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on Forecasting |
| By: | Hyeongwoo Kim; Jisoo Son |
| Abstract: | Charge-offs signal important information about the riskiness of loan portfolios in the banking system, which can generate systemic risk towards deep recessions. We compiled the net charge-off rate (COR) data of the top 10 bank holding companies (BHCs) in the U.S. utilizing consolidated financial statements. We propose factor-augmented forecasting models for CORs by estimating latent common factors, including targeted factors, via an array of data dimensionality reduction methods for a large panel of macroeconomic predictors. Our models outperform the benchmark models especially well for business loan CORs, while enhancing predictive contents for consumer loans are harder at short horizons. Real activity factors enhance the out-of-sample predictability for business loan CORs even when financial sector factors are excluded. |
| Keywords: | Net Charge-Off Rate; Bank Holding Companies; Principal Component Analysis; Partial Least Squares; Out-of-Sample Forecast |
| JEL: | C38 C53 C55 G01 G17 |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:abn:wpaper:auwp2023-02&r=for |
| By: | Hana, Yuefeng; Chenb, Rong; Zhangb, Cun-Hui; Yao, Qiwei |
| Abstract: | We propose a contemporaneous bilinear transformation for a p × q matrix time series to alleviate the difficulties in modeling and forecasting matrix time series when p and/or q are large. The resulting transformed matrix assumes a block structure consisting of several small matrices, and those small matrix series are uncorrelated across all times. Hence, an overall parsimonious model is achieved by modeling each of those small matrix series separately without the loss of information on the linear dynamics. Such a parsimonious model often has better forecasting performance, even when the underlying true dynamics deviates from the assumed uncorrelated block structure after transformation. The uniform convergence rates of the estimated transformation are derived, which vindicate an important virtue of the proposed bilinear transformation, that is, it is technically equivalent to the decorrelation of a vector time series of dimension max(p, q) instead of p × q. The proposed method is illustrated numerically via both simulated and real data examples. Supplementary materials for this article are available online. |
| Keywords: | decorrelation transformation; eigenanalysis; matrix time series; forecasting; uniform convergence rates; grant IIS-1741390. Chen was supported in part by National Science Foundation grants DMS-1503409; DMS-1737857 and IIS-1741390. Zhang was supported in part by NSF grants DMS-1721495; IIS-1741390 and CCF-1934924.; EP/V007556/1; T&F deal |
| JEL: | C1 |
| Date: | 2023–01–11 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:117386&r=for |
| By: | Jack Fosten; Daniel Gutknecht; Marc-Oliver Pohle |
| Abstract: | Quantile forecasts made across multiple horizons have become an important output of many financial institutions, central banks and international organisations. This paper proposes misspecification tests for such quantile forecasts that assess optimality over a set of multiple forecast horizons and/or quantiles. The tests build on multiple Mincer-Zarnowitz quantile regressions cast in a moment equality framework. Our main test is for the null hypothesis of autocalibration, a concept which assesses optimality with respect to the information contained in the forecasts themselves. We provide an extension that allows to test for optimality with respect to larger information sets and a multivariate extension. Importantly, our tests do not just inform about general violations of optimality, but may also provide useful insights into specific forms of sub-optimality. A simulation study investigates the finite sample performance of our tests, and two empirical applications to financial returns and U.S. macroeconomic series illustrate that our tests can yield interesting insights into quantile forecast sub-optimality and its causes. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.02747&r=for |