χc0 ω\chi_{c0} \, \omega production in e+e−e^+e^- annihilation through ψ(4160)\psi(4160)

We argue that the recent BESIII data on the cross section for the process $e^+e^- \to \chi_{c0} \, \omega$ in the center of mass energy range 4.21 - 4.42 GeV can be described by the contribution of the known charmonium-like resonance $\psi(4160)$ with the mass of about 4190\,MeV. The value of the coupling in the transition $\psi(4160) \to \chi_{c0} \, \omega$ needed for this mechanism is comparable to that in another known similar transition $\chi_{c0}(2P) \to J/\psi \, \omega$. The suggested mechanism also naturally explains the reported relative small value of the cross section for the final states $\chi_{c1} \, \omega$ and $\chi_{c2} \, \omega$ above their respective thresholds.Comment: 6 page

Exact entanglement cost of quantum states and channels under PPT-preserving operations

This paper establishes single-letter formulas for the exact entanglement cost of generating bipartite quantum states and simulating quantum channels under free quantum operations that completely preserve positivity of the partial transpose (PPT). First, we establish that the exact entanglement cost of any bipartite quantum state under PPT-preserving operations is given by a single-letter formula, here called the $\kappa$-entanglement of a quantum state. This formula is calculable by a semidefinite program, thus allowing for an efficiently computable solution for general quantum states. Notably, this is the first time that an entanglement measure for general bipartite states has been proven not only to possess a direct operational meaning but also to be efficiently computable, thus solving a question that has remained open since the inception of entanglement theory over two decades ago. Next, we introduce and solve the exact entanglement cost for simulating quantum channels in both the parallel and sequential settings, along with the assistance of free PPT-preserving operations. The entanglement cost in both cases is given by the same single-letter formula and is equal to the largest $\kappa$-entanglement that can be shared by the sender and receiver of the channel. It is also efficiently computable by a semidefinite program.Comment: 54 pages, 8 figures; comments are welcome

Modelling Realized Covariances and Returns

This paper proposes new dynamic component models of realized covariance (RCOV) matrices based on recent work in time-varying Wishart distributions. The specifications are linked to returns for a joint multivariate model of returns and covariance dynamics that is both easy to estimate and forecast. Realized covariance matrices are constructed for 5 stocks using high-frequency intraday prices based on positive semi-definite realized kernel estimates. The models are compared based on a term-structure of density forecasts of returns for multiple forecast horizons. Relative to multivariate GARCH models that use only daily returns, the joint RCOV and return models provide significant improvements in density forecasts from forecast horizons of 1 day to 3 months ahead. Global minimum variance portfolio selection is improved for forecast horizons up to 3 weeks out.eigenvalues, dynamic conditional correlation, predictive likelihoods, MCMC

Modelling Realized Covariances

This paper proposes a new dynamic model of realized covariance (RCOV) matrices based on recent work in time-varying Wishart distributions. The specifications can be linked to returns for a joint multivariate model of returns and covariance dynamics that is both easy to estimate and forecast. Realized covariance matrices are constructed for 5 stocks using high-frequency intraday prices based on positive semi-definite realized kernel estimates. We extend the model to capture the strong persistence properties in RCOV. Out-of-sample performance based on statistical and economic metrics show the importance of this. We discuss which features of the model are necessary to provide improvements over a traditional multivariate GARCH model that only uses daily returns.eigenvalues, dynamic conditional correlation, predictive likelihoods, MCMC