    E --> H["3. Inflation-Mimicking Portfolio in Crypto shows Positive Risk Compensation"]