Bank performance variability and strands of inflationary conditions

Abstract

PurposeThis study seeks to examine the extent to which strands of inflationary related conditions (inflation expectations, inflation uncertainty and realized inflation); macroeconomic uncertainty and the likelihood of recessionary conditions influence performance indicators in the US banking sector over a specified time period.Design/methodology/approachThe study adopts seemingly unrelated regression model (SUR) advanced by Zellner (1962) in its examination of how specific strands of inflationary conditions, and other adverse macroeconomic conditions influence performance dynamics in the US banking sector.FindingsEmpirical evidence suggest that among various adverse macroeconomic conditions examined, inflation expectations and macroeconomic uncertainty tend to have significant constraining impact on key performance indicators in the US banking sector than other conditions examined. Comparatively, this study finds that inflation expectations and macroeconomic uncertainty tend to have much more constraining impact on return on equity, than on return on assets in the US banking sector. Results further suggest that among the three bank performance indicators examined, net interest margin is the least vulnerable bank performance indicator to various adverse macroeconomic conditions examined in the study.Practical implicationsApart from the various empirical results noted above, this study's findings are projected to help inform strategic planning decisions among institutions in the banking sector. The various findings could, for instance, inform policies and operational strategies geared toward reducing vulnerability associated with specific performance indicators such as return on equity. This reduction could be achieved by critically examining how the various performance indicators react to individual adverse macroeconomic conditions examined in this study. The process could ultimately help in developing tailored measures/procedures aimed at reducing how susceptible key performance indicators are to the various adverse macroeconomic conditions. This study's findings could also provide the platform for more adaptive policies aimed at minimizing the effects of noted macroeconomic conditions on operational efficiency in the banking sector.Originality/valueThe uniqueness of this study, compared to related ones found in the literature, stems from its treatment of three variant of related strands of macroeconomic condition (different variant of inflationary conditions) in the same framework in its empirical analysis.

Financial development and institutional quality among emerging economies

“…GARCH process employed in this study takes a cue from notable research studies that used the procedure in respective studies and have confirmed its effectiveness. These works include Abaidoo and Agyapong (2021), Abaidoo and Anyigba (2020), Gö kbulut and Pekkaya (2014) and Asteriou and Price (2005), to mention but a few. GARCH is a statistical model for analyzing time series data in which the variance of the error term is serially autocorrelated.…”

“…The process theorizes that the derivative of the lags of a variable denote its conditional variance; the variance of the stochastic term therefore captures the uncertainty data from the base variable. According to Abaidoo and Anyigba (2020), the GARCH framework captures fluctuations or volatility associated with a base variable as a measure of instability associated with the variable. The GARCH (1,1) equation used in deriving the data for macroeconomic uncertainty and inflation uncertainty variables is presented below.…”

Commodity price volatility and stock market returns in an emerging economy

“…We adopt this approach in deriving the volatility data as against other approaches including using the standard deviation because volatility in price of such key commodities presents a state of uncertainty that could affect key sectors of an economic setting including the returns on the stock exchange. This approach is adopted following its application by notable research works including Abaidoo and Kwenin (2013), Gokbulut and Pekkaya (2014), and Abaidoo and Anyigba (2020). This model is presented in equation () below.…”

Macroeconomic Risk and Political Stability: Perspectives from Emerging and Developing Economies

“…It is worth emphasizing that this approach has been used extensively in the literature by various authors. Mention could be made of such studies, including Asteriou and Price (2005), Akgul and Sayyan ( 2005), Fountas and Karanasos (2007) and Abaidoo and Anyigba (2020), among others, that have resorted to employing GARCH to derive volatility data. In GARCH modelling, the conditional variance depends on its own lags.…”