Exponential Spatiotemporal GARCH Model with Asymmetric Volatility Spillovers
By: Ariane Nidelle Meli Chrisko, Philipp Otto, Wolfgang Schmid
Potential Business Impact:
Predicts how money shocks spread through markets.
This paper introduces a spatiotemporal exponential generalised autoregressive conditional heteroscedasticity (spatiotemporal E-GARCH) model, extending traditional spatiotemporal GARCH models by incorporating asymmetric volatility spillovers, while also generalising the time-series E-GARCH model to a spatiotemporal setting with instantaneous, potentially asymmetric volatility spillovers across space. The model allows for both temporal and spatial dependencies in volatility dynamics, capturing how financial shocks propagate across time, space, and network structures. We establish the theoretical properties of the model, deriving stationarity conditions and moment existence results. For estimation, we propose a quasi-maximum likelihood (QML) estimator and assess its finite-sample performance through Monte Carlo simulations. Empirically, we apply the model to financial networks, specifically analysing volatility spillovers in stock markets. We compare different network structures and analyse asymmetric effects in instantaneous volatility interactions.
Similar Papers
A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for Volatility in Financial Networks
Statistical Finance
Predicts how stock prices will jump together.
A new implementation of Network GARCH Model
Methodology
Predicts stock price swings by looking at neighbors.
Graph Signal Processing for Global Stock Market Realized Volatility Forecasting
General Finance
Predicts stock market ups and downs better.