Hurst Exponent

Intro

  • Name derives from Harold Edwin Hurst (1880–1978), who was the lead researcher in hydrology studies; the use of the standard notation H for the coefficient also relates to his name.
  • Used as a measure of long-term memory of time series.
  • Quantifies the tendency of a time series to revert to its mean or cluster in one direction.
  • The Hurst exponent shows if a time series behaves in a random, trending, or mean-reverting way. It captures the speed autocorrelation decrease as the lag increases.
  • Mean reversion assumes that the properties such as stock returns and volatility will revert to their long-term average over time. Mathematically, such a time series is described by an Ornstein–Uhlenbeck process.

Definition

The Hurst exponent, H, is defined in terms of the asymptotic behaviour of the rescaled range as a function of the time span of a time series as follows:

$$ \begin{align*} {\displaystyle \mathbb {E} \left[{\frac {R(n)}{S(n)}}\right]=Cn^{H}{\text{ as }}n\to \infty \,,} \end{align*} $$

where:
  • $R(n)$ is the range of the first $n$ cumulative deviations from the mean
  • $S(n)$ is the series (sum) of the first n standard deviations
  • ${\displaystyle \mathbb {E} \left[x\right]}$ is the expected value
  • $n$ is the time span of the observation (number of data points in a time series)
  • $C$ is a constant.

Trading

  • Traders use it to pick the right trading strategy for the current market conditions. For example, you do not want to trade a trend strategy if the Hurst exponent shows mean reverting market behavior.

Use the Hurst exponent for strategy selection

  • Hurst exponent ranges between 0 and 1.
  • If $H < 0.5$, the market is mean reverting. Reversal strategies win in these markets. The closer the value is 0, the stronger the mean reversion.
  • If $H = 0.5$ means the market is random. In this case, a trading strategy that relies on the market direction will lose money.
  • If $ H > 0.5$ the market is trending. Markets with a high Hurst exponent are perfect for trend-following strategies. The closer the value is to 1, the stronger the trend.

So, it’s better to first check the Hurst exponent before you start trading using a specific strategy.

Implementation

List of things we want to do in this example:

  1. Get stock price data using OpenBB SDK
  2. Calculate the Hurst exponent
  3. Determine the type of market: if the market is mean reverting, trending, or random?
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Permalink at https://www.physicslog.com/cs-notes/qf/hurst-exponent

Published on Jan 8, 2023

Last revised on Jan 15, 2023

References