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  • Comparative Analysis of Statistical Methods for Backtesting Value-at-Risk Models and their Application to Russian Financial Market

Comparative Analysis of Statistical Methods for Backtesting Value-at-Risk Models and their Application to Russian Financial Market

Student: Nikolaev Ian

Supervisor: Victor A Lapshin

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Final Grade: 9

Year of Graduation: 2019

Value-at-Risk, or VaR, has been recognized not only as a benchmark risk measure but also a powerful instrument of enterprise risk management. As such, VaR is commonly used to set and monitor risk limits or facilitate strategic capital allocation within financial entities. Until recently VaR has been also implemented as a part of a regulatory framework in the banking sector and used to determine minimum amount of capital to be held against market risk. Nevertheless, despite extensive use in the industry, VaR models still lack proper backtesting methodology. Though a number of backtesting techniques has been suggested for VaR models, none of them is recognized as an industry standard or approved by regulation authorities. In this paper a selection of such backtesting techniques is evaluated through the Monte Carlo experiment. It is found that the backtests suffer from substantial size distortions in finite sample settings. Once these distortions are properly addressed, the duration-based backtests offer superior empirical power. Results of size and power simulations presented in this paper have immediate practical implications regarding the backtest-ing process in financial entities and current regulatory framework.

Full text (added May 10, 2019)

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