Weekly, quarterly and yearly risk measures are crucial for risk reporting according to Basel III and Solvency II. For the respective data frequencies, the authors show in a simulation and back-test study that available data series are not sufficient in order to estimate Value at Risk and...

This paper considers estimation of semi-nonparametric GARCH filtered copula models in which the individual time series are modelled by semi-nonparametric GARCH and the joint distributions of the multivariate standardized innovations are characterized by parametric copulas with nonparametric...

This paper introduces a new specification for the heterogeneous autoregressive (HAR) model for the realized volatility of S&P500 index returns. In this new model, the coefficients of the HAR are allowed to be time-varying with unknown functional forms. We propose a local linear method for...

Estimating a density function over a bounded domain can be very complicated and resulting in an unsatisfactory or unrealistic density estimate. In many cases a one-to-one transformation can be applied to the considered data set, but there are also situations where such a unique transformation...

Integer-valued autoregressive (INAR) processes have been introduced to model nonnegative integer-valued phenomena that evolve over time. The distribution of an INAR(p) process is essentially described by two parameters: a vector of autoregression coefficients and a probability distribution on...

Applied researchers often test for the difference of the Sharpe ratios of two investment strategies. A very popular tool to this end is the test of Jobson and Korkie (1981), which has been corrected by Memmel (2003). Unfortunately, this test is not valid when returns have tails heavier than the...

We propose a flexible GARCH-type model for the prediction of volatility in financial time series. The approach relies on the idea of using multivariate B-splines of lagged observations and volatilities. Estimation of such a B-spline basis expansion is constructed within the likelihood framework...

We propose a new asymptotic approximation for the sampling behavior of nonparametric estimates of the spectral density of a covariance stationary time series. According to the standard approach, the truncation lag grows slower than the sample size. We derive first order limiting distributions...