High-Dimensional Spatial Arbitrage Pricing Theory with Heterogeneous Interactions
Researchers have unveiled the Spatial Arbitrage Pricing Theory (SAPT), a new model that integrates spatial interactions and multi-factor analysis to better predict asset prices. The key innovation is the spatial rho, a parameter similar to market beta in CAPM (Capital Asset Pricing Model), which measures how asset performance in one area affects others. Using advanced statistical methods, SAPT outperformed standard models in forecasting U.S. stock and housing returns, though it depends on accurate spatial data and certain assumptions.
What it examines
This paper develops a new asset pricing model that combines spatial interactions and multi-factor analysis for high-dimensional financial data. It introduces the Spatial Arbitrage Pricing Theory (SAPT), proposes a shrinkage Yule-Walker estimation method, and addresses both observable and hidden (latent) factors to improve risk assessment and forecasting.
What it concludes
The SAPT model enhances asset pricing by capturing spatial effects and multiple risk factors, outperforming traditional methods in prediction and efficiency. It is useful for financial forecasting, portfolio management, and real estate analysis. Future research may further refine estimation techniques and explore broader economic applications.
Evidence objects
Researchers unveil the Spatial Arbitrage Pricing Theory (SAPT), a novel model blending spatial interactions and multi-factor analysis to capture both observable and hidden asset pricing factors, revolutionizing financial system understanding.
key_findings bullet 1 · key_findings · validation V0
The SAPT introduces 'spatial rho,' a new parameter akin to market beta in CAPM, measuring spatial risk and spillover effectsrevealing how one asset or regions performance can influence others, a major innovation.
key_findings bullet 2 · key_findings · validation V0
Using advanced shrinkage Yule-Walker estimation and ridge regression, SAPT outperforms standard models like QMLE and Fama-French in forecasting U.S. stocks and housing prices, though it depends on known spatial weights and market completeness.
key_findings bullet 3 · key_findings · validation V0
This paper presents the novel Spatial Arbitrage Pricing Theory (SAPT), uniquely integrating spatial interactions and multi-factor analysis in high-dimensional asset contexts. Introducing 'spatial rho' analogous to market beta and a ridge-regularized Yule-Walker estimator, SAPT advances quantitative finance by unifying spatial effects, high-dimensionality, and arbitrage pricing, addressing practical estimation challenges.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
Abstract: 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… ▽ More 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. △ Less
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