Continuation and stability of rotating waves in the magnetized spherical Couette system: Secondary transitions and multistability

Rotating waves (RW) bifurcating from the axisymmetric basic magnetized spherical Couette (MSC) flow are computed by means of Newton-Krylov continuation techniques for periodic orbits. In addition, their stability is analysed in the framework of Floquet theory. The inner sphere rotates whilst the outer is kept at rest and the fluid is subjected to an axial magnetic field. For a moderate Reynolds number ${\rm Re}=10^3$ (measuring inner rotation) the effect of increasing the magnetic field strength (measured by the Hartmann number ${\rm Ha}$) is addressed in the range ${\rm Ha}\in(0,80)$ corresponding to the working conditions of the HEDGEHOG experiment at Helmholtz-Zentrum Dresden-Rossendorf. The study reveals several regions of multistability of waves with azimuthal wave number $m=2,3,4$, and several transitions to quasiperiodic flows, i.e modulated rotating waves (MRW). These nonlinear flows can be classified as the three different instabilities of the radial jet, the return flow and the shear-layer, as found in previous studies. These two flows are continuously linked, and part of the same branch, as the magnetic forcing is increased. Midway between the two instabilities, at a certain critical ${\rm Ha}$, the nonaxisymmetric component of the flow is maximum.Comment: Published in the Proceedings of the Royal Society A journal. Contains 3 tables and 12 figure

Market Depth in Lean Hog and Live Cattle Futures Markets

Liquidity costs in futures markets are not observed directly because bids and offers occur in an open outcry pit and are not recorded. Traditional estimation of these costs has focused on bidask spreads using transaction prices. However, the bid-ask spread only captures the tightness of the market price. As the volume increases measures of market depth which identify how the order flow moves prices become important information. We estimate market depth for lean hogs and live cattle markets using a Bayesian MCMC method to estimate unobserved data. While the markets are highly liquid, our results show that cost- and risk-reducing strategies may exist. Liquidity costs are highest when larger volumes are traded at distant contracts. For hogs the market becomes less liquid prior to the expiration month. For cattle this occurs during the expiration month when the liquidity risk is also higher. For both markets this coincides with periods of low volume. For the nearby contract highest trading volume occurs at the beginning of the month prior to expiration and lowest trading volume occurs in the expiration month. For both commodities the cumulative effect of volume on price change may lead to liquidity costs higher than a tick.Bayesian MCMC, lean hog futures, liquidity cost, live cattle futures, market depth, market microstructure, Agricultural Finance,

Bid-Ask Spreads, Volume, and Volatility: Evidence from Livestock Markets

Understanding the determinants of liquidity costs in agricultural futures markets is hampered by a need to use proxies for the bid-ask spread which are often biased, and by a failure to account for a jointly determined micro-market structure. We estimate liquidity costs and its determinants for the live cattle and hog futures markets using alternative liquidity cost estimators, intraday prices and micro-market information. Volume and volatility are simultaneously determined and significantly related to the bid-ask spread. Daily volume is negatively related to the spread while volatility and volume per transaction display positive relationships. Electronic trading has a significant competitive effect on liquidity costs, particularly in the live cattle market. Results are sensitive to the bid-ask spread measure, with a modified Bayesian method providing estimates most consistent with expectations and the competitive structure found in these markets.Bayesian estimation, bid-ask spread determinants, liquidity cost, Livestock Production/Industries, Marketing,

Estimating Liquidity Costs in Agricultural Futures Markets using Bayesian Methods

Estimation of liquidity costs in futures markets is challenging because bid-ask spreads are usually not observed. Several estimators of liquidity costs exist that use transaction data, but there is little agreement on their relative accuracy and usefulness, and their performance has been questioned. We use a Bayesian method proposed by Hasbrouck which possesses conceptually desirable properties to estimate liquidity costs of six agricultural future contracts. The method builds on Roll's model and uses Markov Chain Monte Carlo estimation. Our Bayesian estimates are lower than more traditional estimates and as anticipated decrease even more when more realistic assumptions such as discreteness are incorporated. The findings demonstrate the need for further research to clarify the usefulness and accuracy of the procedure.Marketing,

Online Privacy as a Collective Phenomenon

The problem of online privacy is often reduced to individual decisions to hide or reveal personal information in online social networks (OSNs). However, with the increasing use of OSNs, it becomes more important to understand the role of the social network in disclosing personal information that a user has not revealed voluntarily: How much of our private information do our friends disclose about us, and how much of our privacy is lost simply because of online social interaction? Without strong technical effort, an OSN may be able to exploit the assortativity of human private features, this way constructing shadow profiles with information that users chose not to share. Furthermore, because many users share their phone and email contact lists, this allows an OSN to create full shadow profiles for people who do not even have an account for this OSN. We empirically test the feasibility of constructing shadow profiles of sexual orientation for users and non-users, using data from more than 3 Million accounts of a single OSN. We quantify a lower bound for the predictive power derived from the social network of a user, to demonstrate how the predictability of sexual orientation increases with the size of this network and the tendency to share personal information. This allows us to define a privacy leak factor that links individual privacy loss with the decision of other individuals to disclose information. Our statistical analysis reveals that some individuals are at a higher risk of privacy loss, as prediction accuracy increases for users with a larger and more homogeneous first- and second-order neighborhood of their social network. While we do not provide evidence that shadow profiles exist at all, our results show that disclosing of private information is not restricted to an individual choice, but becomes a collective decision that has implications for policy and privacy regulation