CONSTRUCTION AND APPLICATION OF THE CLASSIFICATION SCHEME OF DYNAMIC RISK MEASURES ESTIMATING

Nataly Zrazhevska

Abstract


The most popular methods for dynamic risk measures – Value-at-Risk (VaR) and Conditional VaR (CVaR) estimating were analyzed, description and comparative analysis of the methods were fulfilled, recommendations on the use were given. Results of the research were presented in the form of a classification scheme of dynamic risk measures estimating that facilitates the choice of an estimation method. The GARCH-based models of dynamic risk measures VaR and CVaR evaluation for artificially generated series and two time series of log return on a daily basis of the most well-known Asian stock indexes Nikkey225 Stock Index and CSI30 were constructed to illustrate the effectiveness of the proposed scheme. A qualitative analysis of the proposed models was conducted. To analyze the quality of the dynamic VaR estimations the Cupets test and the Cristoffersen test were used. For CVaR estimations the V-test was used as quality test. The tests results confirm the high quality of obtained estimations. The proposed classification scheme of dynamic risk measures VaR and CVaR estimating may be useful for risk managers of different financial institutions.


Keywords


Dynamic Value at Risk; dynamic Conditional Value at Risk; heteroscedastic model; Nikkey225 Stock Index; CSI300 Index

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References


Morgan, J. P. (1996). Risk Metrics. Technical Document, 4th edition. New York, 284.

Artzner, P., Delbaen, F., Eber, J.-M., Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9 (3), 203–228. doi: 10.1111/1467-9965.00068

Chun, S. Y., Shapiro, A., Uryasev, S. (2012). Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics. Operations Research, 60 (4), 739–756. doi: 10.1287/opre.1120.1072

Filer, R. K., Žiković, S. (2013). Ranking of VaR and ES Models: Performance in Developed and Emerging Markets. Finance a úvěr-Czech Journal of Economics and Finance, 4 (63), 327–359.

Nadarajah, S., Zhang, B., Chan, S. (2013). Estimation methods for expected shortfall. Quantitative Finance, 14 (2), 271–291. doi: 10.1080/14697688.2013.816767

Bao, Y., Lee, T.-H., Saltoglu, B. (2006). Evaluating predictive performance of value-at-risk models in emerging markets: a reality check. Journal of Forecasting, 25 (2), 101–128. doi: 10.1002/for.977

Guégan, D., Zhao, X. (2013). Alternative modeling for long term risk. Quantitative Finance, 14 (12), 2237–2253. doi:10.1080/14697688.2013.835860

Yoon, S. M., Kang, S. H. (2013). VaR Analysis for the Shanghai Stock Market. The Macrotheme Review, 2 (6), 89–95.

Karadzic, V., Cerovic, J. (2014). Market risk of the Western Balkans countries during the global financial crisis. Economic Annals–XXI, 19–24.

Magadia, J. (2011). Confidence interval for expected shortfall using bootstrap methods. 4th Annual BSP‐UP Professorial Chair Lectures, 21–23.

Chen, S. X. (2007). Nonparametric Estimation of Expected Shortfall. Journal of Financial Econometrics, 6 (1), 87–107. doi: 10.1093/jjfinec/nbm019

Zgurovskyj, M. Z., Pankratova, N. D. (2007). Osnovy systemnogo analizu. Kyiv: BHV, 544.

Tsay, R. S. (2010). Analysis of Financial Time Series. (third edition). Hoboken: John Wiley Sons, Inc., 712.

Bidyuk, P. I., Romanenko, V. D., Tymoshhuk, O. L. (2013). Analiz chasovyh ryadiv. Kyiv: Politehnika, NTUU «KPI», 601.

Embrechts, P., Kaufmann, R., Patie, P. (2005). Strategic Long-Term Financial Risks: Single Risk Factors. Comput Optim Applic, 32 (1-2), 61–90. doi: 10.1007/s10589-005-2054-7

Fiszeder, P., Orzeszko, Wi. (2012). Nonparametric Verification of GARCH-Class Models for Selected Polish Exchange Rates and Stock Indices. Finance a úvěr-Czech Journal of Economics and Finance, 5 (62), 430–449.

Cheong, C. W. (2009). A variance ratio test of random walk inenergy spot markets. Journal of quantitative economics, 1 (8), 106–117.

Kjellson, B. (2013). Forecasting Expected Shortfall. An Extreme Value Approach. Bachelor’s thesis in Mathematical Sciences, 43.

Zrazhevskaja, N. G., Zrazhevskij, A. G. (2016). Klassifikacija mer riska dlja odnoj sluchajnoj velichiny. Systemni doslidzhennya ta informacijni texnologiyi, 2.

Pankratova, N. D., Zrazhevskaja, N. G. (2015). Model' avtokorreljacionnoj funkcii vremennogo rjada s sil'noj zavisimost'ju. Problemy upravlenija i informatiki, 5, 102–112.

González-Rivera, G., Lee, T.-H., Yoldas, E. (2007). Optimality of the RiskMetrics VaR model. Finance Research Letters, 4 (3), 137–145. doi: 10.1016/j.frl.2007.06.001

McMillan, D. G., Kambouroudis, D. (2009). Are RiskMetrics forecasts good enough? Evidence from 31 stock markets. International Review of Financial Analysis, 18 (3), 117–124. doi:10.1016/j.irfa.2009.03.006

Taylor, J. W. (2008). Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall. Journal of Financial Econometrics, 6 (3), 382–406. doi:10.1093/jjfinec/nbn007

Engle, R. F., Magnelli, S. (1999). CaViaR: Conditional Autoregressive Value at Risk by Quantile Regression. NBER: Working paper, 7341.

Dowd, K. (2005). Measuring Market Risk (second edition). Chichester: John Wiley and Sons. doi: 10.1002/9781118673485




DOI: http://dx.doi.org/10.21303/2461-4262.2016.00162

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