# NDK_HURST_EXPONENT

 int __stdcall NDK_HURST_EXPONENT ( double * X, size_t N, double alpha, WORD retType, double * retVal )

Calculates the Hurst exponent (a measure of persistence or long memory) for time series.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See SFMacros.h for more details.
Parameters
 [in] X is the input data sample (a one dimensional array). [in] N is the number of observations in X. [in] alpha is the statistical significance level (1%, 5%, 10%). If missing, a default of 5% is assumed. [in] retType is a number that determines the type of return value: 1 = Empirical Hurst exponent (R/S method) 2 = Anis-Lloyd/Peters corrected Hurst exponent 3 = Theoretical Hurst exponent 4 = Upper limit of the confidence interval 5 = Lower limit of the confidence interval [out] retVal is the calculated value of this function.
Note
NDK_FAILED.
2. The input data series may include missing values (NaN), but they will not be included in the calculations.
3. The Hurst exponent, $$h$$, is defined in terms of the rescaled range as follows:
$E \left [ \frac{R(n)}{S(n)} \right ]=C_n^H$ $n \to \infty$
4. Where:
• $$\left [ \frac{R(n)}{S(n)} \right ]$$ is the Rescaled Range.
• $$E \left [x \right ]$$ is the expected value.
• $$n$$ is the time of the last observation (e.g. it corresponds to $$X_n$$ in the input time series data.)
• $$h$$ is a constant. of
5. The Hurst exponent is a measure autocorrelation (persistence and long memory): -A value of $$0<H<0.5$$ indicates a time series with negative autocorrelation (e.g. a decrease between values will probably be followed by another decrease). -A value of $$0.5<H<1$$ indicates a time series with positive autocorrelation (e.g. an increase between values will probably be followed by another increase). -A value of $$H=0.5$$ indicates a "true random walk," where it is equally likely that a decrease or an increase will follow from any particular value (e.g. the time series has no memory of previous values).
6. The Hurst exponent's namesake, Harold Edwin Hurst (1880-1978), was a British hydrologist who researched reservoir capacity along the Nile river.
7. The rescaled range is calculated for a time series, $$X=X_1,X_2,\dots, X_n$$, as follows:
1. Calculate the mean:
$$m=\dfrac{1}{n} \sum_{i=1}^{n} X_i$$
2. Create a mean adjusted series:
$$Y_t=X_{t}-m$$ for $$t=1,2, \dots ,n$$
3. Calculate the cumulative deviate series Z:
$$Z_t= \sum_{i=1}^{t} Y_{i}$$ for $$t=1,2, \dots ,n$$
4. Create a range series R:
$$R_t = max\left (Z_1, Z_2, \dots, Z_t \right )- min\left (Z_1, Z_2, \dots, Z_t \right )$$ for $$t=1,2, \dots, n$$
5. Create a standard deviation series R:
$$S_{t}= \sqrt{\dfrac{1}{t} \sum_{i=1}^{t}\left ( X_{i} - u \right )^{2}}$$ for $$t=1,2, \dots ,n$$  Where:
$$h$$ is the mean for the time series values $$X_1,X_2, \dots, X_t$$
8. Calculate the rescaled range series (R/S):
$$\left ( R/S \right )_{t} = \frac{R_{t}}{S_{t}}$$ for $$t=1,2, \dots, n$$
Requirements
Examples



 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int NDK_HURST_EXPONENT ( double[] pData, UIntPtr nSize, double alpha, short retType, ref double retVal )

Calculates the Hurst exponent (a measure of persistence or long memory) for time series.

Returns
status code of the operation
Return values
 NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See SFMacros.h for more details.
Parameters
 [in] pData is the input data sample (a one dimensional array). [in] nSize is the number of observations in pData. [in] alpha is the statistical significance level (1%, 5%, 10%). If missing, a default of 5% is assumed. [in] retType is a number that determines the type of return value: 1 = Empirical Hurst exponent (R/S method) 2 = Anis-Lloyd/Peters corrected Hurst exponent 3 = Theoretical Hurst exponent 4 = Upper limit of the confidence interval 5 = Lower limit of the confidence interval [out] retVal is the calculated value of this function.
Remarks
1. The input data series may include missing values (NaN), but they will not be included in the calculations.
2. The Hurst exponent, $$h$$, is defined in terms of the rescaled range as follows:
$E \left [ \frac{R(n)}{S(n)} \right ]=C_n^H$ $n \to \infty$
3. Where:
• $$\left [ \frac{R(n)}{S(n)} \right ]$$ is the Rescaled Range.
• $$E \left [x \right ]$$ is the expected value.
• $$n$$ is the time of the last observation (e.g. it corresponds to $$X_n$$ in the input time series data.)
• $$h$$ is a constant. of
4. The Hurst exponent is a measure autocorrelation (persistence and long memory): -A value of $$0<H<0.5$$ indicates a time series with negative autocorrelation (e.g. a decrease between values will probably be followed by another decrease). -A value of $$0.5<H<1$$ indicates a time series with positive autocorrelation (e.g. an increase between values will probably be followed by another increase). -A value of $$H=0.5$$ indicates a "true random walk," where it is equally likely that a decrease or an increase will follow from any particular value (e.g. the time series has no memory of previous values).
5. The Hurst exponent's namesake, Harold Edwin Hurst (1880-1978), was a British hydrologist who researched reservoir capacity along the Nile river.
6. The rescaled range is calculated for a time series, $$X=X_1,X_2,\dots, X_n$$, as follows:
1. Calculate the mean:
$$m=\dfrac{1}{n} \sum_{i=1}^{n} X_i$$
2. Create a mean adjusted series:
$$Y_t=X_{t}-m$$ for $$t=1,2, \dots ,n$$
3. Calculate the cumulative deviate series Z:
$$Z_t= \sum_{i=1}^{t} Y_{i}$$ for $$t=1,2, \dots ,n$$
4. Create a range series R:
$$R_t = max\left (Z_1, Z_2, \dots, Z_t \right )- min\left (Z_1, Z_2, \dots, Z_t \right )$$ for $$t=1,2, \dots, n$$
5. Create a standard deviation series R:
$$S_{t}= \sqrt{\dfrac{1}{t} \sum_{i=1}^{t}\left ( X_{i} - u \right )^{2}}$$ for $$t=1,2, \dots ,n$$  Where:
$$h$$ is the mean for the time series values $$X_1,X_2, \dots, X_t$$
8. Calculate the rescaled range series (R/S):
$$\left ( R/S \right )_{t} = \frac{R_{t}}{S_{t}}$$ for $$t=1,2, \dots, n$$
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static NumXLAPI.DLL
Examples

References
[1] A.A.Anis, E.H.Lloyd (1976) The expected value of the adjusted rescaled Hurst range of independent normal summands, Biometrica 63, 283-298.
[2] H.E.Hurst (1951) Long-term storage capacity of reservoirs, Transactions of the American Society of Civil Engineers 116, 770-808.
[3] E.E.Peters (1994) Fractal Market Analysis, Wiley.
[4] R.Weron (2002) Estimating long range dependence: finite sample properties and confidence intervals, Physica A 312, 285-299.