Forecasting financial risk and risk measurement methods have been of increasing interest for financial market regulators and financial institutions in the past two decades. While the parametric and semi-parametric models have been widely reviewed in the academic literature, the non-parametric...

The aim of this thesis is to improve risk measurement estimation by incorporating extra information in the form of constraint into completely non-parametric smoothing techniques. A similar approach has been applied in empirical likelihood analysis. The method of constraints incorporates...

Measuring risk is a crucial aspect of the portfolio optimization problem in finance, and of capital adequacy assessment in risk management. Expected Shortfall (ES) has been proposed as a coherent risk measure, by contrast with Value-at-Risk (VaR) and the standard-deviation-type of measures....

We analyze the impact of the estimation frequency–updating parameter estimates on a daily, weekly, monthly or quarterly basis–for commonly used GARCH models in a large-scale study, using more than twelve years (2000–2012) of daily returns for constituents of the S&P 500 index. We assess...

A Realised Volatility GARCH model is developed within a Bayesian framework for the purpose of forecasting Value at Risk and Conditional Value at Risk. Student-t and Skewed Student-t return distributions are combined with Gaussian and Student-t distributions in the measurement equation in a GARCH...

In this article, we use the dual long memory properties to assess the value-at-risk and expected shortfall for the Argentinean stock market under both short and long daily trading positions. We attempt to show whether considering for long memory properties in both the returns and volatility,...