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I implement a GARCH-DCC model in Python, for number of asset = 2. My implementation is the following :

def garch_dcc_specification(
    self,
    eps_last: Optional[np.ndarray],
    cond_var_last: Optional[np.ndarray],
    q_last_t: Optional[np.ndarray],
) -> SpecResult:
    if eps_last is None:
        eps_last = np.zeros(self.n)

    if q_last_t is None:
        q_last_t = np.zeros((self.n, self.n))

    epsilon_square_last = np.array([eps_last_i ** 2 for eps_last_i in eps_last])
    # first, evaluate the garch cond. variance. 
    # (garch_alpha, beta and omega are 1D arrays)
    cond_var_t = (self.garch_omega
                  + self.garch_alpha * epsilon_square_last
                  + self.garch_beta * (cond_var_last if cond_var_last is not None else np.zeros(self.n)))

    d_t = np.diag([math.sqrt(v) for v in cond_var_t])

    if cond_var_last is not None:
        d_last_t = np.diag([math.sqrt(v) for v in cond_var_last])
        v_last_t = inv(d_last_t).dot(eps_last)
    else:
        v_last_t = np.zeros(self.n)

    # DCC specification for the conditional correlation
    # note: DO NOT DO v[t - 1].dot(v[t - 1].transpose()) : since v[t - 1] is a 1D array, result would be a number
    q_t = (self.dcc_r * (1 - self.dcc_alpha - self.dcc_beta)
                       + self.dcc_alpha * v_last_t.reshape(1, -1).transpose().dot(v_last_t.reshape(1, -1))
                       + self.dcc_beta * q_last_t)

    # standardize q to get a real correlation matrix 
    r_t = np.zeros((self.n, self.n))
    for i in range(self.n):
        for j in range(self.n):
            r_t[i][j] = q_t[i][j] / math.sqrt(q_t[i][i] * q_t[j][j])

    # transforms to a variance-covariance matrix by incorporing the cond variances 
    h_t = d_t.dot(r_t).dot(d_t)
    return GarchDccParams.SpecResult(cond_var = cond_var_t, q = q_t, h = h_t)

def generate_innovations(self, length: int) -> np.ndarray:
    innovations = np.zeros((length + 1, self.n))
    spec_res: List[GarchDccParams.SpecResult] = []
    for t in range(0, length + 1):
        spec_res.append(self.garch_dcc_specification(
            eps_last = innovations[t - 1] if t != 0 else None,
            cond_var_last = spec_res[t - 1].cond_var if t != 0 else None,
            q_last_t = spec_res[t - 1].q if t != 0 else None,
        ))
        innovations[t] = np.random.multivariate_normal(np.zeros(self.n), spec_res[t].h)

To check my implentation, I control that the generate_innovation() empirical pearson correlation coefficient (with np.corrcoef) is equal to the input self.dcc_r matrix correlation coefficient, which should be the unconditional correlation of the overrall generated innovation, if I understand correctly.

When running with constant variance in the GARCH (garch alpha and beta = 0), I get a good pearson coef coefficient that is equal to the one I set in the input self.dcc_r Though, when the conditional variance is moving (garch alpha and beta > 0), I don't get the same coefficient, I get always a lesser empirical correlation coefficient then expected in the DCC_R. For example, when running with 10000 points, and with an input dcc_r correlation coef. of 0.9, I get an empirical unconditional correlation, in my generated innovations, of around 0.75

PS: To simplificate, I set dcc_alpha and dcc_beta to 0 so only the dcc_r matrix is taken into account (we have a GARCH-CCC model instead of a GARCH-DCC, cond. correlation is always the same). The "problem" (if it is one) still occurs, still when GARCH alpha/beta > 0.

Is it normal ?

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  • $\begingroup$ What exactly are you calculating the correlation between (the one that is not what you think it should be)? $\endgroup$ Mar 27, 2023 at 16:35
  • $\begingroup$ I'm computing the empirical unconditional correlation of the generated innovations (using np.corrcoef I get the correlation matrix, and I take the value that is not on the diagonal, there is only one value because asset number = 2, I will update my queston). I'm expecting this correlation coefficient to be equal to the one I gave as input in the CCC matrix. When GARCH alpha, beta > 0 (variance changing over time), these 2 number are not equal, I don't understand why (I don't know if it is normal or if this is the sign of an error in my implementation) $\endgroup$ Mar 27, 2023 at 19:01
  • $\begingroup$ What are the generated innovations? Are these obtained from a fitted model? If so, let me call them residuals rather than innovations. (Innovations are the theoretical ones or the ones used for simulating a DCC process.) Are they the standardized residuals (for which your doubts would be justified) or raw residuals (that display GARCH patterns)? The raw residuals might not have quite the same correlation as the standardized residuals, because the assumption about correlation is made about the standardized innovations of the process. $\endgroup$ Mar 27, 2023 at 19:46
  • $\begingroup$ Actually the code I provided is just used to generate garch-dcc random innovations, provided some GARCH-DCC parameters (to be used to generate a random realization of more general model, for example a ECM with GARCH-DCC innovations). The more general model call conditional_variance_process.generate_innovations() (conditional_variance_process can be an instance of GarchDcc or whatever conditional variance process, but it will always have a generate_innovation fn). $\endgroup$ Mar 27, 2023 at 20:17
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    $\begingroup$ Note the word standardized in standardized residuals. Meanwhile, you seem to be worried about the correlation estimate from raw/unstandardized residuals not being equal to that of the standardized one. $\endgroup$ Mar 28, 2023 at 13:48

1 Answer 1

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Note the word standardized in standardized residuals. Meanwhile, you seem to be worried about the correlation estimate from raw/unstandardized residuals not being equal to the theoretical correlation of the standardized ones.

Suppose we have standardized innovations $(z_{1,t},z_{2,t})^\top$ that have a certain unconditional correlation $\rho=\text{Corr}(z_{1,t},z_{2,t})$. When multiplied by the time-varying standard deviation, they become raw innovations $(\varepsilon_{1,t},\varepsilon_{2,t})^\top=(\sigma_{1,t}z_{1,t},\sigma_{2,t}z_{2,t})^\top$. The unconditional correlation between them, $\xi=\text{Corr}(\varepsilon_{1,t},\varepsilon_{2,t})$ need not be equal to $\rho$. While $\text{Corr}(aX,bY)=\text{Corr}(X,Y)$ for constants $(a,b)^\top$, this does not apply for random variables $(U,V)^\top$: $\text{Corr}(UX,VY)\not\equiv \text{Corr}(X,Y)$.

The remaining problem is why you still get a noticeable discrepancy between the expected and estimated unconditional correlation, $(\rho,\tilde\rho)^\top=(0.950,0.945)^\top$ with a huge sample of 100k points.

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