- Author
- Serwa Dobromił (Szkoła Główna Handlowa w Warszawie)
- Title
- Overlapping Observations in Credit Risk Models
- Source
- Econometric Research in Finance, 2022, vol. 7, nr 2, s. 193-211, bibliogr. 17 poz.
- Keyword
- Ryzyko kredytowe, Regresja logistyczna, System bankowy
Credit risk, Logistic regression, Banking system - Note
- JEL classification: C13, C23, C25, E51
summ. - Abstract
- Parameters in logistic regression models for probability of default are typically estimated using the maximum likelihood method. The aim of this paper is to verify whether the use of overlapping observations improves precision or causes deterioration of estimation results in these models. Our Monte Carlo simulations demonstrate that the difference between parameter estimates using all overlapping observations in a sample and only non-overlapping observations in a reduced sample is not statistically significant, but the variance of parameter estimates is reduced when overlapping observations are used.(original abstract)
- Accessibility
- The Library of Warsaw School of Economics
- Full text
- Show
- Bibliography
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- Albert, A. Anderson, J. A. (1984). On the Existence of Maximum Likelihood Estimates in Logistic Regression Models." Biometrika", 71(1):1-10.
- Boudoukh, J., Israel, R., Richardson, M. P. (2020). Biases in Long-Horizon Predictive Regressions. Working Paper 27410, National Bureau of Economic Research, Inc.
- Boylan, J. E. Babai, M. Z. (2016). On the Performance of Overlapping and Non-overlapping Temporal Demand Aggregation Approaches". International Journal of Production Economics", 181:136-144.
- Britten-Jones, M., Neuberger, A., Nolte, I. (2011). Improved Inference in Regression with Overlapping Observations. "Journal of Business Finance & Accounting", 38(5-6):657-683.
- Chinn, M. D. (2006). The (Partial) Rehabilitation of Interest Rate Parity in the Floating Rate Era: Longer Horizons, Alternative Expectations, and Emerging Markets." Journal of International Money and Finance", 25(1):7-21.
- Cipollini, A. Fiordelisi, F. (2012). Economic Value, Competition and Financial Distress in the European Banking System." Journal of Banking & Finance", 36(11):3101-3109.
- Darvas, Z. (2008). Estimation Bias and Inference in Overlapping Autoregressions: Implications for the Target-Zone Literature. "Oxford Bulletin of Economics and Statistics", 70(1):1-22.
- European Banking Authority (2017). Guidelines on PD Estimation, LGD Estimation and the Treatment of Defaulted Exposures.
- Frankland, R., Smith, A. D., Sharpe, J., Bhatia, R., Jarvis, S., Jakhria, P., Mehta, G. (2019). Calibration of VaR Models with Overlapping Data. "British Actuarial Journal", 24.
- Hansen, L. P. Hodrick, R. J. (1980). Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis. "The Journal of Political Economy", 88(5):829-853.
- Kiesel, R., Perraudin, W., Taylor, A. (2001). The Structure of Credit Risk: Spread Volatility and Ratings Transitions. Working Paper 131, Bank of England.
- Lioui, A. Poncet, P. (2019). Long Horizon Predictability: An Asset Allocation Perspective. "European Journal of Operational Research", 278(3):961-975.
- Mamingi, N. (2017). Beauty and Ugliness of Aggregation Over Time: A Survey. "Review of Economics", 68(3):205-227.
- Richardson, M. Smith, T. (1991). Tests of Financial Models in the Presence of Overlapping Observations." The Review of Financial Studies, 4(2):227-254.
- Rossana, R. Seater, J. (1995). Temporal Aggregation and Economic Time Series. Journal of Business & Economic Statistics", 13(4):441-51.
- Xu, K.-L. (2021). On the Serial Correlation in Multi-Horizon Predictive Quantile Regression. Economics Letters, 200(C).
- Zwetsloot, I. M. Woodall, W. H. (2021). A Review of Some Sampling and Aggregation Strategies for Basic Statistical Process Monitoring." Journal of Quality Technology", 53(1):1-16.
- Cited by
- ISSN
- 2451-1935
2451-2370 - Language
- eng
- URI / DOI
- DOI: https://doi.org/10.2478/erfin-2022-0007
