On 11 March 2015, SUERF jointly organised a conference with the Oesterreichische Nationalbank and the Austrian Society for Bank Research (Bankwissenschaftliche Gesellschaft - BWG). The present SUERF Study 2015/2 includes a selection of papers based on the authors' contributions to the Vienna...

Abstract It is standard in quantitative risk management to model a random vector ${\mathbf {X}:=\lbrace X_{t_k}\rbrace _{k=1,\ldots ,d}}$ of consecutive log-returns to ultimately analyze the probability law of the accumulated return ${X_{t_1}+\cdots +X_{t_d}}$ . By the Markov regression...

Under the new capital accord stress tests are to be included in market risk regulatory capital calculations. This development necessitates a coherent and objective framework for stress testing portfolios exposed to market risk. Following recent criticism of stress testing methods our tests are...

This paper deals with analysing and forecasting intradaily volatility in electricity spot prices. We analyse the hourly spot prices from the Argentine Electricity Market by grouping prices in three daily series (block bids). We estimate the VAR model for the conditional mean structure and...

In this paper, we examine the forecasting performance of the Value-at-Risk (VaR) models in the MENA equity markets. We use the Asymmetric Power ARCH model to analyze four MENA emerging markets, namely Egypt, Jordan, Morocco, and Turkey. While most empirical studies focus only on holding a long...

This study uses GARCH modelling to estimate and forecast conditional variances and covariances of returns calculated from a set of financial market series: twelve markka exchange rates, twelve corresponding shortterm euro interest rates and the Finnish short-term interest rate, the Finnish...

The study derives a theoretically and empirically founded procedure for volatility estimation and forecasting of daily financial return series for use in value-at-risk model frameworks.GARCH modelling is applied to account for time varying heteroskedastic conditional variances and...