NDK_EWXCF

Computes the correlation factor using the exponential-weighted correlation function.

NDK_EWXCF computes the correlation estimate using the exponential-weighted covariance (EWCOV) and volatility (EWMA/EWV) method for each 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 (or Y).
[in]	lambda	is the smoothing parameter used for the exponential-weighting scheme. If missing, a default value of 0.94 is assumed.
[in]	step	is the forecast time/horizon (expressed in terms of steps beyond the end of the time series X). If missing, a default value of 0 is assumed.
[out]	retVal	is the estimated value of the correlation factor.

Remarks

1. The time series are homogeneous or equally spaced.

2. The two time series must have identical size and time order.

3. The cross correlation function is defined as:

• \(\rho^{(xy)}_t=\frac{\sigma_t^{(xy)}}{{_x\sigma_t}\times{_y\sigma_t}}\)
• \(\sigma_t^{(xy)} = \lambda\sigma_{t-1}^{(xy)}+(1-\lambda)x_{t-1}y_{t-1}\)
• \(_x\sigma_t^2=\lambda\times{_x\sigma_{t-1}^2}+(1-\lambda)x_{t-1}^2\)
• \(_y\sigma_t^2=\lambda\times{_y\sigma_{t-1}^2}+(1-\lambda)y_{t-1}^2\),
  where:
  • \(\rho^{(xy)}_t\) is the sample correlation between X and Y at time t.
  • \(\sigma_t^{(xy)}\) is the sample exponential-weighted covariance between X and Y at time t.
  • \(_x\sigma_t\) is the sample exponential-weighted volatility for the time series X at time t.
  • \(_y\sigma_t\) is the sample exponential-weighted volatility for the time series Y at time t.
  • \(\lambda\) is the smoothing factor used in the exponential-weighted volatility and covariance calculations.

Requirements

Header	SFSDK.H
Library	SFSDK.LIB
DLL	SFSDK.DLL

Examples