This project analyzes the dynamic impacts of trade policy shocks with an SVAR approach, where identification is derived from heteroscedasticity in high frequency data, i.e. here daily, asset price data. This approach has not been applied in this case, according to our knowledge. These data provide enough observations to usefully apply a time-series analysis. Other advantages of the reliance on financial data are that there are no data revisions and that they facilitate an analysis of the distributional consequences of trade policy shocks in terms of countries, sectors or companies gaining/losing most in value in response. Finally, the consideration of asset prices is particularly interesting when longer-term consequences cannot be observed, because asset prices, e.g. stock market prices, incorporate the expectations of market participants. Assuming that the expectations of market participants are informed expectations, they provide a useful measure of the consequences of trade policy shocks, although this “measure” is necessarily noisy when compared to an analysis being conducted many years after these shocks occurred. However, this latter option does not help to inform the urgent policy debate now.