The Autoregressive Stochastic Volatility (ARSV) model is a discrete-time stochastic volatility model that can model the financial returns time series and volatilities. This model is relevant for risk management. However, existing inference methods have various limitations on model assumptions. In this report we discuss a new inference method that allows flexible model assumptions for innovations of the ARSV model. We also present the application of ARSV model to risk management, and compare the ARSV model with another commonly used model for financial time series, namely the GARCH model.