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Realized Volatility or Price Range: Evidence from a discrete simulation of the continuous time diffusion process

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  • Degiannakis, Stavros
  • Livada, Alexandra

Abstract

The study provides evidence in favour of the price range as a proxy estimator of volatility in financial time series, in the cases that either intra-day datasets are unavailable or they are available at a low sampling frequency. A stochastic differential equation with time varying volatility of the instantaneous log-returns process is simulated, in order to mimic the continuous time diffusion analogue of the discrete time volatility process. The simulations provide evidence that the price range measures are superior to the realized volatility constructed at low sampling frequency. The high-low price range volatility estimator is more accurate than the realized volatility estimator based on five, or less, equidistance points in time. The open-high-low-close price range is more accurate than the realized volatility estimator based on eight, or less, intra-period log-returns.

Suggested Citation

  • Degiannakis, Stavros & Livada, Alexandra, 2013. "Realized Volatility or Price Range: Evidence from a discrete simulation of the continuous time diffusion process," MPRA Paper 80489, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:80489
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    Cited by:

    1. Yuta Kurose, 2021. "Stochastic volatility model with range-based correction and leverage," Papers 2110.00039, arXiv.org, revised Oct 2021.
    2. Baruník, Jozef & Dvořáková, Sylvie, 2015. "An empirical model of fractionally cointegrated daily high and low stock market prices," Economic Modelling, Elsevier, vol. 45(C), pages 193-206.
    3. Piotr Fiszeder & Marta Ma³ecka, 2022. "Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 17(4), pages 939-967, December.
    4. Lyócsa, Štefan & Todorova, Neda & Výrost, Tomáš, 2021. "Predicting risk in energy markets: Low-frequency data still matter," Applied Energy, Elsevier, vol. 282(PA).
    5. Baruník, Jozef & Hlínková, Michaela, 2016. "Revisiting the long memory dynamics of the implied–realized volatility relationship: New evidence from the wavelet regression," Economic Modelling, Elsevier, vol. 54(C), pages 503-514.
    6. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.
    7. Wu, Xinyu & Hou, Xinmeng, 2020. "Forecasting volatility with component conditional autoregressive range model," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    8. Xie, Haibin & Wu, Xinyu, 2017. "A conditional autoregressive range model with gamma distribution for financial volatility modelling," Economic Modelling, Elsevier, vol. 64(C), pages 349-356.
    9. Xu, Liao & Gao, Han & Shi, Yukun & Zhao, Yang, 2020. "The heterogeneous volume-volatility relations in the exchange-traded fund market: Evidence from China," Economic Modelling, Elsevier, vol. 85(C), pages 400-408.
    10. Bazán-Palomino, Walter, 2023. "The increased interest in Bitcoin and the immediate and long-term impact of Bitcoin volatility on global stock markets," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1080-1095.
    11. Yuta Kurose, 2022. "Bayesian GARCH modeling for return and range," Economics Bulletin, AccessEcon, vol. 42(3), pages 1717-1727.
    12. Wu, Xinyu & Xie, Haibin & Zhang, Huanming, 2022. "Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).

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    More about this item

    Keywords

    Integrated Volatility; Intra-day Volatility; Price range; Realized volatility; Stochastic Differential Equation.;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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