Model risk as part of the operational risk is a serious problem for financial institutions.As the pricing of derivatives as well as the computation of the marketor credit risk of an institution depend on statistical models the application of awrong model can lead to a serious over- or...

We present a thorough empirical study (based on over 8 years of daily data) of candidate models for forecasting losses in relation to positions held against individual risk factors as well as losses in relation to a portfolio of risk factors. As part of the study, we also define various measures...

We present an analysis of VaR forecasts and P&L-series of all 13 German banks that used internal models for regulatory purposes in the year 2001. To this end, we introduce the notion of well-behaved forecast systems. Furthermore, we provide a series of statistical tools to perform our analyses....

We provide a method for distinguishing long-range dependence from deterministic trends such as structural breaks. The method is based on the comparison of standard log-periodogram regression estimation of the memory parameter with its tapered counterpart. The difference of these estimators...

While it is widely agreed that Purchasing Power Parity (PPP) holds as a long-run concept the specific dynamic driving the process is largely build upon a priori economic belief rather than a thorough statistical modeling procedure. The two prevailing time series models, i.e. the exponential...

In this paper, we propose Phillips-Perron type, semiparametric testing procedures to distinguish a unit root process from a mean-reverting exponential smooth transition autoregressive one. The limiting nonstandard distributions are derived under very general conditions and simulation evidence...