This paper first establishes a selection of stylized facts for high-frequency cointegration processes in the European equity market. Empirical evidence is given by one minute-binned transaction data of all DAX 30 constituents as traded on Deutsche Börse's Xetra market in 2014. A methodology is...

This paper establishes a selection of stylized facts for high-frequency cointegrated processes, based on one-minute-binned transaction data. A methodology is introduced to simulate cointegrated stock pairs, following none, some or all of these stylized facts. AR(1)-GARCH(1,1) and...

This paper first establishes a selection of stylized facts for high-frequency cointegration processes in the European equity market. Empirical evidence is given by one minute-binned transaction data of all DAX 30 constituents as traded on Deutsche Börse's Xetra market in 2014. A methodology is...

In this paper, precise fault location in electrical traction network systems is discussed in the high-frequency domain. Based on the analysis of the equivalent impedance at the measurement terminal, the relationship between the fault distance and the frequency spectrum extreme points of the...

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been...

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been...

High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been...

ARMA-GARCH and FIGARCH models with non-normal, tempered-stable innovations are applied to intraday financial time-series on high-frequency time scales. The goal is to investigate their risk forecasting performance and to observe random scaling behavior. To this end, Value-at-Risk (VaR) and...

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 backtest study that available data series are not sufficient in order to estimate Value at Risk and Expected...

In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five...