Abstract
The aim of this research was to analyze the model risk of Expected Shortfall and Value at Risk in different configurations. The study was done using the close market prices of financial institutions between 2000 and 2020, from the Paris and Frankfurt stock exchanges. We empirically demonstrated a quantitative estimation of the precision metrics for various configurations of Value at Risk and Expected Shortfall computed using four different Monte Carlo approaches. We used two precision metrics to judge these models—the ratio and the spread. The precision provides an estimate of the reproducibility of the model (variability) and we propose using functions of the precision to represent the risk of the outcome of the model.
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References
Basel II (2004) Basel II: International convergence of capital measurement and capital standards: a revised framework. https://www.bis.org/publ/bcbs107.htm. Accessed 20 Dec 2019
Basel III (2010). https://www.bis.org/press/p100912.pdf. Accessed 20 Aug 2020
Boucher CM, Danielsson J, Kouontchou PS, Maillet BB (2014) Risk models-at-risk. J Bank Financ 44:72–92
Crouhy M, Galai D, Mark R (1998) Model risk. J Financ Eng 7:267–288
Danielsson J et al (2016) Model risk of risk model. J Financ Stability 23(C):79–91
Derman E (1996) Model risk: what are the assumptions made in using models to value securities and what are the consequent risks? Risk-London-Risk Magazine Limited 9:34–38
Glasserman P (2003) Monte Carlo methods in financial engineering. Springer Science+Business Media, New York, NY
Glasserman P, Xu X (2014) Robust risk measurement and model risk. Quantitative Financ 14(1):29–58
Guegan D, Hassani B, Li K (2017) Impact of multimodality of distributions on VaR and ES calculations. Documents de travail du Centre d’Economie de la Sorbonne 17019, Université Panthéon-Sorbonne (Paris 1), Centre d’Economie de la Sorbonne
Hendricks D (1996) Evaluation of value-at-risk models using historical data. Econ Policy Rev 2(1)
Holton GA (2014) Value-at-risk theory and practice, 2nd edn (self-published). https://www.value-at-risk.net. Accessed 20 Aug 2020
Investpy (2020) investpy – Financial Data Extraction from Investing.com with Python. developed by Alvaro Bartolome del Canto. https://github.com/alvarobartt/investpy. Accessed 01 Sept 2020
Pasieczna AH (2019) Monte Carlo Simulation Approach to Calculate Value at Risk: Application to WIG20 and MWIG40. Financial Sciences, Wrocław University of Economics, Wrocław 24(2):61–75
Sibbertsen P, Stahl G, Luedtke C (2008) Measuring model risk. J Risk Model Valid 2(4):65–82
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Pasieczna, A.H. (2021). Model Risk of VaR and ES Using Monte Carlo: Study on Financial Institutions from Paris and Frankfurt Stock Exchanges. In: Jajuga, K., Locarek-Junge, H., Orlowski, L.T., Staehr, K. (eds) Contemporary Trends and Challenges in Finance . Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-73667-5_5
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DOI: https://doi.org/10.1007/978-3-030-73667-5_5
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