This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted HAR model, HARP, where predictors are constructed from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000–2020) and via Monte Carlo simulations that the HARP models produce significantly better forecasts across all forecasting horizons. We also show that adjusting for periodicity when estimating the variance risk premium improves return predictability.

According to the “cost efficiency - liquidity creation” hypothesis (CELCH), introduced in this paper, a rise in a bank’s cost efficiency level increases its liquidity creation. By employing a novel stress test scenario under a panel VAR methodology, the CELCH and the direction of causality between liquidity creation and cost efficiency are tested. Moreover, using new measures of liquidity creation (Berger and Bouwman, 2009), the question of whether potential bank mergers and acquisitions (M&As) can enhance liquidity creation and generate additional credit channels in the economy is addressed. The robustness of potential consolidation scenarios are evaluated and compared through the use of new “half-life” measures (Chortareas and Kapetanios, 2013). In line with CELCH, the positive impact of cost efficiency on liquidity creation is shown. The empirical evidence further suggests that potential consolidation activity can enhance the flow of credit in the economy. Bank shocks seem to have the most persistent effect on both liquidity creation and cost efficiency. Finally, doubts are cast on the strategies followed by policy authorities regarding the recent wave of M&As in the banking sector.