Figure 4 - uploaded by Kevin Moran
Content may be subject to copyright.
Source publication
This paper develops a monitoring and forecasting model for the aggregate monthly number of commercial bank failures in the U.S. We extract key sectoral predictors from the large set of macroeconomic variables proposed by McCracken and Ng (2016) and incorporate them in a hurdle negative binomial (HNB) model to predict the number of monthly commercia...
Contexts in source publication
Context 1
... first indicator of the superior forecasting ability of the HNB model is represented in Figure 4. The figure reports the root-mean-squared and mean-absolute errors across the 12 forecasting horizons considered in the exercise, for the standard Poisson model (diamonds) and our HNB framework (squares), in the case where no lagged values of the dependent variable are used. ...
Context 2
... then repeat this analysis for the model versions where lagged values of the dependent variables are used as additionnal predictors; Figure 7, 8 and 9 report the results thus obtained. Overall, the quality of the forecasts appear to improve markedly with this addition: compare for example the scales of the Y-axis in Figure 7 relative to that in Figure 4. As such our formal tests comparing the forecasting performance of different models will be for the cases where the lagged values of bank failures are used (see below). ...
Similar publications
The issue of Non-Performing Assets (NPA) in the banks is discussed. The magnitude and trend in NPA are studied for the 5 year period 2008-13, using a suitable classification of the banks. A critical evaluation of the reasons and a few recommendations are made which have positive practical implications.
Citations
... Cui's research endeavors to provide a comprehensive understanding of the factors that underlie the prediction of bank failures. Gnagne and Moran (2020) have offered a distinctive model for predicting the total monthly number of commercial bank failures in the United States. Their model is founded on macroeconomic variables, initially proposed by McCracken and Ng (2016). ...
This empirical research endeavor seeks to enhance the accuracy of forecasting time series
data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied
on daily closed price index data, spanning from October 2014 to December 2022, encompassing a
total of 2048 observations. To attain statistically significant results, the research employs various
mathematical techniques, including the non-linear spectral model, the maximum overlapping discrete
wavelet transform (MODWT) based on the Coiflet function (C6), and the autoregressive integrated
moving average (ARIMA) model. Notably, the study’s findings encompass the comprehensive
explanation of all past events within the specified time frame, alongside the introduction of a novel
forecasting model that amalgamates the most effective MODWT function (C6) with a tailored ARIMA
model. Furthermore, this research underscores the effectiveness of MODWT in decomposing stock
market data, particularly in identifying significant events characterized by high volatility, which
thereby enhances forecasting accuracy. These results hold valuable implications for researchers
and scientists across various domains, with a particular relevance to the fields of business and
health sciences. The performance evaluation of the forecasting methodology is based on several
mathematical criteria, including the mean absolute percentage error (MAPE), the mean absolute
scaled error (MASE), and the root mean squared error (RMSE).
