Yes / The link between finance and politics (especially opinion polling) is interesting in both theoretical and empirical terms. Inter alia the election date corresponds to the effective price of an underlying at a known future date. This renders a derivative pricing approach appropriate and, ultimately, to a simplification of the approach suggested by Taleb (2018). Thus, we use an options-pricing approach to predict vote share. Rather than systematic bias in polls forecasting errors appear chiefly due to the mode of extracting election outcomes from the share of the vote. In the 2016 US election polling results put the Republicans ahead in the electoral college from July 2016 onwards. In the 2017 UK general election, though set to be the largest party, a Conservative majority was far from certain.

Yes / We discuss the modelling of corporate bank accounts using a proprietary dataset. We thus offer a principled treatment of a genuine industrial problem. The corporate bank accounts in our study constitute spare, irregularly-spaced time series that may take both positive and negative values. We thus builds on previous models where the underlying is real-valued. We describe an intra-monthly effect identified by practitioners whereby account uncertainty is typically lowest at the beginning and end of each month and highest in the middle. However, our theory also allows for the opposite effect to occur. In-sample applications demonstrate the statistical significance of the hypothesised monthly effect. Out-of-sample forecasting applications offer a 9% improvement compared to a standard SARIMA approach.