def cokurt(df):
    # Number of stocks
    num = len(df.columns)

    #First Tensor Product Matrix
    mtx1 = np.zeros(shape = (len(df), num**2))

    #Second Tensor Product Matrix
    mtx2 = np.zeros(shape = (len(df), num**3))

    v = df.values
    means = v.mean(0,keepdims=True)
    v1 = (v-means).T

    for k in range(num):
        for i in range(num):
            for j in range(num):
                    vals = v1[i]*v1[j]*v1[k]
                    mtx2[:,(k*(num**2))+(i*num)+j] = vals/float((len(df)-1)*df.iloc[:,i].std()*\
                                                                df.iloc[:,j].std()*df.iloc[:,k].std())

    m4 = np.dot(v1,mtx2)
    for i in range(num**3):
        use = i%num
        m4[:,i] = m4[:,i]/float(df.iloc[:,use].std())

    return m4

m4 = cokurt(log_ret)