The large-scale magnetic field of a thin accretion disk with outflows

The large-scale magnetic field threading an accretion disk plays an important role in launching jets/outflows. The field may probably be advected inwards by the plasma in the accretion disk from the ambient environment (interstellar medium or a companion star). It has been suggested that the external field can be efficiently dragged inwards in a thin disk with magnetic outflows. We construct a self-consistent global disk-outflow model, in which the large-scale field is formed by the advection of the external field in the disk. The outflows are accelerated by this field co-rotating with the disk, which carry away most angular momentum of the disk and make its structure significantly different from the conventional viscous disk structure. We find that the magnetic field strength in the inner region of the disk can be several orders of magnitude higher than the external field strength for a geometrically thin disk with $H/R \sim 0.1$, if the ratio of the gas to magnetic pressure $\beta_{\rm out} \sim 10^2$ at the outer edge of the disk. The outflow velocity shows layer-like structure, i.e., it decreases with radius where it is launched. The outflow can be accelerated up to $\sim 0.2-0.3$c from the inner region of the disk, and the mass loss rate in the outflows is $\sim 10 - 70\%$ of the mass accretion rate at the outer radius of the disk, which may account for the fast outflows observed in some active galactic nuclei (AGNs).Comment: Accepted for publication in Ap

Modelling exchange rate volatility with random level shifts

Recent literature has shown that the volatility of exchange rate returns displays long memory features. It has also been shown that if a short memory process is contaminated by level shifts, the estimate of the long memory parameter tends to be upward biased. In this article, we directly estimate a random level shift model to the logarithm of the absolute returns of five exchange rates series, in order to assess whether random level shifts (RLSs) can explain this long memory property. Our results show that there are few level shifts for the five series, but once they are taken into account the long memory property of the series disappears. We also provide out-of-sample forecasting comparisons, which show that, in most cases, the RLS model outperforms popular models in forecasting volatility. We further support our results using a variety of robustness checks