Research Article

The comparison of range-based volatility estimators and an application of TVP-VARbased connectedness

Volume: 9 Number: 3 August 7, 2022
  • Yakup Arı *
EN TR

The comparison of range-based volatility estimators and an application of TVP-VARbased connectedness

Abstract

This paper aims to show the application of range-based volatility in connectedness analysis. For this purpose, we compare the volatility estimators Parkinson, Yang-Zhang, Garman-Klass, Rogers-Satchell, and modified Garman- Klass by Yang and Zhang methods. As an example, we calculated the range-based stock prices’ volatility of four defense industry companies quoted in Borsa Istanbul. We compared the forecast performance of volatility against Heteroskedastic Root Mean Square Error statistics. We include the best performing volatility series in the spillover analysis. Instead of the Cholesky decomposition VAR and generalized VAR approaches used in the calculation of the Diebold-Yılmaz connectedness index, we apply the TVP-VAR-based connectedness approach. The comparison results show that Rogers-Satchell for ASELSAN, KATMERLER, and PAPIL, and Parkinson volatility estimator for OTOKAR have the smallest error, respectively. The empirical findings of TVP-VAR connectedness show that the average forecast error variance of the network is 34.35%.

Keywords

References

  1. ANTONAKAKIS, N., & GABAUER, D. (2017). Refined measures of dynamic connectedness based on TVP-VAR. MPRA Paper No. 78282.
  2. ANTONAKAKIS, N., CHATZIANTONIOU, I., & GABAUER, D. (2020). Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4), 84. MDPI AG. Retrieved from http://dx.doi.org/10.3390/jrfm13040084
  3. ARNERIĆ, J., MATKOVIĆ, M., & SORIĆ, P. (2019). Comparison of range-based volatility estimators against integrated volatility in European emerging markets. Finance Research Letters, 28, 118-124.
  4. BALI, T. G., & WEINBAUM, D. (2005). A comparative study of alternative extreme‐value volatility estimators. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 25(9), 873-892.
  5. BAYRACI, S., & UNAL, G. (2014). Stochastic interest rate volatility modeling with a continuous time
  6. GARCH(1, 1) model. Journal of Computational and Applied Mathematics, 259, 464–473. doi:10.1016/j.cam.2013.10.017
  7. BOLLEN, B. (2014). What should the value of lambda be in the exponentially weighted moving average volatility model? Applied Economics, 47(8), 853–860. doi:10.1080/00036846.2014.98285
  8. CHOU, R. Y. (2005). Forecasting financial volatilities with extreme values: the conditional autoregressive range (CARR) model. Journal of Money, Credit and Banking, Vol. 37, 561-582.

Details

Primary Language

English

Subjects

Business Administration

Journal Section

Research Article

Authors

Publication Date

August 7, 2022

Submission Date

June 11, 2022

Acceptance Date

July 20, 2022

Published in Issue

Year 1970 Volume: 9 Number: 3

APA
Arı, Y. (2022). The comparison of range-based volatility estimators and an application of TVP-VARbased connectedness. Journal of Life Economics, 9(3), 147-157. https://izlik.org/JA85PA94UW