Estimating Model Risk of VaR under Different Approaches: Study on European Banks

• Authors: Aleksandra Helena Pasieczna , Magdalena Joanna Szydłowska

Abstracts

EN

The objective of this research is to estimate the model risk, represented as precision, and the accuracy of the Value at Risk (VaR) measure, under three different approaches: historical simulation (HS), Monte Carlo (MC), and generalized ARCH (GARCH). In this work, to analyze the VaR model, the accuracy and precision were used. Estimation of the accuracy and precision was done under the three approaches for four European banks at 95 and 99% confidence levels. The percentage crossings and Kupiec POF were used to judge the model accuracy, whereas the ratio of the maximum and minimum VaR estimates, and the spread between the maximum and minimum VaR estimates were used to estimate the model risk. This was achieved by changing input parameters, specifically, the estimation time window (125, 250, 500 days). Implications/Recommendations: The accuracy alone is not sufficient to evaluate a model and precision is also required. The temporal evolution of the precision metrics showed that the VaR approaches were inconsistent under different market conditions. This article focuses on the accuracy and precision concepts applied to estimate model risk of the Value at Risk (VaR). VaR is the foundation for sophisticated risk metrics, including systemic risk measures like Marginal Expected Shortfall and Delta Conditional Value at Risk. Thus, understanding the risk associated with the use of VaR is crucial for finance practitioners.

Keywords

EN

VaR; Monte Carlo; model risk; precision; historical simulation; GARCH

• Publisher: Państwowa Wyższa Szkoła Techniczno-Ekonomiczna im. ks. Bronisława Markiewicza w Jarosławiu
• Journal: Współczesne Problemy Zarządzania
• Year: 2021
• Volume: 9
• Issue: 2(19)
• Pages: 65-76

Dates

published

2021

Contributors

author

Aleksandra Helena Pasieczna

• Kozminski University, Warsaw

• https://orcid.org/0000000168673584

author

Magdalena Joanna Szydłowska

• Wroclaw University of Economics and Business

• https://orcid.org/000000016251948X

References

• Basel Committee on Banking Supervision. (2004). Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework. Bank for International Settlements.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
• Danielsson, J., James, K. R., Valenzuela, M., Zer, I. (2016). Model risk of risk models. Journal of Financial Stability, 23, 79–91. https://doi.org/10.1016/j.jfs.2016.02.002
• Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
• Ferenstein, E., Gąsowski, M. (2004). Modelling stock returns with AR-GARCH processes. Statistics and Operations Research Transactions, 28(1), 55–68.
• Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer Science+Business Media.
• Holton, G. A. (2014). Value-at-Risk: Theory and Practice, Second Edition. Value-atrisk. https://www.value-at-risk.net (accessed: 17th November 2021).
• Pasieczna, A. H. (2019). Monte Carlo Simulation Approach to Calculate Value at Risk: Application to WIG20 and MWIG40. Financial Sciences, 24(2), 61–75. https://doi.org/10.15611/fins.2019.2.05

Identifiers

DOI

10.52934/wpz.151

Biblioteka Nauki

2079534