Duration and Order Type Clusters

This paper introduces a new bivariate autoregressive conditional framework (ACD×ACL) for modelling the arrival process of buy and sell orders in a limit order book. The model contains two dynamic components to describe the observed clustering of durations and order types: a duration process to capture the time structure, combined with a new "Autoregressive Conditional Logit" model in order to display the traders' order choice. Both processes are adapted to a common natural filtration and modelled simultaneously. It can be shown that the state of the order book as well as the success and the speed of the matching process have a significant influence on the traders' decisions when and on which side of the market to submit orders and, thus, affect the market's liquidityUltra high frequency transaction data, limit order book, market microstructure, ACD model, dynamic logit model, bivariate point process.

Duration and Order Type Clusters

This paper introduces a new bivariate autoregressive conditional framework (ACD×ACL) for modelling the arrival process of buy and sell orders in a limit order book. The model contains two dynamic components to describe the observed clustering of durations and order types: a duration process to capture the time structure, combined with a new "Autoregressive Conditional Logit" model in order to display the traders' order choice. Both processes are adapted to a common natural filtration and modelled simultaneously. It can be shown that the state of the order book as well as the success and the speed of the matching process have a significant influence on the traders' decisions when and on which side of the market to submit orders and, thus, affect the market's liquidityUltra high frequency, transaction data, limit order book, order aggressiveness, market microstructure, ACD model, dynamic logit model, bivariate point process, survival analysis.

Overreaction and Multiple Tail Dependence at the High-frequency Level — The Copula Rose

This paper applies a non- and a semiparametric copula-based approach to analyze the first-order autocorrelation of returns in high frequency financial time series. Using the EUREX D3047 tick data from the German stock index, it can be shown that the temporal dependence structure of price movements is not always negatively correlated as assumed in the stylized facts in the finance literature. Depending on the sampling frequency, the estimated copulas exhibit some kind of overreaction phenomena and multiple tail dependence, revealing patterns similar to the compass rose.High Frequency Data, Non- and Semiparametric Copulas, Overreaction, Tail Dependence, Compass Rose

Resource Allocation for Secure Communication in Systems with Wireless Information and Power Transfer

This paper considers secure communication in a multiuser multiple-input single-output (MISO) downlink system with simultaneous wireless information and power transfer. We study the design of resource allocation algorithms minimizing the total transmit power for the case when the receivers are able to harvest energy from the radio frequency. In particular, the algorithm design is formulated as a non-convex optimization problem which takes into account artificial noise generation to combat potential eavesdroppers, a minimum required signal-to-interference-plus-noise ratio (SINR) at the desired receiver, maximum tolerable SINRs at the potential eavesdroppers, and a minimum required power delivered to the receivers. We adopt a semidefinite programming (SDP) relaxation approach to obtain an upper bound solution for the considered problem. The tightness of the upper bound is revealed by examining a sufficient condition for the global optimal solution. Inspired by the sufficient condition, we propose two suboptimal resource allocation schemes enhancing secure communication and facilitating efficient energy harvesting. Simulation results demonstrate a close-to-optimal performance achieved by the proposed suboptimal schemes and significant transmit power savings by optimization of the artificial noise generation.Comment: 7 pages, 5 figures, and 1 table. Submitted for possible conference publicatio

Max-min Fair Wireless Energy Transfer for Secure Multiuser Communication Systems

This paper considers max-min fairness for wireless energy transfer in a downlink multiuser communication system. Our resource allocation design maximizes the minimum harvested energy among multiple multiple-antenna energy harvesting receivers (potential eavesdroppers) while providing quality of service (QoS) for secure communication to multiple single-antenna information receivers. In particular, the algorithm design is formulated as a non-convex optimization problem which takes into account a minimum required signal-to-interference-plus-noise ratio (SINR) constraint at the information receivers and a constraint on the maximum tolerable channel capacity achieved by the energy harvesting receivers for a given transmit power budget. The proposed problem formulation exploits the dual use of artificial noise generation for facilitating efficient wireless energy transfer and secure communication. A semidefinite programming (SDP) relaxation approach is exploited to obtain a global optimal solution of the considered problem. Simulation results demonstrate the significant performance gain in harvested energy that is achieved by the proposed optimal scheme compared to two simple baseline schemes.Comment: 5 pages, invited paper, IEEE Information Theory Workshop 2014, Hobart, Tasmania, Australia, Nov. 201

Exact Maximum Likelihood Estimation for Copula Models

In recent years, copulas have become very popular in financial research and actuarial science as they are more flexible in modelling the co-movements and relationships of risk factors as compared to the conventional linear correlation coefficient by Pearson. However, a precise estimation of the copula parameters is vital in order to correctly capture the (possibly nonlinear) dependence structure and joint tail events. In this study, we employ two optimization heuristics, namely Differential Evolution and Threshold Ac- cepting to tackle the parameter estimation of multivariate t distribution models in the EML approach. Since the evolutionary optimizer does not rely on gradient search, the EML approach can be applied to estimation of more complicated copula models such as high-dimensional copulas. Our experimental study shows that the proposed method provides more robust and more accurate estimates as compared to the IFM approach.Copula Models, Parameter Inference, Exactly Maximum Likelihood, Differential Evolution, Threshold Accepting