Zhaoxing Gao, Sihan Tu, Ruey S. Tsay
arXiv 3 Nov 2025 · Econometrics
arXiv:2511.01271 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates estimation and inference of a Spatial Arbitrage Pricing Theory (SAPT) model that integrates spatial interactions with multi-factor analysis, accommodating both observable and latent factors. Building on the classical mean-variance analysis, we introduce a class of Spatial Capital Asset Pricing Models (SCAPM) that account for spatial effects in high-dimensional assets, where we define {\it spatial rho} as a counterpart to market beta in CAPM. We then extend SCAPM to a general SAPT framework under a {\it complete} market setting by incorporating multiple factors. For SAPT with observable factors, we propose a generalized shrinkage Yule-Walker (SYW) estimation method that integrates ridge regression to estimate spatial and factor coefficients. When factors are latent, we first apply an autocovariance-based eigenanalysis to extract factors, then employ the SYW method using the estimated factors. We establish asymptotic properties for these estimators under high-dimensional settings where both the dimension and sample size diverge. Finally, we use simulated and real data examples to demonstrate the efficacy and usefulness of the proposed model and method.
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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 | Aquaro et al (2021) Estimation and inference for spatial models with heterogeneous coefficients: an application to US house prices | 1.000 | 9 | 3 | 100% |
| 2 | Lam and Yao (2012) Factor modeling for high-dimensional time series: inference for the number of factors | 1.000 | 9 | 3 | 100% |
| 3 | Bai and Ng (2002) Determining the number of factors in approximate factor models | 1.000 | 7 | 4 | 100% |
| 4 | Gao and Tsay (2022) Modeling high-dimensional time series: A factor model with dynamically dependent factors and diverging eigenvalues | 1.000 | 6 | 3 | 100% |
| 5 | Hu et al (2023) Arbitrage pricing with heterogeneous spatial effects and heteroscedastic disturbances | 0.874 | 7 | 2 | 100% |
| 6 | Lam et al (2011) Estimation of latent factors for high-dimensional time series | 0.874 | 6 | 2 | 100% |
| 7 | Ahn and Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.737 | 3 | 2 | 100% |
| 8 | Ross (1976) The arbitrage theory of capital asset pricing | 0.737 | 3 | 2 | 100% |
| 9 | Kou et al (2018) Asset pricing with spatial interaction | 0.693 | 5 | 1 | 100% |
| 10 | Bai and Li (2021) Dynamic spatial panel data models with common shocks | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 48 scored citations.