This paper introduces a new Empirical Mode Decomposition (EMD) Python package called AdvEMDpy that is demonstrably more flexible and which generalises in numerous important ways the existing EMD packages available in Python, R, and MATLAB. The extensions introduced by this AdvEMDpy package both...

This work serves as a formal supplement to ‘Package AdvEMDpy: Algorithmic Variations of Empirical Mode Decomposition in Python’ with additional synthetic and real-world examples. AdvEMDpy will be shown to be more accurate than its Python competitors in resolving the underlying driving...

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component of volatility in finance for portfolio allocation,...

This paper considers the dynamics of spot and futures prices in the presence of arbitrage. A partially linear error correction model is proposed where the adjustment coefficient is allowed to depend non-linearly on the lagged price difference. The model is estimated using data on the DAX index...

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is...