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Long memory properties in return and volatility: Evidence from the Korean stock market

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  • Kang, Sang Hoon
  • Yoon, Seong-Min

Abstract

In this paper, we study the dual long memory property of the Korean stock market. For this purpose, the ARFIMA–FIGARCH model is applied to two daily Korean stock price indices (KOSPI and KOSDAQ). Our empirical results indicate that long memory dynamics in the returns and volatility can be adequately estimated by the joint ARFIMA–FIGARCH model. We also found that the assumption of a skewed Student-t distribution is better for incorporating the tendency of asymmetric leptokurtosis in a return distribution.

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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 385 (2007)
Issue (Month): 2 ()
Pages: 591-600

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Handle: RePEc:eee:phsmap:v:385:y:2007:i:2:p:591-600

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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

Related research

Keywords: Dual long memory; ARFIMA–FIGARCH; Skewed Student-t distribution; Korean stock market;

References

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Citations

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Cited by:
  1. Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2009. "Modelling Long Memory Volatility in Agricultural Commodity Futures Returns," CIRJE F-Series CIRJE-F-680, CIRJE, Faculty of Economics, University of Tokyo.
  2. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Long memory volatility in Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1425-1433.
  3. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Contemporaneous aggregation and long-memory property of returns and volatility in the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4844-4854.
  4. Wei, Yu, 2012. "Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5546-5556.
  5. Xiao, Weilin & Zhang, Weiguo & Zhang, Xili & Chen, Xiaoyan, 2014. "The valuation of equity warrants under the fractional Vasicek process of the short-term interest rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 320-337.
  6. Xiao, Weilin & Zhang, Weiguo & Xu, Weijun & Zhang, Xili, 2012. "The valuation of equity warrants in a fractional Brownian environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1742-1752.
  7. Adnan Kasman & Erdost Torun, 2007. "Long Memory in the Turkish Stock Market Return and Volatility," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 7(2), pages 13-27.
  8. Xiao, Wei-Lin & Zhang, Wei-Guo & Zhang, Xili & Zhang, Xiaoli, 2012. "Pricing model for equity warrants in a mixed fractional Brownian environment and its algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6418-6431.
  9. Sun, Lin, 2013. "Pricing currency options in the mixed fractional Brownian motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3441-3458.
  10. Kang, Sang Hoon & Yoon, Seong-Min, 2008. "Long memory features in the high frequency data of the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5189-5196.
  11. Kumar, Dilip, 2014. "Long range dependence in the high frequency USD/INR exchange rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 134-148.

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