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Modeling the time-changing dependence in stock markets

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  • Frezza, Massimiliano

Abstract

The time-changing dependence in stock markets is investigated by assuming the multifractional process with random exponent (MPRE) as model for actual log price dynamics. By modeling its functional parameter S(t,ω) via the square root process (S.R.) a twofold aim is obtained. From one hand both the main financial and statistical properties shown by the estimated S(t) are captured by surrogates, on the other hand this capability reveals able to model the time-changing dependence shown by stocks or indexes. In particular, a new dynamical approach to interpreter market mechanisms is given. Empirical evidences are offered by analysing the behaviour of the daily closing prices of a very known index, the Industrial Average Dow Jones (DJIA), beginning on March,1990 and ending on February, 2005.

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  • Frezza, Massimiliano, 2012. "Modeling the time-changing dependence in stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 45(12), pages 1510-1520.
    • Handle: RePEc:eee:chsofr:v:45:y:2012:i:12:p:1510-1520
    DOI: 10.1016/j.chaos.2012.08.009
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    1. Sergio Bianchi & Massimiliano Frezza, 2018. "Liquidity, Efficiency and the 2007-2008 Global Financial Crisis," Annals of Economics and Finance, Society for AEF, vol. 19(2), pages 375-404, November.
    2. Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "COVID-19 and stock returns: Evidence from the Markov switching dependence approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Ayoub Ammy-Driss & Matthieu Garcin, 2021. "Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics," Working Papers hal-02903655, HAL.
    4. Ayoub Ammy-Driss & Matthieu Garcin, 2020. "Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics," Papers 2007.10727, arXiv.org, revised Nov 2021.
    5. Sensoy, A., 2013. "Effects of monetary policy on the long memory in interest rates: Evidence from an emerging market," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 85-88.
    6. Bianchi, Sergio & Pianese, Augusto, 2018. "Time-varying Hurst–Hölder exponents and the dynamics of (in)efficiency in stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 64-75.
    7. Matthieu Garcin, 2019. "Fractal analysis of the multifractality of foreign exchange rates [Analyse fractale de la multifractalité des taux de change]," Working Papers hal-02283915, HAL.
    8. Sergio Bianchi & Augusto Pianese & Massimiliano Frezza, 2020. "A distribution‐based method to gauge market liquidity through scale invariance between investment horizons," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(5), pages 809-824, September.
    9. Sensoy, A., 2013. "Time-varying long range dependence in market returns of FEAS members," Chaos, Solitons & Fractals, Elsevier, vol. 53(C), pages 39-45.
    10. Ammy-Driss, Ayoub & Garcin, Matthieu, 2023. "Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    11. 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.

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