To investigate the association between return to scale (RTS) and profitability in the United Kingdom banking sector, we adopted logistic regression analysis, using sample sizes of 135, 140, and 121 banks for the years 2016, 2015, and 2014, respectively. Our findings indicate a positive and statistically significant association between profits as measured by return on assets (ROA) and increasing RTS during the three years of the sample period. We also investigated the relationship between bank size as represented by the log of total deposits and RTS. Our findings also indicate that bigger banks show increasing RTS, but with decreasing rate, as represented by the negative coefficient of the square of the log of deposits. To investigate further the link between bank size and operating cost with ROA, we employed panel data regression, covering the sample period (2011-2016) for the largest 25 banks. Our results show that there is a positive and significant association between ROA and the total assets of the largest banks, but the operating expenses impact negatively on the ROA. More specifically, 1% increase in total assets increase ROA by 2, and 1% increase in the operating expenses reduce ROA by 1.7%. These results imply that bigger banks in the United Kingdom’s banking sector are able to gain competitive edge in attracting deposits as they operate along the downward sloping portion of average operating cost curve.

This study examines probable dynamic spillover transmissions between the Nigerian stock and money markets using the multivariate volatility framework that simultaneously accounts for both returns and shock spillovers. Based on relevant pre-tests, the VARMA-CCC-GARCH framework is selected and consequently employed to model the spillovers. The study finds significant cross-market return and shock spillovers between the two markets. Thus, a shock to one market is more likely to spill over to the other market. It is also observed that shocks have persistent effects on stock market volatility but transitory effects on money market volatility. In other words, shocks to the money market die out over time while shocks to stock market tend to persist over time. In addition, including lagged own shocks and lagged own conditional variance when forecasting the future volatility of both return series may enhance their forecast performance. An alternative approach proposed by Diebold and Yilmaz (2012) is also employed for robustness and the results are consistent with those obtained from the VARMA-CCC-GARCH model.