Details about Marcin Chlebus

Access statistics for papers by Marcin Chlebus.

Last updated 2022-12-05. Update your information in the RePEc Author Service.

Short-id: pch1469

Working Papers

2021

1. Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts
   Working Papers, Faculty of Economic Sciences, University of Warsaw
2. Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models
   Papers, arXiv.org View citations (3)
3. GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks
   Working Papers, Faculty of Economic Sciences, University of Warsaw
4. HCR & HCR-GARCH – novel statistical learning models for Value at Risk estimation
   Working Papers, Faculty of Economic Sciences, University of Warsaw
5. Machine learning in the prediction of flat horse racing results in Poland
   Working Papers, Faculty of Economic Sciences, University of Warsaw
6. Predicting football outcomes from Spanish league using machine learning models
   Working Papers, Faculty of Economic Sciences, University of Warsaw
7. The effectiveness of Value-at-Risk models in various volatility regimes
   Working Papers, Faculty of Economic Sciences, University of Warsaw View citations (1)

2020

8. Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools
   Working Papers, Faculty of Economic Sciences, University of Warsaw
9. HRP performance comparison in portfolio optimization under various codependence and distance metrics
   Working Papers, Faculty of Economic Sciences, University of Warsaw
10. Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model
    Working Papers, Faculty of Economic Sciences, University of Warsaw
11. Novel multilayer stacking framework with weighted ensemble approach for multiclass credit scoring problem application
    Working Papers, Faculty of Economic Sciences, University of Warsaw
12. Nvidia’s stock returns prediction using machine learning techniques for time series forecasting problem
    Working Papers, Faculty of Economic Sciences, University of Warsaw View citations (1)
    See also Journal Article Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem, Central European Economic Journal, Sciendo (2021) View citations (1) (2021)
13. Size does matter. A study on the required window size for optimal quality market risk models
    Working Papers, Faculty of Economic Sciences, University of Warsaw
14. So close and so far. Finding similar tendencies in econometrics and machine learning papers. Topic models comparison
    Working Papers, Faculty of Economic Sciences, University of Warsaw
15. Towards better understanding of complex machine learning models using Explainable Artificial Intelligence (XAI) - case of Credit Scoring modelling
    Working Papers, Faculty of Economic Sciences, University of Warsaw

2019

16. Old-fashioned parametric models are still the best. A comparison of Value-at-Risk approaches in several volatility states
    Working Papers, Faculty of Economic Sciences, University of Warsaw View citations (3)

2017

17. Is CAViaR model really so good in Value at Risk forecasting? Evidence from evaluation of a quality of Value-at-Risk forecasts obtained based on the: GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and the historical simulation models depending on the stability of financial markets
    Working Papers, Faculty of Economic Sciences, University of Warsaw

2016

18. EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk
    Working Papers, Faculty of Economic Sciences, University of Warsaw View citations (2)
    See also Journal Article EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk, Central European Economic Journal, Sciendo (2017) View citations (3) (2017)
19. One-Day Prediction of State of Turbulence for Portfolio. Models for Binary Dependent Variable
    Working Papers, Faculty of Economic Sciences, University of Warsaw View citations (3)

Journal Articles

2021

1. Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem
   Central European Economic Journal, 2021, 8, (55), 44-62 View citations (1)
   See also Working Paper Nvidia’s stock returns prediction using machine learning techniques for time series forecasting problem, Working Papers (2020) View citations (1) (2020)

2020

2. Ridesharing in the Polish Experience: A Study using Unified Theory of Acceptance and Use of Technology
   Central European Economic Journal, 2020, 7, (54), 279-299

2019

3. Comparison of Block Maxima and Peaks Over Threshold Value-at-Risk models for market risk in various economic conditions
   Central European Economic Journal, 2019, 6, (53), 70-85
   Also in Central European Economic Journal, 2019, 6, (53), 70-85 (2019)

2018

4. Comparison of Semi-Parametric and Benchmark Value-At-Risk Models in Several Time Periods with Different Volatility Levels
   Financial Internet Quarterly (formerly e-Finanse), 2018, 14, (2), 67-82 View citations (1)
5. One-day-ahead forecast of state of turbulence based on today's economic situation
   Equilibrium. Quarterly Journal of Economics and Economic Policy, 2018, 13, (3), 357-389

2017

6. EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk
   Central European Economic Journal, 2017, 3, (50), 01-25 View citations (3)
   See also Working Paper EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk, Working Papers (2016) View citations (2) (2016)

2014

7. One-day prediction of state of turbulence for financial instrument based on models for binary dependent variable
   Ekonomia journal, 2014, 37

Chapters

2016

1. Can Lognormal, Weibull or Gamma Distributions Improve the EWS-GARCH Value-at-Risk Forecasts?
   Chapter 4 in Statistical Review, vol. 63, 2016, 3, 2016, vol. 63, pp 329-350