Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model

If an autoregressive moving average model (ARMA model) is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH, Bollerslev(1986)) model.

$$x_t = \mu + a_t$$
$$\sigma_t^2 = \alpha_o + \sum_{i=1}^p {\alpha_i a_{t-i}^2}+\sum_{j=1}^q{\beta_j \sigma_{t-j}^2}$$
$$a_t = \sigma_t \times \epsilon_t$$
$$\epsilon_t \sim P_{\nu}(0,1)$$
Where:

  • $x_t$ is the time series value at time t.
  • $\mu$ is the mean of GARCH model.
  • $a_t$ is the model's residual at time t.
  • $\sigma_t$ is the conditional standard deviation (i.e. volatility) at time t.
  • $p$ is the order of the ARCH component model.
  • $\alpha_o,\alpha_1,\alpha_2,...,\alpha_p$ are the parameters of the the ARCH component model.
  • $q$ is the order of the GARCH component model.
  • $\beta_1,\beta_2,...,\beta_q$ are the parameters of the the GARCH component model.
  • $\left[\epsilon_t\right]$ are the standardized residuals:
    $$\left[\epsilon_t\right] \sim i.i.d$$
    $$E\left[\epsilon_t\right]=0$$
    $$\mathit{VAR}\left[\epsilon_t\right]=1$$
  • $P_{\nu}$ is the probability distribution function for $\epsilon_t$. Currently, the following distributions are supported:
    1. Normal distribution
      $$P_{\nu} = N(0,1) $$.
    2. Student's t-distribution
      $$P_{\nu} = t_{\nu}(0,1) $$
      $$\nu \succ 4 $$
    3. Generalized error distribution (GED)
      $$P_{\nu} = \mathit{GED}_{\nu}(0,1) $$ 
      $$\nu \succ 1 $$

 

Remarks

  • Clustering: a large $a_{t-1}^2$ or $\sigma_{t-1}^2$ gives rise to a large $\sigma_t^2$. This means a large $a_{t-1}^2$ tends to be followed by another large $a_{t}^2$, generating, the well-known behavior, of volatility clustering in financial time series.
  • Fat-tails: The tail distribution of a GARCH(p,q) process is heavier than that of a normal distribution.
  • Mean-reversion: GARCH provides a simple parametric function that can be used to describe the volatility evolution. The model converge to the unconditional variance of $a_t$
  • In fincnail markets, the negative returns have higher influence on volatility levels than positive ones do. GARCH model does not have different treatment based on shock/error term direction.
  • GARCH(p,q) model with normal-distributed innovation has p+q+2 estimated parameters
  • GARCH(p,q) model with GED or student's t-distributed innovation has p+q+3 estimated parameters

References

Have more questions? Submit a request

0 Comments