We introduce the spline-DCS model with a dynamic cubic spline as a way of capturing periodic behavior in financial data that evolves over time. Our empirical application provides evidence for changing diurnal patterns in the high-frequency financial data we study. We illustrate that this generalization can lead to an improvement in the quality of the fit of the model to the empirical distribution of data, especially in the tail region, for an extended out-of-sample period. Moreover, it can lead to a substantial improvement in predicting intra-day volume proportions, which is useful for Volume-Weighted Average Price stratategies. Our novel approach gives new insights into regular trading behavior and how it responds to changing market conditions.

seasonality