Abstract

The value-at-risk is a useful metric employed by financial institutions to measure the risk of a portfolio. However, accurately forecasting the value-at-risk is difficult, as it requires predicting the returns of the portfolio's assets. Forecasting asset returns is particularly challenging due to their stochastic nature and the presence of 'stylized facts' such as heteroskedasticity, fat tails, and skewness in stock returns series. This thesis considers modeling the assets returns using a recurrent mixture density network, which has been previously proposed to model other financial time-series. In this thesis, we propose an improved recurrent mixture density network architecture, as well as a pretraining method to improve the numerical stability and convergence speed of the model. We also propose the Copula-S-RMDN-GARCH, which extends the current recurrent mixture density network architecture to multivariate settings. We compare the value-at-risk forecast obtained with the Copula-S-RMDN-GARCH with the forecasts obtained from a Copula-AR-GARCH.