Posts

I develop a multi-sector model to examine the dynamics of inter-sector idiosyncratic volatility spillovers within a supplier-customer network. I characterize dynamic structure changes of pairwise risk spillovers using two well-defined factors: Risk-Intensity and Risk-Dominance, and demonstrate their impact on aggregate volatility. Risk-Intensity captures the average magnitude of risk spillovers over time, with higher Intensity facilitating the amplification of sector-specific shocks through the network and increasing aggregate volatility. Risk-Dominance measures the extent to which a few sectors dominate risk spillovers. Higher Dominance reflects more limited shock transmission paths, promoting faster risk diversification and quicker mean reversion of aggregate volatility. Empirically, I construct these factors using stock data and demonstrate their predictability for aggregate volatility. I also show that they are priced consistently with the model’s implications: long-short portfolios formed based on Risk-Intensity and Risk-Dominance betas yield annual return spreads of -4% and +5%, respectively.