Understanding high-dimensional dependencies in modern financial systems requires time series models that capture both contemporaneous and dynamic linkages. This study develops a sparse spatio-temporal vector autoregressive framework to analyse the network structure of the Korean won exchange rate against 36 major trading-partner currencies. The model combines the generalised Yule–Walker equations with structured penalisation to jointly estimate instantaneous and lagged interactions in a data-driven manner. This approach allows for the recovery of economically meaningful spillover networks while maintaining tractability in high dimensions. Using daily data from 2019 to 2023, the results reveal pronounced contemporaneous spillovers among currencies closely tied to Korea’s trade and financial networks, notably the U.S. dollar, Chinese yuan, Japanese yen, and key ASEAN currencies. Monte Carlo simulations confirm the estimator’s consistency and convergence properties, while empirical forecasting exercises demonstrate systematic improvements in both mean-squared and robust error metrics relative to benchmark VAR and spatial autoregressive models. The evidence highlights that modelling sparse, high-dimensional time series structures enhances predictive accuracy and interpretability, particularly under nonstationary and heterogeneous conditions. The proposed framework provides a flexible tool for exploring interconnected time series in economics and finance, offering new insights into exchange-rate linkages and risk transmission in globally integrated markets.
Han et al. (2026) studied this question.