ESTIMATION OF HURST EXPONENT FOR THE FINANCIAL TIME SERIES

2009 ◽  
Author(s):  
J. Kumar ◽  
P. Manchanda ◽  
A. H. Siddiqi ◽  
M. Brokate ◽  
A. K. Gupta
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Financial Time Series ◽  
Financial Time

Hurst exponent analysis of financial time series

2001 ◽  
Vol 5 (4) ◽  
pp. 269-272 ◽  
Author(s):  
Hong-wei SANG ◽  
Tian Ma ◽  
Shuo-zhong Wang
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Financial Time Series ◽  
Financial Time

scholarly journals An Extended Speculation Game for the Recovery of Hurst Exponent of Financial Time Series

2020 ◽  
Vol 16 (02) ◽  
pp. 319-325
Author(s):  
Kei Katahira ◽  
Yu Chen
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Financial Time Series ◽  
Price Change ◽  
Market Price ◽  
Agent Based ◽  
Financial Time ◽  
Real Market ◽  
Initial Price ◽  
Perturbative Part

The speculation game is an agent-based toy model to investigate the dynamics of the financial market. Our model has achieved the reproduction of 10 of the well-known stylized facts for financial time series. However, there is also a divergence from the behavior of real market. The market price of the model tends to be anti-persistent to the initial price, resulting in the quite small value of Hurst exponent of price change. To overcome this problem, we extend the speculation game by introducing a perturbative part to the price change with the consideration of other effects besides pure speculative behaviors.

scholarly journals HURST EXPONENTS AND DELAMPERTIZED FRACTIONAL BROWNIAN MOTIONS

2019 ◽  
Vol 22 (05) ◽  
pp. 1950024
Author(s):  
MATTHIEU GARCIN
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Hurst Exponent ◽  
Stationary Process ◽  
Financial Time Series ◽  
Mean Reversion ◽  
Foreign Exchange Rates ◽  
Medium Term ◽  
Fractional Brownian Motions ◽  
Financial Time

The inverse Lamperti transform of a fractional Brownian motion (fBm) is a stationary process. We determine the empirical Hurst exponent of such a composite process with the help of a regression of the log absolute moments of its increments, at various scales, on the corresponding log scales. This perceived Hurst exponent underestimates the Hurst exponent of the underlying fBm. We thus encounter some time series having a perceived Hurst exponent lower than [Formula: see text], but an underlying Hurst exponent higher than [Formula: see text]. This paves the way for short- and medium-term forecasting. Indeed, in such series, mean reversion predominates at high scales, whereas persistence is overriding at lower scales. We propose a way to characterize the Hurst horizon, namely a limit scale between these opposite behaviors. We show that the delampertized fBm, which mixes persistence and mean reversion, is relevant for financial time series, in particular for high-frequency foreign exchange rates. In our sample, the empirical Hurst horizon is always above 1[Formula: see text]h and 23[Formula: see text]min.

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