Risk estimation using the normal inverse Gaussian distribution JH Venter, PJ De Jongh Potchefstroomse Universiteit vir Christelike Hoër Onderwys, 2001 | 128 | 2001 |
A review of operational risk in banks and its role in the financial crisis E de Jongh, R de Jongh, D de Jongh, G van Vuuren South African Journal of Economic and Management Sciences 16 (4), 364-382, 2013 | 65 | 2013 |
The impact of pre-selected variance inflation factor thresholds on the stability and predictive power of logistic regression models in credit scoring PJ de Jongh, E de Jongh, M Pienaar, H Gordon-Grant, M Oberholzer, ... ORION 31 (1), 17-37, 2015 | 52 | 2015 |
Mallows-type bounded-influence-regression trimmed means PJ De Jongh, T De Wet, AH Welsh Journal of the American Statistical Association 83 (403), 805-810, 1988 | 51 | 1988 |
A proposed best practice model validation framework for banks PJ de Jongh, J Larney, E Mare, G van Vuuren, T Verster | 26 | 2016 |
Selecting an innovation distribution for GARCH models to improve efficiency of risk and volatility estimation JH Venter, PJ De Jongh Journal of Risk 6 (3), 2004 | 23 | 2004 |
Nig-Garch models based on open, close, high and low prices: theory and methods JR Venter, PJ De Jongh, G Griebenow South African statistical journal 39 (2), 79-101, 2005 | 17 | 2005 |
Implementing the countercyclical capital buffer in South Africa: Practical considerations. P BURRA, PJ DE JONGH, H RAUBENHEIMER, G VAN VUUREN, H WIID South African Journal of Economic and Management Sciences 18 (1), 1-13, 2014 | 16 | 2014 |
An introduction to neural networks PJ de Jongh, T de Wet South African Statistical Journal 27, 103-128, 1993 | 16 | 1993 |
Combining scenario and historical data in the loss distribution approach: A new procedure that incorporates measures of agreement between scenarios and historical data. PJ de Jongh, T de Wet, H Raubenheimer, JH Venter Journal of Operational Risk 10 (1), 1-31, 2015 | 15 | 2015 |
Extended stochastic volatility models incorporating realised measures JH Venter, PJ de Jongh Computational Statistics & Data Analysis 76, 687-707, 2014 | 15 | 2014 |
Combining Vasicek and robust estimators for forecasting systematic risk GS Cloete, PJ de Jonah, T De Wet Investment Analysts Journal 31 (55), 37-44, 2002 | 10 | 2002 |
Future: A knowledge-based system for threat assessment PJ De Jongh, KJ Carden, NA Rogers Interfaces 24 (2), 76-86, 1994 | 10 | 1994 |
Trimmed mean and bounded influence estimators for the parameters of the AR (1) process PJ De Jongh, T De Wet Communications in Statistics-Theory and Methods 14 (6), 1361-1375, 1985 | 10 | 1985 |
A critical review of the Basel margin of conservatism requirement in a retail credit context R De Jongh, T Verster, E Reynolds, M Joubert, H Raubenheimer International Business & Economics Research Journal (IBER) 16 (4), 257-274, 2017 | 9 | 2017 |
Designing and Implementing Industry Directed Training and Research Programmes with a Statistical Science Core: The BMI experience PJ DE JONGH, CM ERASMUS South African Journal of Science 10 (11/12), 17-24, 2014 | 7* | 2014 |
A motivation for banks in emerging economies to adapt agency ratings when assessing corporate credit T Verster, R De Jongh, S Greenberg, E Fourie, D de Wet South African Journal of Economic and Management Sciences 22 (1), 1-11, 2019 | 6 | 2019 |
A Simulation Comparison of Quantile Approximation Techniques for Compound Distributions popular in Operational Risk PJ de Jongh, T de Wet, K Panman, H Raubenheimer Journal of Operational Risk 11 (1), 23-48, 2016 | 6 | 2016 |
The impact of PD-LGD correlation on expected loss and economic capital G Van Vuuren, R De Jongh, T Verster Klute Institute, 2017 | 5 | 2017 |
GARCH-type volatility models based on Brownian inverse Gaussian intra-day return processes JH Venter, PJ De Jongh, G Griebenow The Journal of Risk 8 (4), 97, 2006 | 5 | 2006 |