Predictability and herding of bourse volatility: an econophysics analogue

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Financial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation.

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    • Figure 1. Depicting cumulative Log-periodic Re HFT as a function of its observations
    • Table 1. Report of Hurst exponent in ReHFT domain (average Hurst exponent is 0.69812)
    • Table 2. Report of Hurst exponent in Re regular domain (average Hurst exponent is 0.586762)
    • Table 3. Zones of Hurst exponent
    • Table 4. Hurst exponent: interpretation
    • Table 5. Certain periods (Re Regular NIFTY) with high Hurst exponent (H > 0.65) has clear event link as well
    • Table 6. Certain periods (ReHFT NIFTY) with high Hurst exponent (H > 0.65) has clear event link as well