UBC Theses and Dissertations
A flexible inference method for an autoregressive stochastic volatility model with an application to risk management Xie, Yijun
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 assumption for innovation 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.
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