Dergiler / Gazi University Journal of Science / 2016 / Cilt: 29 - Sayı: 3

Garch-EVT Model

FORECASTING VALUE-AT-RISK WITH NOVEL WAVELET BASED GARCH-EVT MODEL

Sayfa
599–614
Yayın Tarihi
2016-09-30
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Empirical findings show that wavelet based GARCH-EVT model is outperformed at high quantiles according to backtesting results.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this study, wavelet based GARCH-Extreme Value Theory (EVT) is proposed to model financial return series to forecast daily value-at-risk.
📊
Temel Bulgular & Veri:Wavelets based GARCH-EVT is hybrid model combining the wavelet analysis and EVT.
💡
Sonuç & Katkı:Empirical findings show that wavelet based GARCH-EVT model is outperformed at high quantiles according to backtesting results.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:GARCH models · volatility forecasting · combining forecasts

Abstract

In this study, wavelet based GARCH-Extreme Value Theory (EVT) is proposed to model financial return series to forecast daily value-at-risk. Wavelets based GARCH-EVT is hybrid model combining the wavelet analysis and EVT. Proposed model contains three stages. In first stage, return series is decomposed into wavelet series and approximation series by applying the maximal overlap discrete wavelet transform. Second stage, detrended return series and approximation series are obtained by using wavelet series and scaling series. GARCH model is fitted to each obtained series to forecast daily volatility. Final stage, EVT is used to estimate quantile estimation of standardized residuals of GARCH model obtained for detrended return series and daily VaR value is forecasted by using volatility forecasts and quantile estimation. Daily VaR forecasting accuracy of proposed hybrid model is compared with the GARCH models specified under heavy-tailed distributions and GARCH-EVT model. Empirical findings show that wavelet based GARCH-EVT model is outperformed at high quantiles according to backtesting results.

🔖

Bu Makaleye Atıf Yap

ALTUN, E., TATLİDİL, H. (2016). Garch-EVT Model. Gazi University Journal of Science, Cilt: 29 - Sayı: 3, 599–614. https://ajindex.com/articles/254715

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (18)

  1. McNeil, A. J., & Frey, R., Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of empirical finance, 7(3), (2000), 271-300.
  2. Gencay, R., & Selcuk, F., Extreme value theory and Value-at-Risk: Relative performance in emerging markets. International Journal of Forecasting, 20(2), (2004), 287-303.
  3. Gilli, M., An application of extreme value theory for measuring financial risk. Computational Economics, 27(2-3), (2006), 207-228.
  4. Onour, I. A., Extreme risk and fat-tails distribution model: empirical analysis. Journal of Money, Investment and Banking, (13), (2010).
  5. Singh, A. K., Allen, D. E., & Robert, P. J., Extreme market risk and extreme value theory. Mathematics and computers in simulation, 94, (2013), 310-328.
  6. Soltane, H. B., Karaa, A., & Bellalah, M., Conditional VaR Using GARCH-EVT Approach: Forecasting Volatility in Tunisian Financial Market.Journal of Computations & Modelling, 2(2), (2012), 95-115.
  7. Chan, K. F., & Gray, P., Using extreme value theory to measure value-at-risk for daily electricity spot prices. International Journal of Forecasting,22(2), (2006), 283-300.
  8. Karmakar, M., Estimation of tail-related risk measures in the Indian stock market: An extreme value approach. Review of Financial Economics,22(3), (2013), 79-85.
  9. Altun, E., & Tatlidil, H., A Comparison of Extreme Value Theory with Heavy-tailed Distributions in Modeling Daily VAR. Journal of Finance and Investment Analysis, 4(2), (2015),69-83
  10. Venkataraman, S., Value at risk for a mixture of normal distributions: the use of quasi-Bayesian estimation techniques. Economic Perspectives-Federal Reserve Bank of Chicago, 21, (1997), 2-13.
  11. Zangari, P., An improved methodology for measuring VaR. RiskMetrics Monitor, 2(1), (1996), 7-25.
  12. Lee, M. C., Su, J. B., & Liu, H. C., Value-at-risk in US stock indices with skewed generalized error distribution. Applied Financial Economics Letters, 4(6), (2008), 425-431.
  13. Angelidis, T., Benos, A., & Degiannakis, S., The use of GARCH models in VaR estimation. Statistical methodology, 1(1), (2004), 105-128.
  14. Daubechies, I., Ten lectures on wavelets, Philadelphia: Society for industrial and applied mathematics, 61, (1992), 198-202.
  15. Chui, C. K. (1992).
  16. An Introduction to Wavelets, Philadelphia, SIAM, 38, (1992).
  17. Graps, A., An introduction to wavelets. Computational Science & Engineering, IEEE, 2(2), (1995), 50-61.
  18. Chi, X., & Kai-jian, H., Wavelet denoised value at risk estimate. In Management Science and En