This paper proposes a stochastic volatility model driven by a first-order autoregressive process with an asymmetric Laplace marginal distribution. The autoregressive structure with asymmetric Laplace marginal is incorporated into the variance equation to better capture asymmetry and heavy tails in financial return series. The model parameters are estimated using the generalized method of moments. A simulation study is conducted to evaluate the performance of the estimators. Finally, a real-data application is presented to illustrate the practical utility of the proposed model and to demonstrate that it captures the stylized features of financial return series.
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