KaydedilenlerDerginizi ekleyin

Dergiler / Central Bank Review / 2009 / Cilt: 9 - Sayı: 1

Estimating value-at-risk for the Turkish stock index futures in the presence of long memory volatility

ADNAN KASMAN
Sayfa
1–14
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)

The empirical results further indicate that based on the Kupiec LR failure rate test the FIGARCH(1,d,1) models with skewed Student-t distribution perform better than those of generated by normal distribution.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:This paper examines the long memory properties for closing prices of the Turkish stock index futures market using the FIGARCH(1,d,1) model with three different distributions: Normal, Student-t, and skewed Student-t.
📊
Temel Bulgular & Veri:The value-at-risk (VaR) values are calculated using the estimated models.
💡
Sonuç & Katkı:The empirical results further indicate that based on the Kupiec LR failure rate test the FIGARCH(1,d,1) models with skewed Student-t distribution perform better than those of generated by normal distribution.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:risk · risk

Abstract

This paper examines the long memory properties for closing prices of the Turkish stock index futures market using the FIGARCH(1,d,1) model with three different distributions: Normal, Student-t, and skewed Student-t. The value-at-risk (VaR) values are calculated using the estimated models. The results indicate strong evidence of long memory in volatility. The evidence of long memory in volatility shows that uncertainty or risk is an important determinant of the behavior of daily futures prices in the Turkish futures market. The empirical results further indicate that based on the Kupiec LR failure rate test the FIGARCH(1,d,1) models with skewed Student-t distribution perform better than those of generated by normal distribution.

Anahtar kelimeler
🔖

Bu Makaleye Atıf Yap

KASMAN, A. (2009). Estimating value-at-risk for the Turkish stock index futures in the presence of long memory volatility. Central Bank Review, Cilt: 9 - Sayı: 1, 1–14. https://ajindex.com/articles/151031

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (15)

  1. Baillie, R.T., Bollerslev, T., and Mikkelsen, H.O., (1996) Fractionally integrated generalized autoregressive conditional heteroscedasticity. Journal of Econometrics 74, 3-30.
  2. Baillie, R.T., Cecen, A.A., and Han, Y. W. (2000) High frequency Deutsche Mark-US dollar returns: FIGARCH representations and nonlinearities, Multinational Finance Journal, 4, 247-267.
  3. Beder, T. (1995) VaR, seductive but dangerous, Financial Analysts Journal, 12–24.
  4. Bollerslev, T.(1986) Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, 31, 307-327.
  5. Ding, Z.C., Granger, W.J., and Engle, R.F. (1993) A long memory property of stock market returns and a new model, Journal of Empirical Finance, 1, 83-106.
  6. Engle R.F. (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 4, 987-1007.
  7. Engle R.F. and Bollerslev, T. (1986) Modelling the persistence of conditional variances, Econometric Reviews, 5, 1-50.
  8. Hendricks, D., (1996) Evaluation of value-at-risk models using historical data, Federal Reserve Bank of New York, Economic Policy Review, 39-69.
  9. Huang, Y.C., and Lin, B.J., (2004) Value-at-risk analysis for Taiwan stock index futures: Fat tails and conditional asymmetries in return innovations, Review of Quantitative Finance and Accounting, 22, 79-95.
  10. Jorion, P. (1996) Risk and turnover in the foreign exchange market, in J.A. Frankel, G.Galli, and A. Giovanni (Eds.), The Microstructure of Foreign Exchange Markets, Chicago: The University of Chicago Press.
  11. Jorion, P. (2000)
  12. Value-at-Risk, McGraw-Hill. Kupiec, P., (1995) Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives, 2, 174-184.
  13. Marshall, C. and Siegel, M. (1997) Value at risk, implementing a risk measurement standard, Journal of Derivatives, 4, 91–111.
  14. Tang, T.L and Shieh, S.J. (2006) Long memory in stock index futures markets: A value-at-risk approach, Physica A, 366, 437-448.
  15. Wu, P.T and Shieh, S.J (2007) Value-at-Risk analysis for long-term interest rate futures: Fat-tail and long memory in return innovations, Journal of Empirical Finance, 14, 248-259.