A Dynamic Spatiotemporal and Network ARCH Model with Common Factors

ArXiv ID: 2410.16526 “View on arXiv”

Authors: Unknown

Abstract

We introduce a dynamic spatiotemporal volatility model that extends traditional approaches by incorporating spatial, temporal, and spatiotemporal spillover effects, along with volatility-specific observed and latent factors. The model offers a more general network interpretation, making it applicable for studying various types of network spillovers. The primary innovation lies in incorporating volatility-specific latent factors into the dynamic spatiotemporal volatility model. Using Bayesian estimation via the Markov Chain Monte Carlo (MCMC) method, the model offers a robust framework for analyzing the spatial, temporal, and spatiotemporal effects of a log-squared outcome variable on its volatility. We recommend using the deviance information criterion (DIC) and a regularized Bayesian MCMC method to select the number of relevant factors in the model. The model’s flexibility is demonstrated through two applications: a spatiotemporal model applied to the U.S. housing market and another applied to financial stock market networks, both highlighting the model’s ability to capture varying degrees of interconnectedness. In both applications, we find strong spatial/network interactions with relatively stronger spillover effects in the stock market.

Keywords: Spatiotemporal Modeling, Bayesian MCMC, Volatility Spillovers, Latent Factor Models, Network Analysis, Equities/Real Estate

Complexity vs Empirical Score

  • Math Complexity: 9.0/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail
  • Why: The paper introduces a novel spatiotemporal ARCH model with latent factors, requiring advanced Bayesian MCMC estimation, finite mixture approximations, and regularization techniques, indicating very high mathematical density. The empirical rigor is solid, featuring a rigorous Bayesian framework, model selection criteria (DIC), a simulation study, and two real-world financial applications (housing and stock markets) demonstrating model fit and spillover detection, making it backtest-ready but not heavily focused on implementation details like code or live trading metrics.
  flowchart TD
    G["Research Goal: Model Dynamic Spatiotemporal Volatility with Latent Factors"] --> M["Methodology: Dynamic Spatiotemporal & Network ARCH Model"]
    M --> D["Data: U.S. Housing & Stock Market Networks"]
    D --> C["Process: Bayesian MCMC Estimation<br/>DIC & Regularized Selection"]
    C --> O["Outcome: Captured Network Spillovers<br/>Stronger in Financial Markets"]