Lending is associated with credit risk. Modelling the loss stochastically,the cost of credit risk is the expected loss. In credit business the probabilitythat the debtor will default in payments within one year, often is the onlyreliable quantitative parameter. Modelling the time to default as...

We show that the power of the KPSS-test against inte-gration, as measured by divergence rates of the test statistic underthe alternative, remains the same when residuals from an OLS-regression rather than true observations are used.[...]

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 market or credit risk of an institution depend on statistical models the application of a wrong model can lead to a serious over- or...

This paper discusses the existence of spurious long memory in common nonlinear time series models, namely Markov switching and threshold models. We describe the asymptotic behavior of the process in terms of autocovariance and autocorrelation function and support the theoretical evidences by...

We consider the problem of forecasting time series with long memory when the memory parameter is subject to a structural break. By means of a large-scale Monte Carlo study we show that ignoring such a change in persistence leads to substantially reduced forecasting precision. The strength of...

Lending is associated with credit risk. Modelling the loss stochastically, the cost of credit risk is the expected loss. In credit business the probability that the debtor will default in payments within one year, often is the only reliable quantitative parameter. Modelling the time to default...