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Multifractal Value at Risk model

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  • Lee, Hojin
  • Song, Jae Wook
  • Chang, Woojin

Abstract

In this paper new Value at Risk (VaR) model is proposed and investigated. We consider the multifractal property of financial time series and develop a multifractal Value at Risk (MFVaR). MFVaR introduced in this paper is analytically tractable and not based on simulation. Empirical study showed that MFVaR can provide the more stable and accurate forecasting performance in volatile financial markets where large loss can be incurred. This implies that our multifractal VaR works well for the risk measurement of extreme credit events.

Suggested Citation

  • Lee, Hojin & Song, Jae Wook & Chang, Woojin, 2016. "Multifractal Value at Risk model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 113-122.
    • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:113-122
    DOI: 10.1016/j.physa.2015.12.161
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    Cited by:

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    3. Xin-Lan Fu & Xing-Lu Gao & Zheng Shan & Zhi-Qiang Jiang & Wei-Xing Zhou, 2018. "Multifractal characteristics and return predictability in the Chinese stock markets," Papers 1806.07604, arXiv.org.
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    5. Lee, Minhyuk & Song, Jae Wook & Park, Ji Hwan & Chang, Woojin, 2017. "Asymmetric multi-fractality in the U.S. stock indices using index-based model of A-MFDFA," Chaos, Solitons & Fractals, Elsevier, vol. 97(C), pages 28-38.
    6. Massimiliano Frezza & Sergio Bianchi & Augusto Pianese, 2022. "Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process," Computational Management Science, Springer, vol. 19(1), pages 99-132, January.
    7. Ko, Bonggyun & Song, Jae Wook, 2018. "A simple analytics framework for evaluating mean escape time in different term structures with stochastic volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 398-412.

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