| int __stdcall NDK_XCF | ( | double * | X, |
| double * | Y, | ||
| size_t | N, | ||
| size_t | K, | ||
| WORD | method, | ||
| WORD | retType, | ||
| double * | retVal | ||
| ) |
Calculates the cross-correlation function between two time series.
- Returns
- status code of the operation
- Return values
-
NDK_SUCCESS Operation successful NDK_FAILED Operation unsuccessful. See Macros for full list.
- Parameters
-
[in] X is the first univariate time series data (a one dimensional array). [in] Y is the second univariate time series data (a one dimensional array). [in] N is the number of observations in X. [in] K is the lag order (e.g. 0=no lag, 1=1st lag, etc.) to use with the second time series input (X). If missing, a default lag order of zero (i.e. no-lag) is assumed. [in] method is the algorithm/method to use for calculating the correlation (see notes below) [in] retType is a switch to select the return output (1 = correlation value(default), 2 = std error). [out] retVal is the calculated value of this function.
- Remarks
- 1. The time series is homogeneous or equally spaced.
- 2. The two time series must be identical in size.
- 3. The NDK_XCF functions supports the following methods:
Method Value Description XCF_PEARSON 1 Pearson XCF_SPEARMAN 2 Spearman XCF_KENDALL 3 Kendall - 3. The Pearson correlation, \(r_{xy}\), is defined as follows:
- \[r_{xy}= \frac{\sum_{i=1}^N(x_i-\bar{x})(y_i-\bar{y})}{\sqrt{\sum_{i=1}^N(x_i-\bar{x})^2\times\sum_{i=1}^N(y_i-\bar{y})^2}}\],
- where:
- \(\bar{x}\) is the sample average of time series X.
- \(\bar{y}\) is the sample average of time series Y.
- \(x_i \in X\) is a value from the first input time series data.
- \(y_i \in Y\) is a value from the second input time series data.
- \(N\) is the number of pairs \(\left ( x_i,y_i \right )\) that do not contain a missing observation.
- Requirements
-
Header SFSDK.H Library SFSDK.LIB DLL SFSDK.DLL
- Examples
-
| Namespace: | NumXLAPI |
| Class: | SFSDK |
| Scope: | Public |
| Lifetime: | Static |
| int NDK_XCF | ( | double[] | pData1, |
| double[] | pData2, | ||
| UIntPtr | nSize, | ||
| UIntPtr | nLag, | ||
| short | nMethod, | ||
| short | retType, | ||
| ref double | retVal | ||
| ) |
Calculates the cross-correlation function between two time series.
- Return Value
-
a value from NDK_RETCODE enumeration for the status of the call.
NDK_SUCCESS operation successful Error Error Code
- Parameters
-
[in] pData1 is the first univariate time series data (a one dimensional array). [in] pData2 is the second univariate time series data (a one dimensional array). [in] nSize is the number of observations in pData1. [in] nLag is the lag order (e.g. 0=no lag, 1=1st lag, etc.) to use with the second time series input (X). If missing, a default lag order of zero (i.e. no-lag) is assumed. [in] nMethod is the algorithm/method to use for calculating the correlation (see notes below) [in] retType is a switch to select the return output (1 = correlation value(default), 2 = std error). [out] retVal is the calculated value of this function.
- Remarks
- 1. The time series is homogeneous or equally spaced.
- 2. The two time series must be identical in size.
- 3. The NDK_XCF functions supports the following methods:
Method Value Description XCF_PEARSON 1 Pearson XCF_SPEARMAN 2 Spearman XCF_KENDALL 3 Kendall - 3. The Pearson correlation, \(r_{xy}\), is defined as follows:
- \[r_{xy}= \frac{\sum_{i=1}^N(x_i-\bar{x})(y_i-\bar{y})}{\sqrt{\sum_{i=1}^N(x_i-\bar{x})^2\times\sum_{i=1}^N(y_i-\bar{y})^2}}\],
- where:
- \(\bar{x}\) is the sample average of time series X.
- \(\bar{y}\) is the sample average of time series Y.
- \(x_i \in X\) is a value from the first input time series data.
- \(y_i \in Y\) is a value from the second input time series data.
- \(N\) is the number of pairs \(\left ( x_i,y_i \right )\) that do not contain a missing observation.
- Examples
-
- References
- Hull, John C.; Options, Futures and Other DerivativesFinancial Times/ Prentice Hall (2011), ISBN 978-0132777421
