This article introduces a new approach for estimating Value at Risk (VaR), which is then used to show the likelihood of the impacts of the current financial crisis. A commonly used two-stage approach is taken, by combining a Generalized Autoregressive Conditional Heteroscedasticity (GARCH)...

Extreme value theory is widely used financial applications such as risk analysis, forecasting and pricing models. One of the major difficulties in the applications to finance and economics is that the assumption of independence of time series observations is generally not satisfied, so that the...

A new extreme value mixture modelling approach for estimating Value-at-Risk (VaR) is proposed, overcoming the key issues of determining the threshold which defines the distribution tail and accounts for uncertainty due to threshold choice. A two-stage approach is adopted: volatility estimation...

This paper applies graphical modelling to the S&P 500, Nikkei 225 and FTSE 100 stock market indices to trace the spillover of returns and volatility between these three major world stock market indices before, during and after the 2008 financial crisis. We find that the depth of market...

In this paper we consider the forecasting performance of a range of semi- and non- parametric methods applied to high frequency electricity price data. Electricity price time-series data tend to be highly seasonal, mean reverting with price jumps/spikes and time- and price-dependent volatility....

In this paper graphical modelling is used to select a sparse structure for a multivariate time series model of New Zealand interest rates. In particular, we consider a recursive structural vector autoregressions that can subsequently be described parsimoniously by a directed acyclic graph, which...

It is now recognised that long memory and structural change can be confused because the statistical properties of times series of lengths typical of financial and econometric series are similar for both models. We propose a new set of methods aimed at distinguishing between long memory and...