Identifying Volatility Signals from Time-Varying Simultaneous Stock Market Interaction
In the academic literature, the economic interpretation of stock market volatility is inherently ambivalent, being considered an indicator of either information flow or uncertainty. We show in a stylized model economy that both views suggest volatility-dependent cross-market spillovers. If higher volatility in one market leads to higher (lower) reactions in another market, volatility reflects information (uncertainty). We introduce a simultaneous time-varying coefficient model, where structural ARCH-type variances serve two purposes: governing the time variation of spillovers and ensuring statistical identification. The model is applied to data of US and further stock markets. Indeed, we find strong nonlinear, volatility-dependent effects.
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