Calculates the pvalue of the ARCH effect test (i.e. the whitenoise test for the squared time series).
Syntax
ARCHTest(X, Order, M, Return_type, Alpha)
 X
 is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)).
 Order
 is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).
Order Description 1 ascending (the first data point corresponds to the earliest date) (default) 0 descending (the first data point corresponds to the latest date)  M
 is the maximum number of lags included in the ARCH effect test. If omitted, the default value of log(T) is assumed.
 Return_type
 is a switch to select the return output (1 = PValue (default), 2 = Test Stats, 3 = Critical Value.
Method Description 1 PValue 2 Test Statistics (e.g. Zscore) 3 Critical Value  Alpha
 is the statistical significance of the test (i.e. alpha). If missing or omitted, an alpha value of 5% is assumed.
Remarks
 The time series is homogeneous or equally spaced.
 The time series may include missing values (e.g. #N/A) at either end.
 The ARCH effect applies the whitenoise test on the time series squared:
$y_{t}=x_t^2$  The test hypothesis for the ARCH effect:
$H_{o}: \rho_{1}=\rho_{2}=...=\rho_{m}=0$
$H_{1}: \exists \rho_{k}\neq 0$
$1\leq k \leq m$
Where:
 $H_{o}$ is the null hypothesis.
 $H_{1}$ is the alternate hypothesis.
 $\rho$ is the population autocorrelation function for the squared time series (i.e. $y_t=x_t^2$).
 $m$ is the maximum number of lags included in the ARCH effect test.
 The LjungBox modified $Q^*$ statistic is computed as:
$Q^*(m)=T(T+2)\sum_{j=1}^{m}\frac{\hat\rho_{j}^2}{Tl}$
Where:
 $m$ is the maximum number of lags included in the ARCH effect test.
 $\hat{\rho_j}$ is the sample autocorrelation at lag j for the squared time series.
 $T$ is the number of nonmissing values in the data sample.
 $Q^*(m)$ has an asymptotic chisquare distribution with $m$ degrees of freedom and can be used to test the null hypothesis that the time series has an ARCH effect.
$Q^*(m) \sim \chi_{\nu=m}^2()$
Where:
 $\chi_{\nu}^2()$ is the Chisquare probability distribution function.
 $\nu$ is the degrees of freedom for the Chisquare distribution.
 This is oneside (i.e. onetail) test, so the computed pvalue should be compared with the whole significance level ($\alpha$).
Examples
Example 1:


Formula  Description (Result) 

=ARCHTest($B$2:$B$30,1)  pvalue of ARCH effect test (0.5663) 
Files Examples
References
 Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0691042896
 Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0471690740
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